Tokushima University / Educator and Researcher Directory --- Ito, Shin-ichi

Personal Web Page

https://researchmap.jp/read0147442 (researchmap)

Field of Study

○	Bio-Signal Processing
○	Bio-Signal Processing
○	Human Science
○	Human support

Subject of Study

○	Psychophysiological Engineering and Its Application (electroencephalogram, Individual Characteristics, Information Visualization, experimental psychology) (生理心理特性に基づく人間分類，生体信号で観測される個人特性・個人差に関する研究)
○	Electroencephalogram Analysis and Its Application (electroencephalogram) (外部刺激に対する人間の反応に関する研究(音楽聴取時の脳波分析)
○	Visualization of Optimization Techniques and Its Application (Information Visualization) (最適化探索課程および結果の可視化(遺伝的アルゴリズムの可視化))
○	Communication support, Human life support, Brain-computer interface, Decision-making detection, Afflatus and/or a sense of discomfort detection, Presage detection, Stress evaluation

Book / Paper

Book:

1.	Yoshiyasu Ishimaru, Shin-ichi Ito, Takeshi Uno, Yasuhiro Okamura, Kenji Ohira, Kosuke Ono, Hidekazu Kakei, Kazuhide Kanenishi, Maiko Sawada, Toru Hasunuma, Ray S. Furuya, Kenji Matsuura, Kazuyuki Matsumoto, 土岐順子, 阿部香織, 山﨑紅, 辰巳丈夫 and 勝村幸博 : 情報科学入門, 日経BP社, Tokyo, Apr. 2019.
2.	Yoshiyasu Ishimaru, Shin-ichi Ito, Takeshi Uno, Yasuhiro Okamura, Kenji Ohira, Kosuke Ono, Hidekazu Kakei, Kazuhide Kanenishi, Maiko Sawada, Toru Hasunuma, Ray S. Furuya, Kenji Matsuura, Kazuyuki Matsumoto, 土岐順子, 阿部香織, 山﨑紅, 辰巳丈夫 and 勝村幸博 : 情報科学入門, 日経BP社, Tokyo, Apr. 2018.
3.	Yoshiyasu Ishimaru, Shin-ichi Ito, Takeshi Uno, Yasuhiro Okamura, Kenji Ohira, Kosuke Ono, Hidekazu Kakei, Kazuhide Kanenishi, Maiko Sawada, Toru Hasunuma, Ray S. Furuya, Kenji Matsuura, Kazuyuki Matsumoto, 土岐順子, 阿部香織, 山﨑紅, 辰巳丈夫 and 勝村幸博 : 情報科学入門, 日経BP社, Tokyo, Apr. 2017.

Academic Paper (Judged Full Paper):

1.	Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Electroencephalogram Analysis Method to Detect Unspoken Answers to Questions Using Multistage Neural Networks, IEEE Access, Vol.11, 137151-137162, 2023. (Summary) Braincomputer interfaces (BCI) facilitate communication between the human brain and computational systems, additionally offering mechanisms for environmental control to enhance human life. The current study focused on the application of BCI for communication support, especially in detecting unspoken answers to questions. Utilizing a multistage neural network (MSNN) replete with convolutional and pooling layers, the proposed method comprises a threefold approach: electroencephalogram (EEG) measurements, EEG feature extraction, and answer classification. The EEG signals of the participants are captured as they mentally respond with yes or no to the posed questions. Feature extraction was achieved through an MSNN composed of three distinct convolutional neural network models. The first model discriminates between the EEG signals with and without discernible noise artifacts, whereas the subsequent two models are designated for feature extraction from EEG signals with or without such noise artifacts. Furthermore, a support vector machine is employed to classify the answers to the questions. The proposed method was validated via experiments using authentic EEG data. The mean and standard deviation values for sensitivity and precision of the proposed method were 99.6% and 0.2%, respectively. These findings demonstrate the viability of attaining high accuracy in a BCI by preliminarily segregating the EEG signals based on the presence or absence of artifact noise and underscore the stability of such classification. Thus, the proposed method manifests prospective advantages of separating EEG signals characterized by noise artifacts for enhanced BCI performance. (Keyword) Answer to question / convolutional neural networks / electroencephalogram / multistage neural networks, / personal model / support vector machine (Tokushima University Institutional Repository) ● Metadata: 118851 (Link to Publication Site) ● Publication site (DOI): 10.1109/ACCESS.2023.3339665 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1109/ACCESS.2023.3339665 (Tokushima University Institutional Repository: 118851, DOI: 10.1109/ACCESS.2023.3339665)
2.	Mikito Ogino, Suguru Kanoga, Shin-ichi Ito and Yasue Mitsukura : Semi-Supervised Learning for Auditory Event-Related Potential-Based Brain-Computer Interface, IEEE Access, Vol.9, 47008-47023, 2021. (Summary) A brain-computer interface (BCI) is a communication tool that analyzes neural activity and relays the translated commands to carry out actions. In recent years, semi-supervised learning (SSL) has attracted attention for visual event-related potential (ERP)-based BCIs and motor-imagery BCIs as an effective technique that can adapt to the variations in patterns among subjects and trials. The applications of the SSL techniques are expected to improve the performance of auditory ERP-based BCIs as well. However, there is no conclusive evidence supporting the positive effect of SSL techniques on auditory ERP-based BCIs. If the positive effect could be verified, it will be helpful for the BCI community. In this study, we assessed the effects of SSL techniques on two public auditory BCI datasets - AMUSE and PASS2Dusing the following machine learning algorithms: step-wise linear discriminant analysis, shrinkage linear discriminant analysis, spatial temporal discriminant analysis, and least-squares support vector machine. These backbone classifiers were firstly trained by labeled data and incrementally updated by unlabeled data in every trial of testing data based on SSL approach. Although a few data of the datasets were negatively affected, most data were apparently improved by SSL in all cases. The overall accuracy was logarithmically increased with every additional unlabeled data. This study supports the positive effect of SSL techniques and encourages future researchers to apply them to auditory ERP-based BCIs. (Keyword) Auditory stimuli / brain-computer interface / event-related potential / semi-supervised learning / P300 (Tokushima University Institutional Repository) ● Metadata: 116331 (Link to Publication Site) ● Publication site (DOI): 10.1109/ACCESS.2021.3067337 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85103244526 (Tokushima University Institutional Repository: 116331, DOI: 10.1109/ACCESS.2021.3067337, Elsevier: Scopus)
3.	Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Human-Wants Detection Based on Electroencephalogram Analysis During Exposure to Music, Journal of Robotics and Mechatronics, Vol.32, No.4, 724-730, 2020. (Summary) We propose a method to detect human wants by using an electroencephalogram (EEG) test and specifying brain activity sensing positions. EEG signals can be analyzed by using various techniques. Recently, convolutional neural networks (CNNs) have been employed to analyze EEG signals, and these analyses have produced excellent results. Therefore, this paper employs CNN to extract EEG features. Also, support vector machines (SVMs) have shown good results for EEG pattern classification. This paper employs SVMs to classify the human cognition into wants, not wants, and other feelings. In EEG measurements, the electrical activity of the brain is recorded using electrodes placed on the scalp. The sensing positions are related to the frontal cortex and/or temporal cortex activities although the mechanism to create wants is not clear. To specify the sensing positions and detect human wants, we conducted experiments using real EEG data. We confirmed that the mean and standard deviation values of the detection accuracy rate were 99.4% and 0.58%, respectively, when the target sensing positions were related to the frontal and temporal cortex activities. These results prove that both the frontal and temporal cortex activities are relevant for creating wants in the human brain, and that CNN and SVMare effective for the detection of human wants. (Keyword) wants detection / electroencephalogram / listening to music / convolutional neural network / support vector machine (Tokushima University Institutional Repository) ● Metadata: 116074 (Link to Publication Site) ● Publication site (DOI): 10.20965/jrm.2020.p0724 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.20965/jrm.2020.p0724 (Tokushima University Institutional Repository: 116074, DOI: 10.20965/jrm.2020.p0724)
4.	Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Individual Differences in Brain Activities When Human Wishes to Listen to Music Continuously Using Near-Infrared Spectroscopy, International Journal of Advanced Science and Technology, Vol.29, No.6, 807-813, 2020. (Summary) This paper introduces an individual difference in the activities of the prefrontal cortex when a person wants to listen to music using near-infrared spectroscopy. The individual differences are confirmed by visualizing the variation in oxygenated hemoglobin level. The sensing positions used to record the brain activities are around the prefrontal cortex. The existence of individual differences was verified by experiments. The experiment results show that active positions while feeling a wish to listen to music are different in each subject, and an oxygenated hemoglobin level is different in each subject compared to its value when a subject does not feel the wish to listen to music. The experiment results show that it is possible to detect a wish to listen to the music based on changes in the oxygenated hemoglobin level. Also, these results suggest that active positions are different in each subject because the sensitivities and how to feel on stimulus are different. Lastly, the results suggest that it is possible to express the individual differences as differences in active positions. (Keyword) Individual difference / near infrared spectroscopy / wish to listen to music / prefrontal cortex activity / music therapy (Tokushima University Institutional Repository) ● Metadata: 114822 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85083553004 (Tokushima University Institutional Repository: 114822, Elsevier: Scopus)
5.	Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Japanese sign language classification based on gathered images and neural networks, International Journal of Advances in Intelligent Informatics, Vol.5, No.3, 243-255, 2019. (Summary) This paper proposes a method to classify words in Japanese Sign Language (JSL). This approach employs a combined gathered image generation technique and a neural network with convolutional and pooling layers (CNNs). The gathered image generation generates images based on mean images. Herein, the maximum difference value is between blocks of mean and JSL motions images. The gathered images comprise blocks that having the calculated maximum difference value. CNNs extract the features of the gathered images, while a support vector machine for multi-class classification, and a multilayer perceptron are employed to classify 20 JSL words. The experimental results had 94.1% for the mean recognition accuracy of the proposed method. These results suggest that the proposed method can obtain information to classify the sample words. (Keyword) Japanese sign language / Gathered image / mean image / convolutional neural network (Tokushima University Institutional Repository) ● Metadata: 114106 (Link to Publication Site) ● Publication site (DOI): 10.26555/ijain.v5i3.406 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85077381259 (Tokushima University Institutional Repository: 114106, DOI: 10.26555/ijain.v5i3.406, Elsevier: Scopus)
6.	Shun Yamamoto, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Verification of the Usefulness of Personal Authentication with Aerial Input Numerals Using Leap Motion, Advances in Science, Technology and Engineering Systems, Vol.4, No.5, 369-374, 2019. (Summary) With the progress of IoT, everything is going to be connected to the network. It will bring us a lot of benefits however some security risks will be occurred by connecting network. To avoid such problems, it is indispensable to strengthen security more than now. We focus on personal authentication as one of the security.As a security enhancement method, we proposed a method to carry out numeral identification and personal authentication using numerals written in the air with Leap motion sensor. In this paper, we also focus on proper handling of aerial input numerals to verify whether the numerals written in the air are helpful for authentication. We collect numerals 0 to 9 from five subjects, then apply three preprocessing to these data, learn and authenticate them by CNN (convolutional neural network) which is a method of machine learning. As a result of learning, an average authentication accuracy was 92.4%. This result suggests that numerals written in the air are possible to carry out personal authentication and it will be able to construct a better authentication system. (Keyword) Leap motion / neural network / machine learning / Personal authentication / Aerial input numeral (Tokushima University Institutional Repository) ● Metadata: 113905 (Link to Publication Site) ● Publication site (DOI): 10.25046/aj040548 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85079279693 (Tokushima University Institutional Repository: 113905, DOI: 10.25046/aj040548, Elsevier: Scopus)
7.	Shin-ichi Ito, Koyuki Orihashi, Momoyo Ito and Minoru Fukumi : A Gathered Images Analysis Method to Evaluate Sound Sleep, Journal of the Institute of Industrial Applications Engineers, Vol.7, No.1, 16-24, 2019. (Summary) This paper proposes a method to evaluate a sound sleep using an image gathering technique and itsanalysis techniques. The proposed method consists of three phases; gathered images generation, gathered images analysis and sound sleep evaluation. The gathered images designed to gather sleep postures and their changes are generated at 1 second, 10 seconds, 1 minute, 10 minutes, 1 hour intervals and all times, respectively. In the gathered image analysis, the gathered images are analyzed by calculating difference values among the gathered images of 10-minute and all times. Then, the sound sleep conditions are evaluated by visual inspection and analysis results. In order to show the effectiveness of the proposed method, we conduct experiments using real movies and their images. In experimental results, we confirm that there were sound sleep conditions, bad sleep conditions and borderline cases by checking subjective evaluation using questionnaire and generated gathered images visually. Moreover, we confirm that the calculated difference values among the gathered images of 10-minute and all times are different between sound sleep and other cases. Furthermore, the analyzed results show that the proposed method was successful in the sleep conditions classifications on four of five subjects. These results suggest that the gathered images analysis method is effective for evaluating whether sleep condition is sound sleep or not. In particular, it is important to calculate the difference values among the gathered images of 10-minute and all times to evaluate sleeping conditions. (Keyword) Sound sleep / Gathered image / Information visualization / Sleep posture (Tokushima University Institutional Repository) ● Metadata: 113016 (Link to Publication Site) ● Publication site (DOI): 10.12792/JIIAE.7.16 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.12792/JIIAE.7.16 (Tokushima University Institutional Repository: 113016, DOI: 10.12792/JIIAE.7.16)
8.	Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : An Electroencephalogram Analysis Method to Detect Preference Patterns Using Gray Association Degrees and Support Vector Machines, Advances in Science, Technology and Engineering Systems, Vol.3, No.5, 105-108, 2018. (Summary) This paper introduces an electroencephalogram (EEG) analysis method to detect preferences for particular sounds. Our study aims to create novel braincomputer interfaces (BMIs) to control human mental (NBMICM), which are used to detect human mental conditions i.e., preferences, thinking, and consciousness, choose stimuli to control these mental conditions, and evaluate these choices. It is important to detect the preferences on stimuli. If the stimuli related to the preference can be detected, the NBCIMC can provide stimuli to the user based on their emotions by detecting their favorite stimuli. The proposed method adopted EEG recording technique, extraction techniques of EEG features and detection methods of preferences. EEG recording employs a simple electroencephalograph, for which the measurement position is the left frontal lobe (Fp1) of the brain. We assume that the differences of the EEG activities on the patterns of preference are expressed in the association between the changes of the power spectra on each frequency band of the EEG. To calculate the association, we employ the gray theory model. The EEG feature is extracted by calculating the gray association degree, then, the preferences are detect using a support vector machine (SVM). Experiments are conducted to test the effectiveness of this method, which is validated by a mean accuracy rate >88% on the favorite sound detection. These results suggest that the detection of subjects favorite sounds becomes easy when the EEG signals are analyzed while the gray associate degrees are used as the EEG feature and the SVM is used as the classifier. (Keyword) electroencephalogram / Preference / Favorite sounds / Simple electroencephalography / Gray association degree / Support vector machine (Tokushima University Institutional Repository) ● Metadata: 112341 (Link to Publication Site) ● Publication site (DOI): 10.25046/aj030514 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85061818378 (Tokushima University Institutional Repository: 112341, DOI: 10.25046/aj030514, Elsevier: Scopus)
9.	Higasa Takashi, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : A Specification Method of Character String Region in Augmented Reality, Journal of the Institute of Industrial Applications Engineers, Vol.6, No.2, 73-79, 2018. (Summary) This paper proposes a method to enter characters and/or character string in an augmented reality usinga gesture motion. The proposed method detects the region of character string using the gesture motion. It consistsof five phases; template generation, skin color detection, hand region detection, gesture motion extraction anddesignation of character string region. The template image consists of two fingers because a gesture is to takehold the tips of the first and second fingers. In the skin color detection, we extract the skin color on the basis ofvalues in saturation by using threshold processing. The hand region is detected by calculating areas and detectingthe area with the maximum value as a hand. The gesture motion is extracted using template matching. In orderto show the effectiveness of the proposed method, we conduct experiments for character string specification. (Keyword) Augmented Reality / HSV Color System / Gesture Motion / Quadrangle for character string region (Tokushima University Institutional Repository) ● Metadata: 112350 (Link to Publication Site) ● Publication site (DOI): 10.12792/jiiae.6.73 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.12792/jiiae.6.73 (Tokushima University Institutional Repository: 112350, DOI: 10.12792/jiiae.6.73)
10.	Daiki Hiraoka, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Japanese Janken Recognition by Support Vector Machine Based on Electromyogram of Wrist, ECTI Transactions on Computer and Information Technology, Vol.11, No.2, 154-162, 2017. (Summary) In this paper, we propose a method which can discriminate hand motions. We measured an electromyogram of wrist by using 8 dry type sensors. We focused on four motions, such as ``Rock-Scissors-Paper'' and ``Neutral''. ``Neutral'' is a state that does not do anything. In the proposed method, we apply fast Fourier transformation (FFT) to measured EMG data, and then remove hum noise. Next, we combine values of sensors based on a gaussian function. In this gaussian function, variance and mean are 0.2 and 0, respectively. After that, we apply normalization by linear transformation to the values. Subsequently, we resize the values into range from -1 to 1. Finally, support vector machine (SVM) conducts learning and discrimination. We conducted experiments in three subjects. Discrimination accuracy of the proposed method for three subjects was 96.9%, 95.3%, 92.2%, respectively. It is clear that the gaussian function is robust to difference of sensor position because this function combines both adjacent channels of sensors. (Tokushima University Institutional Repository) ● Metadata: 113535 (Tokushima University Institutional Repository: 113535)
11.	Shoichiro Fujisawa, Ishibashi Tatsuki, Katsuya SATO, Shin-ichi Ito and Osamu Sueda : Evaluation of Orientation Performance of Attention Patterns for Blind Person, Studies in Health Technology and Informatics, Vol.242, 910-917, 2017. (Summary) Tactile walking surface indicators (TWSIs) are installed on footpath to support independent travel for the blind. There are two types of TWSIs, attention patterns and guiding patterns. The attention pattern is usually installed at the crosswalk entrances. The direction of the crossing can be acquired by the row of the projection of the attention pattern through the soles of the shoes. In addition, truncated domes or cones of the attention pattern were arranged in a square grid, parallel or diagonal at 45 degrees to the principal direction of travel. However, the international standard organization (ISO) allows a wide-ranging size. In this research, the direction indicating performance was compared at the same intervals for the five diameters specified by the international standard. As a result of the experiment, the diagonal array does not indicate the direction of travel, but the projection row does indicate the direction of travel in the parallel array. When the attention pattern is installed at a crosswalk entrance, a parallel array should be installed in the direction of the crossing. (Keyword) Tactile walking surface indicators (TWSIs) / Attention patterns / Blind person / Orientation / Crosswalk / Direction of travel (Link to Publication Site) ● Publication site (DOI): 10.3233/978-1-61499-798-6-910 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 28873905 ● Summary page in Scopus @ Elsevier: 2-s2.0-85028884192 (DOI: 10.3233/978-1-61499-798-6-910, PubMed: 28873905, Elsevier: Scopus)
12.	Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : A Consideration for Electroencephalogram Analysis using Self-Organizing Map Based on Learning Algorithm for Plural-Attribue Information, IEEJ Transactions on Electronics, Information and Systems, Vol.137, No.2, 302-309, 2017. (Summary) This paper discusses a method to detect electroencephalogram (EEG) patterns using a self-organizing map(SOM) based on a learning algorithm for plural-attribute information (SOMPA). The input data for SOMPA has two attributes which are EEG feature and individual feature. We set the EEG feature to main feature and individual feature to sub-attribute information. The winning node in the learning algorithm of SOMPA is determined by using main feature and sub-attribute information. In the preprocessing, we extract the EEG feature vector by calculating the time average on each frequency band which are , and , respectively. The individual feature is analyzed though the ego analysis using psychological testing. In order to prove the effectiveness of the proposed method, we conduct experiments using real EEG data. The experimental results show that the EEG pattern detection accuracy using SOMPA improves compared with the standard SOM. (Keyword) plural-attribute / self-organizing map / electroencephalogram / individual feature / auditory stimuli (Link to Publication Site) ● Publication site (DOI): 10.1541/ieejeiss.137.302 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390001204607067392 ● Summary page in Scopus @ Elsevier: 2-s2.0-85012890680 (DOI: 10.1541/ieejeiss.137.302, CiNii: 1390001204607067392, Elsevier: Scopus)
13.	Norihiro Ikeda, Kazuya Takahashi, Tomoyuki Inagaki, Katsuya SATO, Shin-ichi Ito, Motohiro Seiyama and Shoichiro Fujisawa : Emitting LED block at crosswalk entrance for visually impaired persons, Procedia Manufacturing, Vol.3, 3147-3151, 2015. (Summary) Visually impaired persons require tactile walking surface indicators (TWSIs) for independent walking. However, if these blocks are installed following guidelines for constructing TWSIs, it's unkind to users. Therefore, we propose LED blocks installed at crosswalk entrances for blind and visually impaired persons. These blocks can be detected through the soles of shoes and through use of residual vision. This study conducted an experiment with amblyopic examinees in which we evaluate the visibility of the LED blocks. (Keyword) TWSIs / Visually impaired person / Assistive devices / LED block / Crosswalk (Link to Publication Site) ● Publication site (DOI): 10.1016/j.promfg.2015.07.863 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1016/j.promfg.2015.07.863 (DOI: 10.1016/j.promfg.2015.07.863)
14.	Jiro Yonezaki, Maki Ikeda, Takuya Tsubaki, Katsuya SATO, Shin-ichi Ito, Osamu Sueda and Shoichiro Fujisawa : Development of Augmentative and Alternative Communication Assessment Tools for Patients with ALS, Studies in Health Technology and Informatics, Vol.217, 805-810, 2015. (Summary) Amyotrophic lateral sclerosis (ALS) is a progressive, intractable disease that causes the body to gradually become weaker. A communication device for the handicapped with a high degree of disability (see Fig. 1) is often used for a seriously ill ALS patient. However, the degree of the symptoms differs in each patient, so it is necessary to select a suitable input switch. Moreover, the selection of the switch is greatly influenced by differences in knowledge and experience, so support is often advanced preceded by a subjective evaluation [1]-[6]. This research developed an evaluation apparatus capable of measuring the movement of the finger from the displacement of the lever using a sensor built into the input switch. As a result, appropriate support is expected to evaluate patient's remaining function based on objective data when the input switch is selected. (Keyword) Augmentative and Alternative Communication (AAC) / Amyotrophic lateral sclerosis (ALS) (Link to Publication Site) ● Publication site (DOI): 10.3233/978-1-61499-566-1-805 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 26294567 ● Summary page in Scopus @ Elsevier: 2-s2.0-84951912232 (DOI: 10.3233/978-1-61499-566-1-805, PubMed: 26294567, Elsevier: Scopus)
15.	Shoichiro Fujisawa, Kyohei Hirono, Shin-ichi Ito, Katsuya SATO and Osamu Sueda : Walking Characteristics of Persons with Visually Impairment Crossing Intersections with Audible Pedestrian Signals, Studies in Health Technology and Informatics, Vol.217, 633-638, 2015. (Summary) The authors have been continuously researching tactile walking surface indicators and audible pedestrian signals used for crossing intersections. A pedestrian walks to the opposite side of the crosswalk using audible pedestrian signals. The position where the audible pedestrian signal is set up clearly influences the pedestrian's path. We performed an intersection crossing experiment with road noise for different positions where the audible pedestrian signal was set up. Vehicle road noise is thought to influence walking pedestrians. However, we could not clearly separate the difference of where the audible pedestrian signal was set up from the influence of the road noise. We therefore conducted the experiment with no road noise in the intersection to determine the difference of the position where the audible pedestrian signal was set up. The research results were compared with and without road noise to specify the road noise influence. (Keyword) Tactile walking surface indicators / Audible pedestrian signals / person with visually impairment / veering distance (Link to Publication Site) ● Publication site (DOI): 10.3233/978-1-61499-566-1-633 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 26294540 ● Summary page in Scopus @ Elsevier: 2-s2.0-84951940140 (DOI: 10.3233/978-1-61499-566-1-633, PubMed: 26294540, Elsevier: Scopus)
16.	Norihiro Ikeda, Kazuya Takahashi, Tomoyuki Inagaki, Katsuya SATO, Shin-ichi Ito, Motohiro Seiyama, Kiyohito Takeuchi, Hiroshi Ogino and Shoichiro Fujisawa : Visibility of LED Blocks Mounted on Crosswalk Boundaries for low Visual Capacity, Studies in Health Technology and Informatics, Vol.217, 512-517, 2015. (Summary) According to the WHO, about 80% of visually impaired persons in the world are with low visual capacity. It is considered that LVs' safe independent mobility will be improved as employing Tactile Walking Surface Indicators (TWSIs). In the night, guidelines for constructing warning blocks, a kind of tactile surface indicator, have not been well established. Therefore, crossing crosswalks is one of the most dangerous situations for visually impaired persons. We thus developed novel LED blocks that indicate crosswalk boundaries between sidewalks and roadways. This study addresses the LED blocks under various environmental illuminations and illumination from the opposite side of the crossing. (Keyword) LED block / Crosswalk / Visual distance / Turbulence illumination / Tactile Walking Surface Indicators (TWSIs) (Link to Publication Site) ● Publication site (DOI): 10.3233/978-1-61499-566-1-512 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 26294521 ● Summary page in Scopus @ Elsevier: 2-s2.0-84952054448 (DOI: 10.3233/978-1-61499-566-1-512, PubMed: 26294521, Elsevier: Scopus)
17.	Katsuya SATO, Yuki OGAWA, Shin-ichi Ito, Shoichiro Fujisawa and Kazuyuki MINAMI : Strain magnitude dependent intracellular calcium signaling response to uniaxial stretch in osteoblastic cells, Journal of Biomechanical Science and Engineering, Vol.10, No.3, 1-11, 2015. (Summary) In osteoblast cells, cells change their intracellular Ca2+ concentration ([Ca2+]i) as the response to mechanical stimuli. Although it has been reported that osteoblast cells responded to many kinds of mechanical stimuli including stretch of substrate, shear stress in fluid flow, direct indentation of glass microneedle and hydrostatic pressure etc., the detail of the characteristics of intracellular calcium signaling response to substrate stretch still remains unclear because motion artifact during stretch application causes out of focus plane and observation area, and complicates the in situ time lapse observation of change in [Ca2+]i in a single cell level. In this study, we combined our originally developed cell stretching MEMS device with the ratiometric microscopy method with two kinds of visible wavelength calcium indicator dyes. The cell stretching micro device and the ratiometric method reduce the influence of motion artifact during stretch application, and enable us to quantitatively evaluate the characteristics of cellular calcium signaling response to stretch. MC3T3-E1 osteoblastic cells were plated onto the cell stretching micro device and fluorescently labeled by Ca2+ indicator Fluo 3 and Fura Red. A uniaxial stretch with three magnitudes of strain 5%, 10% and 15% with constant strain rate were applied to the cells, and in situ time lapse observation of cellular calcium signaling response to stretch was conducted with high temporal and spatial resolution. We succeeded in obtaining time lapse fluorescent image sequences during stretch application without excessive out of focus and blank time. The results revealed that MC3T3-E1 cells change the intensity of calcium signaling response to stretch according to the stretch strain magnitude. As stretch strain magnitude was increased, the amount of change in fluorescent ratio value of Ca2+ indicators in stretched cells also increased. This result suggests the possibility that osteoblastic cells can sense the magnitude of mechanical stimuli at upstream of mechanotransduction pathway such as influx of extracellular Ca2+. (Keyword) Cell Biomechanics / Mechanotransduction / osteoblast / intracellular calcium / Stretch stimuli (Link to Publication Site) ● Publication site (DOI): 10.1299/jbse.15-00242 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390282680238072960 ● Search Scopus @ Elsevier (DOI): 10.1299/jbse.15-00242 (DOI: 10.1299/jbse.15-00242, CiNii: 1390282680238072960)
18.	Shin-ichi Ito, Momoyo Ito, Shoichiro Fujisawa and Minoru Fukumi : Method to Classify Matching Patterns between Music and Humans Mood Using EEG Analysis Technique Considering Personality, The Online Journal on Computer Science and Information Technology, OJCSIT, Vol.5, No.3, 341-345, 2015. (Summary) In this paper we introduce a method to classify matching patterns between music and human mood using an electroencephalogram (EEG) analysis technique and considering personality. We analyse the EEG of the left prefrontal cortex by single-point sensing. The EEG recording device uses dry-type sensors. The feature vector is created by connecting the personality quantification results and the EEG features. Egogramsthe Yatabe-Guilford personality inventory and a Kretschmer-type personality inventory are used to quantify personality. The EEG features are extracted using fast Fourier transform. Then, the matching patterns are classified using the k-nearest neighbour method. To show the effectiveness of the proposed method, we conduct experiments using real EEG data. (Keyword) electroencephalogram / pattern recognition / personality / Yatabe-Guilford personality inventory / Kretschmer type personality inventory / k -nearest neighbour method / egogram (Tokushima University Institutional Repository) ● Metadata: 112404 (Tokushima University Institutional Repository: 112404)
19.	Peng Zhang, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Development of Eye Mouse Using EOG signals and Learning Vector Quantization Method, Journal of the Institute of Industrial Applications Engineers, Vol.3, No.2, 52-58, 2015. (Summary) Recognition of eye motions has attracted more and more attention of researchers all over the world in recent years. Compared with other body movements, eye motion is responsive and needs a low consumption of physical strength. In particular, for patients with severe physical disabilities, eye motion is the last spontaneous motion for them to make a respond. In order to provide an efficient means of communication for patients such as ALS (amyotrophic lateral sclerosis) who cannot move even their muscles except eye, in this paper we proposed a system that uses EOG signals and Learning Vector Quantization algorithm to recognize eye motions. According to recognition results, we use API (application programming interface) to control cursor movements. This system would be used as a means of communication to help ALS patients. (Tokushima University Institutional Repository) ● Metadata: 113536 (Link to Publication Site) ● Publication site (DOI): 10.12792/JIIAE.3.52 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1050001338414876928 ● Search Scopus @ Elsevier (DOI): 10.12792/JIIAE.3.52 (Tokushima University Institutional Repository: 113536, DOI: 10.12792/JIIAE.3.52, CiNii: 1050001338414876928)
20.	Takako Ikuno, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Lost Property Detection by Template Matching using Genetic Algorithm and Random Search, Journal of the Institute of Industrial Applications Engineers, Vol.3, No.2, 59-64, 2015. (Summary) In this paper, we propose an object search method which is adapted to transformation of an object to be searched to detect lost property. Object search is divided into two types; global and local searches. We used a template matching using Genetic Algorithm (GA) in the global search. Moreover we use a random search in the local search. According to experimental results, this system can detect rough position of the object to be searched. The search accuracy obtained using the present method is 83.6%, and that of a comparative experiment using only GA is 42.1%. We have verified that our proposed method is effective for lost property detection. In the future, we need to increase search accuracy to search objects more stably. In particular, we need to improve local search. (Tokushima University Institutional Repository) ● Metadata: 113537 (Link to Publication Site) ● Publication site (DOI): 10.12792/JIIAE.3.59 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.12792/JIIAE.3.59 (Tokushima University Institutional Repository: 113537, DOI: 10.12792/JIIAE.3.59)
21.	Shin-ichi Ito, Momoyo Ito, Katsuya SATO, Shoichiro Fujisawa and Minoru Fukumi : Preference Analysis Method Applying Relationship between Electroencephalogram Activities and Egogram in Prefrontal Cortex Activities, --- How to collaborate between engineering techniques and psychology ---, International Journal of Advances in Psychology, Vol.3, No.3, 86-93, 2014. (Summary) This paper introduces a method of preference analysis based on electroencephalogram (EEG) analysis of prefrontal cortex activity. The proposed method applies the relationship between EEG activity and the Egogram. The EEG senses a single point and records readings by means of a dry-type sensor and a small number of electrodes. The EEG analysis adapts the feature mining and the clustering on EEG patterns using a self-organizing map (SOM). EEG activity of the prefrontal cortex displays individual difference. To take the individual difference into account, we construct a feature vector for input modality of the SOM. The input vector for the SOM consists of the extracted EEG feature vector and a human character vector, which is the human character quantified through the ego analysis using psychological testing. In preprocessing, we extract the EEG feature vector by calculating the time average on each frequency band: theta, low-beta, and high-beta. To prove the effectiveness of the proposed method, we perform experiments using real EEG data. These results show that the accuracy rate of the EEG pattern classification is higher than it was before the improvement of the input vector. (Keyword) egogram / electroencephalogram / Individual difference / self-organizing map / pattern recognition (Tokushima University Institutional Repository) ● Metadata: 113282 (Link to Publication Site) ● Publication site (DOI): 10.14355/ijap.2014.0303.03 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.14355/ijap.2014.0303.03 (Tokushima University Institutional Repository: 113282, DOI: 10.14355/ijap.2014.0303.03)
22.	Shin-ichi Ito, Momoyo Ito, Katsuya SATO, Shoichiro Fujisawa and Minoru Fukumi : Preference Classification Method Using EEG Analysis Based on Gray Theory and Personality Analysis, The Online Journal on Computer Science and Information Technology, OJCSIT, Vol.4, No.3, 276-280, 2014. (Summary) This paper introduces a method to classify the preference patterns of sounds on the basis of an electroencephalogram (EEG) analysis and a personality analysis. We analyze the EEG of the left prefrontal cortex by single-point sensing. For EEG recording, a dry-type sensor and few electrodes were used. The proposed feature extraction method employs gray relational grade detection on the frequency bands of EEG and egogram. The gray relational grade is used for extracting the EEG feature. The egogram is extracted for quantifying the subject's personality. The preference patterns generated when the subject is hearing a sound are classified using the nearest neighbor method. To show the effectiveness of the proposed method, we conduct experiments using real EEG data. These results show that the accuracy rate of the preference classification using the proposed method is better than that using the method that does not to consider the subject's personality. (Keyword) egogram / electroencephalogram / Individual difference / gray model / pattern recognition (Tokushima University Institutional Repository) ● Metadata: 112402 (Tokushima University Institutional Repository: 112402)
23.	Kuramoto Nobuhisa, Shin-ichi Ito, Katsuya SATO and Shoichiro Fujisawa : Electroencephalogram Analysis of Mechanisms Underlying Brain Activity during Voluntary Movement, International Journal of Bioscience, Biochemistry, Bioinformatics (IJBBB), Vol.4, No.1, 49-56, 2014. (Summary) This paper proposes an electroencephalogram (EEG)-based method for identifying the mechanism underlying brain activity during a voluntary movement. Our final goal is to develop a rehabilitation assistance system that can be used on a daily basis by observing a patients degree of functional recovery. In order to assist in ambulation rehabilitation, the mechanism related to the voluntary movement need to be identified. As a first step, we elucidated the mechanism underlying brain activity. The proposed method involves EEG recording, blind source separation, noise reduction, and feature enhancement. The EEG device has dry-type sensors. Independent component analysis is used to delink the signal. Median morphological filters are used to reduce EEG noise and emphasize the feature signal. To demonstrate the effectiveness of the proposed method, we conducted experiments using real EEG data. (Link to Publication Site) ● Publication site (DOI): 10.7763/IJBBB.2014.V4.309 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.7763/IJBBB.2014.V4.309 (DOI: 10.7763/IJBBB.2014.V4.309)
24.	Matsubara Kayoko, Shin-ichi Ito, Katsuya SATO, Masayuki Booka, Oku Hidehisa and Shoichiro Fujisawa : Difference in Road Surface Recognition by Disease and Grades of The Low Vision, Assistive Technology Research Series, Vol.33, 988-993, 2013. (Summary) In our previous studies, Lateral Bands that were placed in both side of Tactile Walking Surface Indicators, and the result of clinical testing of Lareral Bands indicated the effectiveness for the Low Vison to show walking way. In this paper, relation between visibility and miss-recognition was studied from the different point of view in date of the previous study. Influence both by the difference in visual impairment and the difference in grades of disability was also studied. As the results, it was suggested that 30% of luminance contrast was the optimum value for both keeping visibility of Tactile Walking Surface Indicators with Lateral Bands and producing no miss-recognition. (Link to Publication Site) ● Publication site (DOI): 10.3233/978-1-61499-304-9-988 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.3233/978-1-61499-304-9-988 (DOI: 10.3233/978-1-61499-304-9-988)
25.	Ikeda Norihiro, Takahashi Kazuya, Yamamoto Genji, Kimura Yuki, Shin-ichi Ito, Katsuya SATO and Shoichiro Fujisawa : Verification of LED Blocks used at Crosswalk Entrances for Persons with Visually Impairment, Assistive Technology Research Series, Vol.33, 982-987, 2013. (Summary) The eyesight of a person with low visual capacity (LV) might decrease remarkably under low-light conditions such as the morning, evening, and nighttime, decreasing their opportunities to go out. Research and development underway to ensure safe walking for LVs examined a walking guidance system in which LED blocks are embedded into the road at edges of warning blocks installed at crosswalk entrances. The LED block can be detected with the LVs residual vision. The detectability of this LED block was evaluated by subjective experiments performed by visually impaired and blind persons. (Link to Publication Site) ● Publication site (DOI): 10.3233/978-1-61499-304-9-982 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84889075823 (DOI: 10.3233/978-1-61499-304-9-982, Elsevier: Scopus)
26.	Maeno Tetsuya, Shoichiro Fujisawa, Shin-ichi Ito, Katsuya SATO, Ogawa Takehiro and Ishimoto Hiroh : Research on the Occupational Ability Evaluation Technique in the Group Work of Intellectually/Mentally Handicapped Persons by MODAPTS Method, Assistive Technology Research Series, Vol.33, 970-975, 2013. (Summary) The first objective is to analyze the characteristics of the intellectually/mentally disabled more deeply in order to help the government and companies to understand them and the employment rate to increase. The second objective is to review the work evaluation method of the intellually/mentally disabled anew through this evaluation in order to trigger the research on the intellectually/mentally disabled to be activated. The result of the verification performed by this research has showed that the success or failure in a continuous company employment is more significantly related to the factors of ``surrounding healthy persons'' which are external factors than the factors arising from the disability of a disabled person. (Link to Publication Site) ● Publication site (DOI): 10.3233/978-1-61499-304-9-970 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.3233/978-1-61499-304-9-970 (DOI: 10.3233/978-1-61499-304-9-970)
27.	Shoichiro Fujisawa, Miyazaki Tatsuya, Shin-ichi Ito and Katsuya SATO : Continuity of Tactile Walking Surface Indicators and Audible Pedestrian Signals at Crosswalks, Assistive Technology Research Series, Vol.33, 647-652, 2013. (Summary) Crossing an intersection is one of the most risky actions for a visually impaired person. A visually impaired pedestrian is guided to the crosswalk entrance by the tactile walking surface indicators "TWSIs". It is then important that he/she safely and smoothly cross the intersection using the audible pedestrian signal. However, neither connectivity nor the continuity between the TWSIs and the audible pedestrian signal travel support systems has been verified. The purpose of this research is to verify this connectivity and continuity. It should be possible to acquire basic scientific data for developing guidelines from the findings of this research. (Link to Publication Site) ● Publication site (DOI): 10.3233/978-1-61499-304-9-647 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84889022133 (DOI: 10.3233/978-1-61499-304-9-647, Elsevier: Scopus)
28.	Shin-ichi Ito, Yasue Mitsukura, Takafumi Saito, Katsuya SATO and Shoichiro Fujisawa : Visualization Method to Confirm Relationships among Optimal Solutions, INFORMATION, Vol.16, No.7(B), 4893-4906, 2013. (Summary) This paper proposes method to visualize the relationship between the gene and objective values of all individuals in a Pareto solution. The gene and objective values are expressed as color and gray scales, respectively. The rules of visualization apply three normalization techniques for coloring the gene values and the objective function values, respectively. The gene values normalize using maximum and minimum values over all gene of all suboptimal solutions, in each gene and in each individual, respectively. The objective values normalize using maximum and minimum values in each objective function. We define the visualization space based on the visualization rules. The information visualization technique in this paper has the advantage of indicating the Pareto solution and its details in one frame. To show the effectiveness of the proposed method, we apply the proposed method to benchmark problems. We found that it is possible to confirm the Pareto solution, its details and relationships among those easily in one frame. (Keyword) optimization / Information Visualization (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84886892577 (Elsevier: Scopus)
29.	Yohei Takeuchi, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : A Nonlinear Learning Algorithm for Large Scale Datasets, International Journal of Engineering and Innovative Technology, Vol.2, No.7, 407-412, 2013. (Summary) Nonlinear feature generation techniques are among the most important processing tools in pattern recognition. As linear feature generation techniques, Principal Component Analysis (PCA) and Fisher Linear Discriminant Analysis (FLDA) have been developed and improved in various ways. Simple-FLDA (SFLDA) is an improved version of FLDA and has demonstrated good performance in recognition experiments. In SFLDA, eigenvectors spanning the eigenspace are obtained with simple iterative calculations, unlike the original FLDA which needs to solve eigenvalue problem of a covariance matrix created with all input samples. However, FLDA and SFLDA algorithms are both linear feature extraction methods. Therefore, the steps used for obtaining the eigenspace from an input space might not be suitable with respect to large, complex datasets. For adjustability to such datasets, the kernel trick is applied to many linear feature generation methods, turning them into nonlinear methods. In this paper, we propose Simple Kernel Discriminant Analysis (SKDA) for higher recognition performance by applying a kernel trick to the SFLDA algorithm. Similar to the SFLDA algorithm, SKDA algorithm is composed with simple calculations, but in recognition experiments using the UCI datasets as well as face image dataset; its features equal or surpass those of the SFLDA algorithm.
30.	Shin-ichi Ito, Msashi Hamaguchi, Katsuya SATO and Shoichiro Fujisawa : Analysis of Individual Difference in EEG using Self-organizing Map, Journal of Life Support Engineering, Vol.24, No.4, 153-158, 2012. (Summary) This paper introduces a method to analyze an individual difference in an electroencephalogram (EEG) using a self-organizing map (SOM). The EEG recording position is a left prefrontal pole (Fp1) in the international 10-20 system. The device for recording the EEG uses dry-type sensor and a small number of electrodes. The EEG data is analyzed through the FFT and EEG features are extracted by calculating the time average of the power spectrum of five frequency bands which are theta, low-alpha, high-alpha, low-beta and high-beta, respectively. In order to confirm the individual difference in the EEG, the EEG patterns are classified using the SOM. The EEG patterns are based on human responses on sounds listened to. Finally, we conduct experiments using real EEG data. The experimental results suggest that it is possible to express the individual differences in the EEG using SOM. (Keyword) electroencephalogram / interindividual difference / self-organizing map / information visualization (Link to Publication Site) ● Publication site (DOI): 10.5136/lifesupport.24.153 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390282679343390976 ● Search Scopus @ Elsevier (DOI): 10.5136/lifesupport.24.153 (DOI: 10.5136/lifesupport.24.153, CiNii: 1390282679343390976)
31.	Shin-ichi Ito, Yasue Mitsukura, Katsuya SATO, Shoichiro Fujisawa and Minoru Fukumi : Interindividual Difference Analysis in Prefrontal Cortex EEGs Based on the Relationship with Personality, Journal of Signal Processing, Vol.16, No.5, 443-450, 2012. (Summary) In this study, we propose a method to analyse interindividual differences in electroencephalograms (EEGs) taken while a subject listened to music, which is based on the relationship of these EEGs to human personality. The frequencies of the EEGs that are analysed have components that contain both significant and immaterial information as well as different levels of importance. We express the different levels of importance through the weight values of frequencies using a real-coded genetic algorithm. Then, the EEG patterns, which are determined based on the evaluation of the impression on the music, are detected using the $k$-nearest neighbour method. We assume that differences between detection results for EEG patterns with the highest and lowest recognition accuracies show interindividual differences. Moreover, ego analysis based on psychological testing is used to analyse personality, and the ego score is determined using a questionnaire. Finally, we discuss the relationship between personality and interindividual differences observed in experimental EEGs. An interesting tendency of a person with a combined ego type is that he or she has a unique response to negative stimuli compared with that of positive stimuli. (Keyword) electroencephalogram / interindividual difference / personality / egogram (Link to Publication Site) ● Publication site (DOI): 10.2299/jsp.16.443 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390001204465273600 ● Search Scopus @ Elsevier (DOI): 10.2299/jsp.16.443 (DOI: 10.2299/jsp.16.443, CiNii: 1390001204465273600)
32.	上田喜敏, Shin-ichi Ito, Katsuya SATO and Shoichiro Fujisawa : Lower back pain survery during care work and Bed care load assessment, 福祉のまちづくり研究, Vol.14, No.2, 9-17, 2012. (Summary) We conducted a questionnaire about lower-back-pain to the participants at the Toyama lower-back-pain measure promotion study session (1925 persons). The question asked the presence of lower back pain and investigated what kind of lower back pain they had in respect of a care work place. Furthermore, the IEMG and the breathing gas analyzing apparatus analyzed the weight load (10kg) in the position which changed the height in the bed depended for lifting. About 60 percent of caregivers had a lower-back-pain person by a caregiver. When the persons who had lower back pain in the past were included, about 80 percent of them had lower back pain. Caregivers often felt lower back pain when engaging in care work at the bedside. The causation of the onset of chronic lower back pain was "the work which kept their body with half-sitting posture for a long time." In care work with a bed, work in the height of the waist reduced the load for a line active mass and breathing gas volume. The present care work had the high risk of causing lower back pain. To decrease the risk, the care worker should do bed care work at the height of the waist. This result should be adopted as a job standard. (Keyword) 介助作業場 / ベッド介助 / 筋電計 / 呼気ガス / 作業標準 / Care work place / Care work place / Bed care / Bed care / Electromyograph / Electromyograph / Breathing gas / Breathing gas / Job standard / Job standard (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390282680228340864 (CiNii: 1390282680228340864)
33.	Matsubara Kayoko, Hamada Takamaro, Shin-ichi Ito, Katsuya SATO, Masayuki Booka, Oku Hidehisa and Shoichiro Fujisawa : Mis-recognition of Road Surface by The Low Vision, Assistive Technology Research Series, Vol.29, 610-617, 2011. (Summary) The low vision sometimes miss-recognize road surface as holes or ditches. The purpose of our research is to find parameters of road surface that causes this kind of miss-recognition. As the preliminary result of our research, this paper describes how the low vision recognizes road surface incorrectly. Fifty-nine low vision persons were participated as subjects in these experiments. Visibility of road surface's line that have different parameters in width, luminance contrast, and illumination was experimentally studied. One of the three experiment, eight lines that have different parameters respectively were showed to a subject at a time. The lines were arranged in ascending order. At the end of each experiment, comments from a subject were obtained. (Link to Publication Site) ● Publication site (DOI): 10.3233/978-1-60750-814-4-610 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84865526906 (DOI: 10.3233/978-1-60750-814-4-610, Elsevier: Scopus)
34.	Shinji Takahashi, Akifumi Miyake, Shin-ichi Ito, Katsuya SATO, Masayuki Booka and Shoichiro Fujisawa : An evaluation of tactile walking surface indicators for the visually impaired, Assistive Technology Research Series, Vol.29, 587-594, 2011. (Summary) Tactile Walking Surface Indicators (TWSIs) are a vital walking aid for the visually impaired, especially those with complete blind person, but the blocks are still not available on all walkways. This study was performed with visually inpaired person to determine to what degree TWSI (in particular, blocks with raised parts to indicate caution) contribute to direction determination, with the goal of improving the utility of TWSIs. By comparing drift after 5m of straight line travel, significant differences were seen between the existence and nonexistence of border elevation for both square and zigzag patterns, confirming that determining direction when using square patterns over zigzag patterns. (Link to Publication Site) ● Publication site (DOI): 10.3233/978-1-60750-814-4-587 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84865482619 (DOI: 10.3233/978-1-60750-814-4-587, Elsevier: Scopus)
35.	Shin-ichi Ito, Yasue Mitsukura and Minoru Fukumi : Study on Model for Predicting the Intra-Individual Difference in Left Prefrontal Pole Electroencephalogram Variability and Its Evaluation, Electronics and Communications in Japan, Vol.94, No.5, 9-16, 2011. (Summary) This paper introduces a novel statistical model to estimate an intra-individual difference in left prefrontal cortex electroencephalogram (EEG) activities, and a method for evaluating the proposed model. It is known that an EEG contains the individual characteristics. However, extraction of these individual characteristics has not been reported. The analyzed frequency components of an EEG can be classified as the components that contain significant number of features and the ones that do not contain any. From the viewpoint of these feature differences, we propose the model for extracting features of the EEG. The model assumes a latent structure and employs factor analysis by considering the model error as personal error. We consider the first factor loading that is calculated by eigenvalue decomposition as the EEG feature. Furthermore, we use k-nearest neighbor (kNN) algorithm for evaluating the proposed model and the extracted EEG features. In general, the distance metric used is Euclidean distance. It is possible that the distance metric used depends on the characteristic of the extracted EEG feature and on the subject. Therefore, depending on the subject, we use one of the three distance metrics: Euclidean distance, cosine distance and correlation coefficient. Finally, in order to show the effectiveness of the proposed model, we perform an experiment using real EEG data. (Keyword) electroencephalogram / individual characteristics / genetic algorithm / distance function / k-nearest neighbor method (Link to Publication Site) ● Publication site (DOI): 10.1002/ecj.10326 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1002/ecj.10326 (DOI: 10.1002/ecj.10326)
36.	Fuji Taiki, Shin-ichi Ito, Yasue Mitsukura, Tanabata Takanari, Kimura Nobutaka and Morita Toshio : Aligning Real Space with the 2D Model on See-through HMD Using GA with Interactive Handling, Journal of Signal Processing, Vol.13, No.4, 347-350, 2009. (Summary) In this paper, we propose an approach for the aligning the real space with the 2D model. In our approach, we use a monocular see-through head mount display (HMD) and a virtual reality (VR) sensor. This sensor can measure three degree of freedom (3DOF) angle of head directions. However, this sensor has some errors, which are divided into three types of errors. The first one is the drift errors of the sensor. Second one is the false of the sensor detections. These errors are caused by the sensor itself. The last one is caused by personal visual features. Therefore, we solve these problems with the genetic algorithm (GA) and threshold processing. In this system, we incorporate personal subjective evaluation into the optimization system to cut down processing speed and get close to the answer. Users handle the model interactively to align with real space using air mouse. (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1523106605325673984 (CiNii: 1523106605325673984)
37.	Shin-ichi Ito, Yasue Mitsukura and Minoru Fukumi : A model for extracting personal features of an electroencephalogram and its evaluation method, IEEJ Transactions on Electronics, Information and Systems, Vol.129, No.1, 17-24, 2009. (Summary) This paper introduces a model for extracting features of an electroencephalogram (EEG) and a method for evaluating the model. In general, it is known that an EEG contains personal features. However, extraction of these personal features has not been reported. The analyzed frequency components of an EEG can be classified as the components that contain significant number of features and the ones that do not contain any. From the viewpoint of these feature differences, we propose the model for extracting features of the EEG. The model assumes a latent structure and employs factor analysis by considering the model error as personal error. We consider the EEG feature as a first factor loading, which is calculated by eigenvalue decomposition. Furthermore, we use a k-nearest neighbor (kNN) algorithm for evaluating the proposed model and extracted EEG features. In general, the distance metric used is Euclidean distance. We believe that the distance metric used depends on the characteristic of the extracted EEG feature and on the subject. Therefore, depending on the subject, we use one of the three distance metrics: Euclidean distance, cosine distance, and correlation coefficient. Finally, in order to show the effectiveness of the proposed model, we perform a computer simulation using real EEG data. (Keyword) electroencephalogram / personal feature / genetic algorithm / distance function / k-nearest neighbor method / electroencephalogram / personal feature / genetic algorithm / distance function / -nearestneighbormethod (Link to Publication Site) ● Publication site (DOI): 10.1541/ieejeiss.129.17 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390282679581936896 ● Search Scopus @ Elsevier (DOI): 10.1541/ieejeiss.129.17 (DOI: 10.1541/ieejeiss.129.17, CiNii: 1390282679581936896)
38.	Shin-ichi Ito, Yasue Mitsukura, Hiroko Miyamura, Takafumi Saito and Minoru Fukumi : Extraction of EEG Characteristics While Listening to Music and Its Evaluation Based on a Latency Structure Model with Individual Characteristics, Electronics and Communications in Japan, Vol.92, No.1, 9-17, 2009. (Summary) EEG is characterized by the unique and individual characteristics. Little research has been done to take into account the individual characteristics when analyzing EEG signals. Often the EEG has frequency components which can describe most of the significant characteristics. Then there is the difference of importance between the analyzed frequency components of the EEG. We think that the importance difference shows the individual characteristics. In this paper, we propose a new EEG extraction method of characteristic vector by a latency structure model in individual characteristics (LSMIC). The LSMIC is the latency structure model, which has personal error as the individual characteristics, based on normal distribution. The real-coded genetic algorithms (RGA) are used for specifying the personal error that is unknown parameter. Moreover we propose an objective estimation method that plots the EEG characteristic vector on a visualization space. Finally, the performance of the proposed method is evaluated using a realistic simulation and applied to a real EEG data. The result of our experiment shows the effectiveness of the proposed method. (Link to Publication Site) ● Publication site (DOI): 10.1002/ecj.10009 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1002/ecj.10009 (DOI: 10.1002/ecj.10009)
39.	Tomita Yohei, Shin-ichi Ito, Koda Naoko, Jianting Cao, Yasue Mitsukura and Minoru Fukumi : Objectively Psychological Evaluation Using the EEG, Journal of Signal Processing, Vol.12, No.6, 465-472, 2008. (Summary) The animal assisted therapy (AAT) has been known to have psychological and social effects on human being. However, scientific research is insufficient yet. Because it is difficult to have done in the medical clinic. Furthermore, the government cannot allow as a medical care because of nothing of clear proof. Therefore, in order to show a scientific basis of the AAT effects, we analyze the electroencephalogram (EEG) in the case of using the AAT. The EEG is generated by the brain activity, so that there is the correlation between the EEG and mentality. First of all, we define the healing by questionnaires. Secondly, we extract the correlation between the EEG features and mentality. Finally, we investigate the correlation between EEG features and the healing.
40.	Shin-ichi Ito, Yasue Mitsukura, 宮村浩子, 斎藤隆文, Minoru Fukumi and Cao Jianting : Detection Method of Music to Match Users Mood in Prefrontal Cortex EEG Activity, INFORMATION, Vol.10, No.6, 889-901, 2007. (Summary) In this article, we propose a method for detecting music to match a users mood in prefrontal cortex electroencephalogram (EEG) activity. The frequencies of the EEG analyzed are the components that contain significant and immaterial information. We focused on the significant frequency combinations. These frequency combinations are thought to express personal features of EEG activity. The proposed method calculates the percentage of the spectrum of these frequency combinations and evaluates whether the music matches the users mood through a simple threshold processing. A genetic algorithm (GA) is used to specify the frequency of personal features on the EEG.
41.	Shin-ichi Ito, Yasue Mitsukura, Takafumi Saito, Hiroko Miyamura and Minoru Fukumi : EEG Characteristic Extraction Method of Listening Music and Objective Estimation Method Based on Latency Structure Model in Individual Characteristics, IEEJ Transactions on Electronics, Information and Systems, Vol.127, No.6, 874-881, 2007. (Summary) EEG is characterized by the unique and individual characteristics. Little research has been done to take into account the individual characteristics when analyzing EEG signals. Often the EEG has frequency components which can describe most of the significant characteristics. Then there is the difference of importance between the analyzed frequency components of the EEG. We think that the importance difference shows the individual characteristics. In this paper, we propose a new EEG extraction method of characteristic vector by a latency structure model in individual characteristics (LSMIC). The LSMIC is the latency structure model, which has personal error as the individual characteristics, based on normal distribution. The real-coded genetic algorithms (RGA) are used for specifying the personal error that is unknown parameter. Moreover we propose an objective estimation method that plots the EEG characteristic vector on a visualization space. Finally, the performance of the proposed method is evaluated using a realistic simulation and applied to a real EEG data. The result of our experiment shows the effectiveness of the proposed method. (Keyword) 脳波 / 個人特性 / 潜在構造モデル / 遺伝的アルゴリズム / 可視化 / electroencephalogram / individual characteristics / latency structure model / real-coded genetic algorithms / visualization (Link to Publication Site) ● Publication site (DOI): 10.1541/ieejeiss.127.874 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390001204606321152 ● Summary page in Scopus @ Elsevier: 2-s2.0-34250004324 (DOI: 10.1541/ieejeiss.127.874, CiNii: 1390001204606321152, Elsevier: Scopus)
42.	Shin-ichi Ito, Yasue Mitsukura, Minoru Fukumi and Norio Akamatsu : Proposal of the EEG Analysis Method Using the Individual Characteristic of the EEG, IEEJ Transactions on Electronics, Information and Systems, Vol.124-C, No.6, 1259-1266, 2004. (Summary) 本論文は，音楽を聴いている場合の脳波を解析することにより，脳の状態を推定する手法を提案している．特に，遺伝的アルゴリズムによる特徴選択と因子分析による特徴解析により脳波分類に適した個人特徴を抽出し，ニューラルネットワークにより学習識別する手法を提案している．音楽鑑賞時における実際の脳波信号に対する計算機シミュレーションにより，本手法の有効性を定量的に評価している． (Keyword) EEG / Genetic Algorithms / Factor Analysis / Neural Networks (Link to Publication Site) ● Publication site (DOI): 10.1541/ieejeiss.124.1259 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390282679582766464 ● Search Scopus @ Elsevier (DOI): 10.1541/ieejeiss.124.1259 (DOI: 10.1541/ieejeiss.124.1259, CiNii: 1390282679582766464)

Academic Letter:

1.	Kyohei Hirono, Tatsuya Miyazaki, Shin-ichi Ito, Katsuya SATO and Shoichiro Fujisawa : Research on continuity among assistive devices for personal mobility of visually impaired persons around crossings, Assistive Technology Research Series, Vol.35, 435-436, 2014. (Summary) Tactile walking-surface indicators (TWSIs) are necessary for visually impaired persons to walk independently. Audible pedestrian signals are also necessary for them to cross intersections. TWSIs support approaching the intersection, and audible pedestrian signals support crossing the intersection. However, the support of audible pedestrian signals may not necessarily be related to the support of the TWSIs. This research sought to verify how visually impaired persons are guided by different positions where the audible pedestrian signals are set up. (Keyword) visually impaired person / Tactile Walking Surface Indicator / audible pedestrian signal (Link to Publication Site) ● Publication site (DOI): 10.3233/978-1-61499-403-9-435 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84902257466 (DOI: 10.3233/978-1-61499-403-9-435, Elsevier: Scopus)

Proceeding of International Conference:

1.	Momoyo Ito, Daiki Fujiwara, Shin-ichi Ito and Minoru Fukumi : Fundamental Study on the Influence of Driver Distraction Level on Face Orientation Change at Intersections, Proceedings of 7th International Symposium on Future Active Safety Technology toward zero traffic accidents, Thu-PM1-B-5, Kanazawa, Nov. 2023.
2.	Shoichiro Fujisawa, Shoya Nishimori, Yoshifuru Atsuta, Kenji Sakami, Jiro Morimoto, Jyunji Kawata, Yoshio Kaji, Mineo Higuchi, Shin-ichi Ito and Tomoyuki Inagaki : Stimulation and Visibility of Flashing Lights With Different Illuminance, Proceedings of the AHFE 2023 International Conference on Human Factors in Transportion, Vol.87, 62-70, USA, Jul. 2023. (Summary) This study aimed to present a demonstration experiment to determine a highly visible light emission pattern while feeling that the stimulus is suppressed using a sensory evaluation. A sensory evaluation of stimulus intensity and visibility of simple blinking that repeats on and off and fade-in/fade-out blinking that gradually brightens andfades was performed to investigate the trade-off relationship between stimulus adaptation and arousal. Light-emitting blocks that are constantly lit, simple blinking, and fade-in/fade-out blinking were compared, and the relationship between the blinking stimulus and visibility was clarified. The subjects underwent a sensory evaluation forthree types of environmental illuminance (20, 50, and 100 lx) and were asked to evaluate the strength of stimulation, visibility, and discomfort of each blinking pattern using normal light as a reference. When the surrounding illuminance was low (20 lx), the overall evaluation value was higher because the fade-in/fade-out flashing was less stimulating and easier to find. When the surrounding illuminance was high (50 lx), simple blinking was more stimulating and easier to find, and the overall evaluation value was higher. Although it was a simple blinking, it seemed that the visibility forpeople with visual impairment had been secured. Moreover, when the surrounding illuminance was 100 lx, the difference in the illuminance between the surroundings and the light-emitting block was small, the light-emitting block in both simple blinking and fade-in/fade-out blinking was difficult to find, and the overall evaluation value was very low. Simple or fade-in/fade-out blinking is selected based on the surrounding illuminance. By installing light-emitting blocks that blink in an optimal blinking pattern at the entrance of a crosswalk, it is expected that the blinking will be effective in guiding people with visual impairment while considering pedestrians and the surrounding environment. (Keyword) Person with visually impairment / Low vision / LED block / Visibility / Sensory test / Awakening / Adaptation (Link to Publication Site) ● Publication site (DOI): 10.54941/ahfe1003654 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.54941/ahfe1003654 (DOI: 10.54941/ahfe1003654)
3.	Shoichiro Fujisawa, Masaki Okegawa, Kenji Sakami, Jyunji Kawata, Jiro Morimoto, Yoshio Kaji, Mineo Higuchi and Shin-ichi Ito : On the Stimulation and Visibility by Blinking Light Emitting Block for Low Vision, Proceedings of the AHFE 2022 International Conference on Human Factors in Transportion, Vol.37, 423-7, USA, Jul. 2022. (Summary) Approximately 80% of people with visual impairment have residual visual acuity. As such, the authors have developed a light-emitting block that is placed at the entrance of a pedestrian crossing to support those with visual impairment at night. A blinking light has generally been used for selectively distinguishing the target light source from the surrounding light source. However, this type of blinking (i.e., simple blinking) can be stimulating, making it suitable for night-time road construction sites but not for normal road environments. Therefore, the authors have focused on phase-in-phase-out blinking, which gradually brightens and darkens with each blink, thereby suppressing stimulation. This blinking pattern has been considered to reduce visibility just by suppressing the stimulus. However, focusing on the human sensory characteristics of adaptation and arousal, evidence has suggested the existence of a blinking cycle and pattern that awakens while suppressing stimuli. Therefore, the current study sought to identify blinking cycles and patterns that awaken while suppressing light stimulation. The intensity of light stimulation is evaluated via sensory evaluation. Based on the intensity of the stimulus and degree of arousal, we searched for the optimum blinkingcycle that promotes adaptation, a human sensory characteristic, and arousal from the blinking pattern and verified the visibility of the obtained blinking pattern in individuals with visual impairment and healthy subjects. Previously, we had conduct-ed an experiment in a region with a period of 47 s. This time, however, we con-ducted an experiment in a region with a period of 2 3.5 s. This study verified that phasein-phase-out blinking is effective for pedestrians while considering the surroundings. The purpose of this experiment was to determine the optimal blinking method for supporting people with visual impairment. Although it is desirable to provide support for people with visual impairment by promoting stronger visibility, introducing flashing blinks in public facilities may cause discomfort to healthy people around them. For instance, introducing a blinking light of approximately 1 Hz, which has a fast blinking cycle, at the entrance of a pedestrian crossing can enter the drivers field of vision, causing discomfort and making the driver look away, thereby leading to accidents. The ideal blinking light for support is that which limits arousal to some extent anddoes not induce discomfort. Based on the blinking patterns used in this experiment under the aforementioned condition, we believe that a fade-in-fade-out type pattern with a blinking cycle of 2 s and a lighting time of 2 and 3 s would be effective. The reason for this is that the intensity of the stimulus obtained from the sensory evaluation of healthy subject and those with visual impairment is suppressed, visibility is secured to some extent, and the blinking method does not induce discomfort. Thus, the fade-in-fade-out blinking method can be expected to suppress discomfort. (Keyword) Person with visually impairment / Low vision / LED block / Visibility / Sensory test (Link to Publication Site) ● Publication site (DOI): 10.54941/ahfe1001636 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.54941/ahfe1001636 (DOI: 10.54941/ahfe1001636)
4.	Shoichiro Fujisawa, Kenji Sakami, Tomoya Sakaguchi, Takatoshi Aoki, Masaki Okegawa, Jiro Morimoto, Yoshio Kaji, Jyunji Kawata, Yoshio Kaji, Mineo Higuchi and Shin-ichi Ito : Visual Guidance by Blinking Light of LED Block for Individuals Affected with Low Vision, Proceedings of the AHFE 2021 International Conference on Human Factors in Transportion, 423-433, USA, Jul. 2021. (Summary) Approximately 80% of the visually impaired are composed of individuals with low vision. As people with low vision have residual visual acuity, light guidance has been deemed effective. Thus, the authors have developed a light-emitting block that is placed at the entrance to a pedestrian crosswalk to support the visually impaired at night. Generally, blinking light is accepted as a method to selectively distinguish a target light source from an ambient light source. However, on/off blinking lights, such as those used at a road construction site at night, are unsuitable for normal road environments. Therefore, the authors focused on fade-in/fade-out blinking, which gradually brightened and darkened, suppressing inappropriate visual stimulation. Therefore, this study aimed to search for blinking cycles and patterns that ensured visibility while suppressing light stimulation and reducing discomfort. (Keyword) Person with visually impairment / Low vision / LED block / Visibility / Sensory test (Link to Publication Site) ● Publication site (DOI): 10.1007/978-3-030-80091-8_50 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85112232561 (DOI: 10.1007/978-3-030-80091-8_50, Elsevier: Scopus)
5.	Shoichiro Fujisawa, Katsuya Sato, Shin-ichi Ito, Jyunji Kawata, Jiro Morimoto, Mineo Higuchi, Yoshio Kaji and Masayuki Booka : Manual Wheelchair Tire Pressure and Ride Comfort When Pushed by a Caregiver, Proceedings of the AHFE 2021 International Conference on Human Factors in Transportion, 1144-1151, USA, Jul. 2021. (Summary) Wheelchairs are very convenient for the elderly who cannot walk, but the vibration generated during use can cause discomfort to the rider and can have consequences, such as convulsions. Tire pressure is one of the factors thataffects the ride quality of wheelchairs. Wheelchairs are often used indoors, in which case they travel on hard flooring or on carpet. The purpose of this study was to investigate how the road surface on which the wheelchair travels and thetire pressure of the wheelchair contribute to the vibration experienced by the rider, and the influence of the vibration on the human body. We used an air pressure indicator that measured an English valve. The vibration generated was measured and the sensory perception of comfort and security were evaluated on hard and soft surfaces at different tire pressures. The vibration test revealed that vibration tends to increase as tire pressure increases and that ride comfortdecreases as tire pressure increases. (Keyword) Wheelchair / Tire pressure / Travel over a level difference / Vabration / Sensor evaluation (Link to Publication Site) ● Publication site (DOI): 10.1007/978-3-030-80091-8_136 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85112192328 (DOI: 10.1007/978-3-030-80091-8_136, Elsevier: Scopus)
6.	Hideyuki Mimura, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Personal Authentication and Recognition of Aerial Input Hiragana Using Deep Neural Network, Proceedings Volume 11794, Fifteenth International Conference on Quality Control by Artificial Vision, 1-7, Tokushima (Running Virtually), May 2021. (Summary) We use Leap Motion and a deep neural network to perform personal authentication and character recognition of all hiragana characters entered in the air. We use Leap Motion to detect the index finger and store its trajectory as time series data. The input data was pre-processed to unify the data length by linear interpolation. For identification, the accuracy of Long Short Term Memory (LSTM) was compared with Support Vector Machine (SVM). As a result, SVM and LSTM achieved 97.25% and 98.18% F-measure in character recognition, respectively. In personal authentication, SVM has an accuracy of 92.45%, False Acceptance Rate (FAR) was 0.73%, and False Rejection Rate (FRR) was 41.59%. On the other hand, LSTM had an accuracy of 96.13%, FAR of 1.73% and FRR of 14.55%. Overall, the LSTM performed better than the SVM. (Keyword) Biometrics / Personal Authentication / Leap motion / Aerial Input Hiragana / deep learning (Link to Publication Site) ● Publication site (DOI): 10.1117/12.2585333 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85112416684 (DOI: 10.1117/12.2585333, Elsevier: Scopus)
7.	Eisuke Yamamoto, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Separation of Compound Actions with Wrist and Finger Based on EMG, Proc. SPIE 11794, Fifteenth International Conference on Quality Control by Artificial Vision, 1-7, Tokushima (Running Virtually), May 2021. (Summary) In this paper, we propose to measure the EMGs of the wrist and fingers using dry-type sensors worn near the wrist, and to separate the measured data into wrist and finger EMGs by using independent component analysis (ICA). Then we can confirm the EMGs of the wrist and fingers from the complex motion and realize individual identification in more complex motions. The final goal of this study is to identify individual motions from complex motions. In this paper, as a preliminary step, the ICA is used to isolate compound motions and the validity of the method is evaluated. We measured the EMGs for three days and four motions. The results of the combination of FastICA, Infomax and JADE, respectively, were evaluated by the correlation coefficient with the original signal. The most accurate combination was FastICA + Infomax with an accuracy of 70.5%. (Keyword) Biometrics / Independent Component Analysis / Wrist EMG / Hand motion recognition (Link to Publication Site) ● Publication site (DOI): 10.1117/12.2585334 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85112477963 (DOI: 10.1117/12.2585334, Elsevier: Scopus)
8.	Tsubasa Fukui, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Motion Identification of fingerspelling by Wrist EMG Analysis, Proc. 2020 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE, 1739-1744, Running Virtually, Dec. 2020. (Summary) Recent years, interfaces using biometric information are progressing. Electromyogram(EMG) has been used in a variety of situations. Many studies have measured EMG in the shoulders and arms where there is a lot of muscle mass In addition, wet type sensors have been often used. However those are inconvenient to use in everyday life and high cost. In this research we measure wrist EMG for convenience and cost. Currently researches have been done on the wrist EMG motion identification and personal identification. These studies have conducted simple movements and a large number of electrodes for discrimination. Furthermore authentication by password sequence with gestures has not been done. In this paper we propose to realize motion identification and personal authentication with complex movements using a small number of electrodes. The measured data was preprocessed such as removing noise and smoothing. We compared the accuracies obtained using Support Vector Machine(SVM) and Long Short term memory(LSTM) for motion identification and authentication. The accuracies obtained using SVM and LSTM were 60.4%and 62.4%, respectively. In this case the number of data was small. It is therefore necessary for increasing the number of data to perform deep learning. (Keyword) Biometrics / neural network / machine learning / Wrist EMG / Hand motion recognition (Link to Publication Site) ● Publication site (DOI): 10.1109/SSCI47803.2020.9308269 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85099688079 (DOI: 10.1109/SSCI47803.2020.9308269, Elsevier: Scopus)
9.	Kazuki Nagatomo, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Finger Motion Identification Based on Wrist EMG Analysis Using Machine Learning, Proc. of International Conference on System Science and Engineering 2020, 522-523, Sep. 2020. (Summary) In this paper we identify 3 pattern motions that are Rock-scissors-paper finger motions. Conventional work recognized such motions using EMG in a stable state. We try to identify the motions by using the short-time wrist EMG at the beginning of movement and SVM. As a result, we obtained identification accuracy of 82%. (Keyword) EMG / Finger motion / machine learning / SVM
10.	Shoichiro Fujisawa, Katsuya Sato, Shin-ichi Ito, Jyunji Kawata, Jiro Morimoto, Mineo Higuchi and Masayuki Booka : Effects of Tire Pressure on Wheelchairs When Riding on Uneven Ground, Proceedings of the AHFE 2020 International Conference on Human Factors in Transportion, 920-927, San Diego, Jul. 2020. (Summary) Wheelchairs for the elderly and people with disabilities are very convenient, but vibration generated during movement may cause user discomfort and even motion sickness. Tire pressure is one factor affecting wheelchair ride quality. The commonly used English-style valve for wheelchairs allows air to be filled into the tire but cannot be used to adjust tire air pressure. Therefore, it is unclear how wheelchair tire pressure affects the vibration of the wheelchair.Additionally, it is necessary to consider the burden on the assistant. Wheelchairs are often used indoors on hard floors and relatively flexible carpets. The purpose of this study is to clarify the relationship between vibrations generated in a wheelchair, the surface on which a wheelchair travels, tire pressure, and the influence of these vibrations on the human body. By using an air pressure indicator suitable for use with the English-style valve to measure tire air pressure, vibration measurements were conducted under controlled conditions by rolling a manual wheelchair at constant speed. The vibration test revealed that the vibration tends to increase as tire pressure increases. Additionally, it wasfound that ride comfort decreases as tire pressure increases. (Keyword) Wheelchair / Tire pressure / Travel over a level difference / Vabration / Sensor evaluation (Link to Publication Site) ● Publication site (DOI): 10.1007/978-3-030-51828-8_122 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85088740618 (DOI: 10.1007/978-3-030-51828-8_122, Elsevier: Scopus)
11.	Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Japanese Sign Language Classification Using Gathered Images and Convolutional Neural Networks, Proceedings of 2020 IEEE 2nd Global Conference on Life Sciences and Technologies (LifeTech), 349-350, Kyoto, Mar. 2020. (Summary) A method to classify Japanese sign language (JSL) words using a gathered image generation method and a convolutional neural network (CNN) is proposed. The JSL words consist of words that are often used in information queries. Gathered images are generated based on the difference between the first image, which indicates the start position of a JSL word and target images, which indicate the motion position for a JSL word. The CNN is used to extract features from the gathered images. The JSL words are classified using a support vector machine. To show the effectiveness of the proposed method, we conducted experiments and computer simulations. We confirmed that the mean recognition accuracy for 10, 20, and 42 JSL words was 99.2%, 94.3, and 86.2%, respectively. (Keyword) Japanese sign language / gathered image / convolutional neural networks / communication tool / welfare system (Link to Publication Site) ● Publication site (DOI): 10.1109/LifeTech48969.2020.1570618953 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1109/LifeTech48969.2020.1570618953 (DOI: 10.1109/LifeTech48969.2020.1570618953)
12.	CHUNYU GUO, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Personal Authentication by Walking Motion using Kinect, Proc. of ISPACS 2019, 1-2, Taipei, Dec. 2019. (Summary) In recent years, with the rapid development of the information society, the importance of personal authentication has become higher and higher. This paper focuses on the use of a Kinect sensor to obtain walking characteristics for personal authentication. In terms of the proposal method, Kinect is used to obtain body's physical feature quantity, such as the angle of joint bending when a person walks, the displacement of coordinates. In terms of learning recognition, the support vector machine and the obtained feature amount are used for personal authentication. We measured 3 subjects data 5 times a day for 4 days, and obtained an average recognition accuracy of 77.4% using crossvalidation. (Keyword) Personal authentication / SVM / Walking motion / Kinect (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85081092650 (Elsevier: Scopus)
13.	Shan Xiao, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Study on Discrimination of Finger Motions based on EMG signals, Proc. of ISPACS 2019, 1-2, Taipei, Dec. 2019. (Summary) In recent years, biological signals have attracted attention as tools for human interfaces. Researches on biological signals have been actively conducted. In this paper, we propose a method which distinguishes ten motions, such as One Two Three Four Five Six Seven Eight Nine and Ten by measured the electromyogram of the wrist. We measure data by installing 8 dry type sensors on the right wrist. We carry out frequency analysis using FFT and try to take 3 kinds of methods to remove noise. Finally, we use Support Vector Machine (SVM) for identification and classification. We conducted experiments with four subjects. In the experimental result, the accuracy of finger motions recognition was 65%. In the future, we will also add more methods to remove noise, and try to find other methods to improve the accuracy in the research. (Keyword) electromyogram activity / SVM / Finger motion (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85081091347 (Elsevier: Scopus)
14.	Kazuki Shimamoto, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : A Nail Image Analysis Method to Evaluate Accumulated Stress Using Fuzzy Reasoning, Proc. of ISPACS 2019, 1-2, Taipei, Dec. 2019. (Summary) In this paper, we propose a nail image analysis methodtoevaluateaccumulatedstressusingfuzzyreasoning.The proposed method consists of three stages: measurement, feature extraction, and stress evaluation. In the measurement, we take a nail image. In the feature detection, we extract the lunula of the nail to calculate its height. In the stress evaluation, we evaluate accumulated stress using fuzzy reasoning. The experimental results suggest that the proposed method can determine the presence or absence of accumulated stress. (Keyword) fuzzy reasoning / stress evaluation / nail image (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85081091661 (Elsevier: Scopus)
15.	Yurika Fujii, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Detection of Dangerous Objects By Pan-tilt Camera, Proc. of ISPACS 2019, 1-2, Taipei, Dec. 2019. (Summary) Security cameras have increased in public facilities. The number of crimes has decreased by security cameras, but we will have too much data of cameras. In this paper, we have aim of security improvement. First, we search whether there are humans in images by OpenPose. We then obtain position of humans hands. Finally, we detect dangerous objects around the hands by image classification. (Keyword) Pan-tilt Camera / Transfer learning / machine learning / Deep learning / dangerous object (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85081089362 (Elsevier: Scopus)
16.	Misato Matsushita, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Verication of Regression Analysis of Muscle Fatigue Using Wrist EMG, Proc. of ISPACS 2019, 1-2, Taipei, Dec. 2019. (Summary) Muscles can cause injury by training to improve physical performance. However, there are few ways to assess muscle fatigue currently. Therefore, in this paper, muscle fatigue is evaluated using surface EMG(ElectroMyoGram). For discrimination, we used linear regression analysis and support vector regression, and performed comparative verication. (Keyword) electromyogram activity / Muscle Fatigue / machine learning / Deep learning / SVM (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85081094519 (Elsevier: Scopus)
17.	Shin-ichi Ito, Momoyo Ito, Shoichiro Fujisawa and Minoru Fukumi : Electroencephalogram Data for Classifying Answers to Questions with Neural Networks and Support Vector MachineNetworks, Proceedings of International Conference on Electronics and Signal Processing, ICESP2019, Hong Kong, Aug. 2019. (Summary) This paper proposes a method for classifying answers to conversational questions from electroencephalogram (EEG) data. The proposed method includes steps for EEG recording, feature extraction, and answer classification. For EEG measurements, this paper employs a simple electroencephalograph. The EEG signals from the frontal lobe are recorded. The EEG features are calculated by normalizing the EEG signals and using convolutional neural networks (CNN) for extraction. The answers to questions are then classified from the EEG features using a support vector machine. To show the effectiveness of the proposed method, we conducted experiments using real EEG data. The experimental results confirm that the mean recognition accuracy was 99% or more if the CNN features are individual to the subject. These results suggest that the answers to yes/no questions can be classified using EEG signals and that the EEG analysis technique using CNN and the support vector machine is suitable for extracting and classifying EEG features. (Keyword) electroencephalogram / answers of questions / convolutional neural networks / personal differences / human support system / human communication
18.	Kohei Sasada, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Proposal of Japanese Sign Language Motion Recognition Method using Leap Motion, Proc. of ICEAS 2019, 74-84, Honolulu, Aug. 2019. (Summary) In Japan, in recent years, many people suffer from hearing and language related disorders. Sign language is then one of their main means of communication. As their opinion, there is a demand for smooth communication means. In recent years, researches using motion sensors are in progress. In particular, we focus on Leap Motion as a motion sensor in this research. The final goal is to build a sign language recognition system, and this paper proposes a sign language recognition method as the first step. The measured data using the Leap Motion sensor are subjected to preprocessing such as vectorization, distance transformation, noise processing, data length change, and correction of operation start position. An identification unit uses SVM and CNN to compare their accuracy. The result was 98.86% for SVM and 97.11% for CNN. SVM was 1.75% higher than CNN in accuracy. However, the CNN used in this experiment has a simple layer configuration, and its accuracy can be expected to be improved by changing the layer configuration. In addition, it is considered that the accuracy is improved by adding a feature amount or devising pre-processing as a measure for improving misidentification. In the future, we would like to introduce those methods and cope with sign languages other than Japanese sign language. (Keyword) Sign Language / Leap Motion / machine learning / Deep learning / CNN
19.	Kenta Matsumura, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Driver State Estimation Based on Visual and Heart Rate Statistical Features, Proc. of ICEAS 2019, 62-73, Honolulu, Aug. 2019. (Summary) Inattentive driving is one of the main causes of traffic accidents. It is required to develop a system for detecting this inattentive state from in-vehicle information and biological information. In this paper, we obtain visual information and heart rate information and describe the driver's state estimation. We verify the usefulness of the proposed method in a simulated environment with a driving simulator. Moreover, in order to aim at the installation of a real vehicle environment, we build a general model which doesn't choose the target person, and an individual model focusing on driving characteristics such as individual habits and investigate the usefulness of these models. (Keyword) eye movement / heart rate variability / drivers inattentiveness / machine learning
20.	Ryosuke Saitoh, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Construction of Japanese Vowel Identification System Using Lips EMG, Proc. of ICEAS 2019, 41-49, Honolulu, Aug. 2019. (Summary) Recent years, biological signals have attracted much attention as a tool of human interface. Electromyogram(EMG) has been used in a variety of situations in particular. On the one hand, people who lost their voices due to vocal disorder communicate by alternative means of voice. In recent years, researches for detecting speech by electromyography analysis and image analysis have been actively conducted. Therefore, in this paper, we measure EMG by attaching dry type sensors to facial muscles, and identify Japanese vowels. A method proposed in this paper consists of an input, a preprocessing, and a learning identifying sections. We attach dry type sensors to muscles around the lips and measure EMG signals. We use a convolutional neural network(CNN) for learning and identification. In addition, we try to use a support vector machine(SVM) for comparison. The average identification accuracy by CNN was 67.4%. On the other hand, the average identification accuracy by SVM was 70.2%. In future work, we will try to increase the number of data and improve CNN accuracy. Therefore, it is necessary to ameliorate a layer configuration in CNN. (Keyword) Deep Learning / Lips EMG / Japanese Vowel Identification
21.	Shun Yamamoto, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : An authentication system for aerial input numerals using Leap motion and CNN, Proc. of ICEAS 2019, 29-40, Honolulu, Aug. 2019. (Summary) As information technology has advanced in recent years, services which include personal authentication systems such as ATM are increasing. Current main personal authentication systems include IC cards, passwords, and biometrics authentication such as fingerprint authentication. However, there are several problems in these systems. Therefore, better systems are needed. As such systems, we propose a method to write numerals in the air using the Leap motion and to carry out personal authentication from such aerial handwriting data. We try to authenticate numerals 0 to 9 which are written by three subjects. After applying some pre-processing to inputs, learning and identification are carried out using CNN which is a method of machine learning. As a result, average identification accuracy was 92.7%. From this result, it is suggested that input numerals in the air can be authenticated and there is a possibility to construct a new personal authentication system. (Keyword) Deep Learning / Leap Motion / Authentication
22.	Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : A Method of Classifying Japanese Sign Language using Gathered Image Generation and Convolutional Neural Networks, Proceedings of International Conference on Pervasive Intelligence and Computing, PICom2019, 868-871, Fukuoka, Aug. 2019. (Summary) This paper proposes a method for classifying Japanese sign language (JSL) using a combined gathered image generation technique and a convolutional neural network (CNN) approach. In the combined gathered image generation, the maximum difference from the previous and next images is calculated for each block, and the block information that had maximum difference was embedded into an image on all blocks. After information on all images has been gathered into single words, the CNNs are used to extract features for the classification of JSL words. A multi-class support vector machine (SVM) is then used to classify words related to greeting and requesting. The mean and the standard deviation of the recognition accuracy of the proposed method were experimentally shown to be 84.2% and 4%, respectively. These results suggest that it is possible to obtain information for classifying 10 JSL words using the proposed combined gathered image generation and CNN approach. (Keyword) finger language / gathering / gathered image / pattern recognition / convolutional neural networks (Link to Publication Site) ● Publication site (DOI): 10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00157 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85075168431 (DOI: 10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00157, Elsevier: Scopus)
23.	Yurika Fujii, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Detection of Dangerous Objects By Pan-tilt Camera, Proceedings of The Fifth International Conference on Electronics and Software Science ICESS2019, Japan, 2019, 61-70, Takamatsu, Aug. 2019. (Summary) Security cameras are increasing in public facilities. The main reason is improvement of security. Furthermore, security cameras help quick arrest of criminals. It is, therefore, important that we install security cameras. However, we expect to obtain too much data of movie by increasing the number of cameras too. Security guard cannot watch all movie of all cameras always. For these reasons, we thought not only getting data but also security improvement. If humans have dangerous objects, the movies situation should define them as dangerous. First, we detect humans in camera's images. Second, if we detect dangerous objects in human detection area, we think that there are dangerous humans. We use SSD to detect humans and dangerous objects. (Keyword) SSD / security camera / image processing / Deep learning
24.	Misato Matsushita, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Multiclass Classification and Regression Analysis of Muscle Fatigue Using Wrist EMG, Proceedings of The Fifth International Conference on Electronics and Software Science ICESS2019, Japan, 2019, 90-95, Takamatsu, Aug. 2019. (Summary) Muscles can cause injury by training to improve physical performance. However, there are few ways to assess muscle fatigue currently. Therefore, in this paper, muscle fatigue is evaluated using surface EMG(ElectroMyoGram). The proposed method in this research consists of 4 parts: Measurement, Pre-processing, Feature extraction, and Learning identification parts. The effectiveness of the proposed method is demonstrated in two ways, classification and regression analysis, and comparative verification is conducted. (Keyword) wrist EMG / SVM / CNN / Deep learning / regression analysis / multiclass classification
25.	Kazuki Shimamoto, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : A Method to Evaluate Accumulated Stress Using Nail Image, Proceedings of The Fifth International Conference on Electronics and Software Science ICESS2019, Japan, 2019, 12-17, Takamatsu, Aug. 2019. (Summary) In this paper, we propose a method to evaluate accumulated stress by extraction the height of the lunula of the nail from a nail image. The proposed method consists of three stages: measurement, preprocessing, and stress evaluation. In the measurement, we take a nail image. In the preprocessing, we extract the height of the lunula of the nail. Then, we carry out edge detection using a hue histogram in a rectangle. In the stress evaluation, we evaluate accumulated stress at 0 to 1 using fuzzy reasoning. In order to show the effectiveness of the proposed method, we conducted experiments. These results suggested that the difference between the minimum and maximum values of the height of the lunula while the experiment might be able to determine the presence or absence of accumulated stress. (Keyword) image processing / fuzzy reasoning / stress evaluation / nail image / social system (Tokushima University Institutional Repository) ● Metadata: 113733 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1050292572129037312 (Tokushima University Institutional Repository: 113733, CiNii: 1050292572129037312)
26.	Kazuki Shimamoto, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : A Method to Extract Change of Lunula of the Nail, Proc. of SAMCON 2019, TT9-2-1-TT9-2-4, Chiba, Mar. 2019. (Summary) This paper proposes a method to extract change of lunula of the nail. The proposed method consists of three phases; HSV color system conversion, finger detection, and rectangle search. In the HSV color system conversion, we converts the RGB color system into the HSV color system. Then, color components are divided into each component of Hue, Saturation, Value. In the finger detection, we use the threshold value of Value in HSV for finger detection. We apply the labeling process to the binarized image for saving the fingertip area. In the rectangle search, the change of the Hue histogram in the rectangle is used to extract the lunula and the edge of the nail plate. The rectangle moves upward until it finds an edge. The height of the lunula is calculated using the extracted edge. In order to show the effectiveness of the proposed method, we conducted experiments. (Keyword) Nail image processing / Lunula of the nail / HSV color / Edge detection using histgram / Image segmentation
27.	Hisaki Omae, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : A Method to Detect Presence or Absence of Learning Understanding Using Center Cumulative Frequency Comparison Method and Multistage ICA, Proc. of SAMCON 2019, TT9-4-1-TT9-4-4, Chiba, Mar. 2019. (Summary) This paper proposes a method to detect the presence and absence of learning understanding using center cumulative frequency comparison (CCFC) method and multistage independent component analysis (ICA). The proposed method consists of four stages: electroencephalogram (EEG) measurement, EEG preprocessing, EEG feature extraction and EEG pattern classification. In the EEG measurement, the EEG signals are measured using a simple electroencephalograph. The EEG preprocessing consists of two phases: eye blink artifacts detection and eye blink artifacts removal. The EEG feature extraction consists of two phases: frequency analysis and band division based on the rhythm of brain activities. In the EEG pattern classification, k-nearest neighbor (k-NN) is used to classify EEG patterns on the basis of band division results. In order to show the effectiveness of the proposed method, this paper conducted three cases. The experimental results suggest that split of the discrimination model using the proposed method is relatively effective when detecting the presence and absence of learning understanding using EEG. (Keyword) electroencephalogram / Eye blink artifact / Independent component analysis / Learning understnading
28.	Mana Sasaoka, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : A Method to Estimate Pressing Positions of Guitar String for Guitar Solo Skill Acquisition, Proc. of SAMCON 2019, Chiba, Mar. 2019. (Summary) This paper proposes a method to estimate string pressing positions of the solo part of the lead guitar using image processing technology. The proposed method consists of four phases: image rotation, creation of standard fingerboard image, guitar position detection, and estimation of the string pressing positions. The image rotation method consists of line detection and rotation of the image. The creation of standard fingerboard image consists of detection of frets and intersection detection of frets and strings. In the guitar position detection, the coordinates of the color marker which attach to the bridge and nut are detected. The estimation of the string pressing positions consists of deformation of the standard fingerboard image and detecting finger positions. In order to show the effectiveness of the proposed method, we conduct experiments using videos playing the guitar. The experimental results suggest that the estimation of the string pressing positions in this proposed method is relatively effective. (Keyword) image processing / Hough transform / HSV color / Guitar solo skill / Human support
29.	Shin-ichi Ito : An Electroencephalogram Analysis Method to Detect Answers of Questions Using Machine Learning Techniques, 2019 International Conference for Leading and Young Computer Scientists, Okinawa, Mar. 2019. (Summary) This paper introduces any methods to detect answers of questions based on an electroencephalogram (EEG) analysis techniques. The proposed methods consist of three phases; EEG recordings, EEG feature extraction, answers detection. In the EEG recordings, the simple electroencephalograph is used to record the EEG signals. This device has lots of sensors that are dry type. It is easy to wear the device. In the EEG feature extraction, the frequency analysis technique and convolutional neural networks (CNNs) are employed to calculate the features of the EEG signals. Fast Fourier transform is used to calculate the power spectra of the EEG signals. The CNNs are used to extract the EEG features related to the answers of the questions. In the answers detection, the multi-layer perceptron (MLP), support vector machine (SVM) are employed to classify the answers of questions (yes or no). In order to show the effectiveness of the proposed method, we conduct the experiments using real EEG data sets. This paper has two experiments. In experiment 1, the proposed method classifies the answers of questions on daily conversation. The EEG feature is extracted using the FFT and the CNNs. The answers are classified using the SVM. In the experimental results, we confirm that the recognition accuracy is 90% or more. In experiment 2, the proposed method classified the answers of questions on understanding the contents of the learning material. The EEG feature is extracted using the FFT by selecting the EEG recording positions. The answers are classified using the MLP. In the experimental results, we confirm that the recognition accuracy is 70% or more. Then, best of the recognition accuracy is 90% or more. These results suggest that it is possible to detect the answers of questions using the proposed method. (Keyword) electroencephalogram / answer of questions / machine learning
30.	Shun Yamamoto, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Recognition of Aerial Input Numerals by Leap Motion and CNN, Proc. of 2018 SCIS&ISIS, 1189-1192, Toyama, Dec. 2018. (Summary) As information technology has advanced in recent years, services which include personal authentication systems such as ATM are increasing. Current main personal authentication systems include IC cards, passwords, and biometrics authentication such as fingerprint authentication.However, there are several problems in these systems.Therefore, better systems are needed.As such systems, we propose a method to write numerals inthe air using the Leap motion and to carry out personal authentication from such aerial handwriting data. We try to identify numerals 0 to 9 as a previous stage. After applying some pre-processing to inputs, learning and identification are carried out using CNN which is a method of machine learning. As a result, average identification accuracy rate was 93.4%. From this result, it is suggested that input numerals in the air can be identified and there is a possibility to construct a new personal authentication system. (Keyword) Aerial input / Deep learning / Leap Motion (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85067095983 (Elsevier: Scopus)
31.	Ryohei Shioji, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Personal Authentication and Hand Motion Recognition Based on Wrist EMG Analysis by a Convolutional Neural Network, Proceedings of 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems, 1184-1188, Toyama, Dec. 2018. (Summary) Recent years, biological signals have attractedmuch attention as a tool of human interface. Electromyogram(EMG) has been used in a variety of situations in particular.Generally EMG of muscular volume arms or shoulders has been measured in many cases. In addition, expensive wet typesensors have been often used. However, they are inconvenientand high-cost. On the other hand, in hand motion recognitionand personal authentication using wrist EMG, we haveobtained good results. However, there has been no way toestablish them at the same time. Therefore, in this paper wemeasure EMG by attaching dry type sensors to wrist, andcarry out hand motion recognition and personal authentication.The conventional method on hand motion recognition usedEMG of movement Japanese Janken. The average accuracywas 92.9%. The conventional method on personalauthentication used only "paper" of Japanese Janken. Theaverage accuracy was 96.7%. We used a Convolutional NeuralNetwork (CNN) for learning and identification. In theproposed method, we try to carry out hand motion recognitionand personal authentication at the same time. We use a multiinputand multi-output models of CNN. The average accuracyof hand motion recognition is 94.5%. The average accuracy of personal authentication is 94.57%. (Keyword) electromyogram activity / deep learning / Wrist EMG (Link to Publication Site) ● Publication site (DOI): 10.1109/IOTAIS.2018.8600826 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85067097623 (DOI: 10.1109/IOTAIS.2018.8600826, Elsevier: Scopus)
32.	Ryohei Shioji, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Personal Authentication and Hand Motion Recognition Based on Wrist EMG Analysis by a wide Residual Network, Proc. of 2018 Annual Conference on Engineering and Applied Science, 38-48, Osaka, Nov. 2018. (Summary) Recent years, biological signals have attracted much attention as a tool of human interface. Electromyogram (EMG) has been used in a variety of situations in particular. Generally, EMG in muscular volume of arms or shoulders has been measured in many cases. In addition, expensive wet type sensors have been often used. However, they are inconvenient and high-cost. On the other hand, in hand motion recognition and personal authentication using wrist EMG, we have obtained good results. However, accuracy is poor when hand motion recognition and personal authentication are carried out at the same time. For the above reasons, we carry out hand motion recognition and personal authentication at the same time, and try to obtain higher accuracy than the previous research. The conventional method used EMG of movement Japanese Janken (Fig.1). We use a multi-input and multi-output model of a Convolutional Neural Network (CNN). The average accuracy of hand motion recognition is 94.6%. The average accuracy of personal authentication is 95.0%. In this paper, we use a Wide Residual Network (WRN). The average accuracy of hand motion recognition is 97.8%. The average accuracy of personal authentication is 98.4%. In future work, we aim to improve accuracy by adjusting WRN parameters. In addition, we prepare a class not belonging to any class in multi-class classification. (Keyword) electromyogram activity / Deep learning / Wrist EMG
33.	Shion Morikawa, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Personal Authentication by Lips EMG Using Dry Electrode andCNN, Proc. of 2018 International Conference on Internet of Things and Intelligence System (IOTAIS), 180-183, Denpasar, Nov. 2018. (Summary) As an alternative to voice, sign language and artificial larynx can be used. However, there are disadvantages where they require a long-term training and are expensive. Therefore, researches on detection of utterance by electromyography (EMG) analysis around the lips have beenconducted. On the one hand, it is necessary to construct a personal authentication system to identify speakers. The electrode used in this paper is 2 electrodes sensor, which is small in size and a dry type. Three sensors are attached in the orbicularis muscle, the zygomatic major muscle, and the depressor angle oris muscle which can acquire myoelectric information necessary for identification in Japanese vowelutterance. EMG signals are measured using P-EMG plus. In order to eliminate noises, signal cutting is carried out before and after the central point of the acquired raw data. Furthermore, EMG data are divided to increase the number of data while overlapping. These are named DATA 1. A Hamming window is then applied for them, and the amplitude values of the power spectra are calculated by fast Fouriertransform. Automatic verification and elimination of noise parts by quartile method were carried out. In order to reconstruct signals after noise elimination, the inverse Fourier transform is carried out and then a inverse Hamming window is applied. These are named DATA 2. Learning identification is carried out using a convolutional neuralnetwork. A large difference was found in accuracy dependingon the data set created separately by measurement date. Therefore, it was found that intra-individual variation by each subject was large. In the future, it is necessary to further improve the data and to reduce individual variation within each subject. (Keyword) Lips EMG / Deep learning / Dry Electrode (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85061695767 (Elsevier: Scopus)
34.	Shun Yamamoto, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Authentication of Aerial Input Numerals by Leap Motion and CNN, Proc. of 2018 International Conference on Internet of Things and Intelligence System (IOTAIS), 189-193, Denpasar, Nov. 2018. (Summary) As information technology has advanced in recent years, services which include personal authentication systems such as ATM are increasing. Current main personal authentication systems include IC cards, passwords, and biometrics authentication such as fingerprint authentication. However, there are several problems in these systems.Therefore, better systems are needed. As such systems, we propose a method to write numerals in the air using the Leap motion and to carry out personal authentication from such aerial handwriting data. We try toauthenticate numerals 0 to 9 which are written by three subjects.After applying some pre-processing to inputs, learning and identification are carried out using CNN which is a method of machine learning. As a result, average identification accuracy was 90.3%. From this result, it is suggested that input numerals in the air can be authenticated and there is a possibility to construct a new personal authentication system. (Keyword) Leap Motion / Deep learning / Numeral (Link to Publication Site) ● Publication site (DOI): 10.1109/IOTAIS.2018.8600847 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85061722389 (DOI: 10.1109/IOTAIS.2018.8600847, Elsevier: Scopus)
35.	Ryohei Shioji, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Personal Authentication and Hand Motion Recognition Based on Wrist EMG Analysis by a Convolutional Neural Network, Proc. of 2018 International Conference on Internet of Things and Intelligence System (IOTAIS), 184-188, Denpasar, Nov. 2018. (Summary) Recent years, EMG has attracted much attention as a tool of human interface. In hand motion recognition and personal authentication using wrist EMG, we have obtained good results. However, there has been no way to establish them at the same time. Therefore, in this paper we measure EMG by attaching dry type sensors to wrist, and carry out hand motionrecognition and personal authentication. The conventional method used EMG of movement Japanese Janken. We use a multi-input and multi-output model of a Convolutional Neural Network (CNN). The average accuracy of hand motion recognition is 94.5%. The average accuracy of personalauthentication is 94.6%. In the conventional method, personalauthentication was classified into two classes. However, we carry out multi-class classification in the proposed method. In feature extraction, we obtain 128×8 input data from the measuring unit. Then, a filter size of the convolution layers is 3×3. CNN does not contain pooling layers in this paper. In the proposed method, the average accuracy of hand motion recognition is 94.6%. The average accuracy of personal authentication is 95.0%. (Keyword) electromyogram activity / Deep learning / Wrist EMG (Link to Publication Site) ● Publication site (DOI): 10.1109/IOTAIS.2018.8600826 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85061735879 (DOI: 10.1109/IOTAIS.2018.8600826, Elsevier: Scopus)
36.	Kazuki Shimamoto, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : A Method to Extract a Nail Half Moon for Accumulation Stress Evaluation, Proceedings of the 6th IIAE International Conference on Intelligent Systems and Image Processing 2018, 289-292, Matsue, Sep. 2018. (Summary) In this paper, we propose a method to extract a nail half moon for accumulation stress evaluation. The proposed method consists of three phases; nail half moon area extraction, HSV color system conversion and nail half moon outline detection. Trimming of the nail half moon area is carried out with fixed parameters because the shooting environment of the nail is fixed. In the HSV color system conversion, the value of hue is used to binarize the nail half moon part and the nail plate part. The outline of the nail half moon is detected using labeling a region that has the maximum value in the nail image. In order to show the effectiveness of the proposed method, we conduct experiments. (Keyword) electroencephalogram / nail image procession / accumulation stress / HSV color system (Link to Publication Site) ● Publication site (DOI): 10.12792/icisip2018.054 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.12792/icisip2018.054 (DOI: 10.12792/icisip2018.054)
37.	Hisaki Omae, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : A Method to Check whether Human Understands Contents of Learning Using Electroencephalogram, Proceedings of the 6th IIAE International Conference on Intelligent Systems and Image Processing 2018, 283-288, Matsue, Sep. 2018. (Summary) This paper proposes a method to check whether human understands contents of learning using electroencephalogram (EEG). The proposed method consists of three stages; EEG measurement, EEG feature extraction and EEG pattern classification. The EEG feature extraction method (stage) consists of three phases; frequency analysis, band division based on rhythm of brain activities and principal component extraction. The frequency analysis converts an original waveform of EEG into frequency components (bands). In the band division, frequency components were divided into five rhythms ( , Low-, High- , Low- and High- waves). In the principal component extraction, we calculate the first and the second principal components using the principal component analysis (PCA). In the EEG pattern classification, support vector machine (SVM) is used to classify EEG patterns based on PCA results. In order to show the effectiveness of the proposed method, we conduct experiments using real EEG data. The experimental results suggest that the most important sensing position to record EEG is F7 when checking whether he/she understands contents of learning using EEG. (Keyword) electroencephalogram / understanding / electroencephalogram / support vector machine
38.	Shin-ichi Ito, Momoyo Ito, Shoichiro Fujisawa and Minoru Fukumi : An Electroencephalogram Analysis Method to Classify Answers of Questions Using Deep Neural Networks, International Conference on Information Technology and Computer Science, 16, Toronto, Aug. 2018. (Summary) This paper proposes a method to classify answers of questions during human communications based on an electroencephalogram (EEG) analysis technique. The proposed method consists of three phases; EEG measurement, EEG feature extraction and answers classification. In the EEG measurement, this paper employs the simple electroencephalograph. The EEG signals of frontal lobe are recorded. The EEG features are calculated by normalizing the EEG signals and using deep neural networks. The answers of questions classify by analysing the EEG features using support vector machine. In order to show the effectiveness of the proposed method, we conducted experiments using real EEG data. In the experimental results, we confirm that mean of the recognition accuracy was 93.5%. These results suggest that the answers of questions during human communications can be classified using the EEG signals and their analysis, and that the EEG analysis technique using the deep neural networks and the support vector machine is suitable for extracting the EEG features and classifying the EEG features. (Keyword) electroencephalogram / answer of questions / deep neural networks / support vector machine
39.	Shoichiro Fujisawa, Okada Tomohiro, Kihara Taiki, Nagahama Hideaki, Inagaki Tomoyuki, Ikeda Norihiro, Takahashi Kazuya, Takeuchi Kiyohito, Ogino Hiroshi, Shin-ichi Ito, Katsuya SATO and Osamu Sueda : Verification of blinking LED block installed at crosswalk entrance for low-vision people, Proceedings of the AHFE 2018 International Conference on Human Factors in Transportion, 615-622, Orlando, Jul. 2018. (Summary) There are individual differences in the degree of disorder and appearance of low vision people, but in many cases discrimination of light and darkness is possible. Therefore, it is considered effective to use flashing light as support for low vision people. As support for such low vision, research is being conducted in this laboratory to introduce a light emitting blinking block at the entrance to the pedestrian crossing. In this study, as an introduction study of the development of luminescent block for support to amblyopia, we asked subjects to evaluate the intensity of blinking stimulus when presenting blinking light to subjects and the goodness of visibility by sensory evaluation, Consider the blinking method best suited for support. (Keyword) LED block / Blind person / Crosswalk / Orientation / Veering distance / Tactile walking surface indicators (Link to Publication Site) ● Publication site (DOI): 10.1007/978-3-319-93885-1_56 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85049689281 (DOI: 10.1007/978-3-319-93885-1_56, Elsevier: Scopus)
40.	Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : An Electroencephalogram Analysis Method to Detect Preference Using Gray Association Degree, The proceedings of International Conference on Electronics, Information, and Communication 2018, 304-305, Honolulu, Jan. 2018. (Summary) This paper introduces an electroencephalogram (EEG) analysis method to detect human preference. The proposed method consists of three phases; EEG recording, EEG feature extraction and preference detection. In EEG recording, we employ the simple electroencephalograph. The measurement position to record the EEG is left frontal lobe (FP1). The gray association degree is used to extract the EEG feature. The support vector machine is used to detect human preference on sounds listened to. In order to show the effectiveness of the proposed method, we conduct the experiments. In the experimental results, the mean of the accuracy rate of the favorite sound detection was higher than 88%. (Keyword) electroencephalogram / preference / gray association degree / support vector machine (Link to Publication Site) ● Publication site (DOI): 10.23919/ELINFOCOM.2018.8330622 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85048548091 (DOI: 10.23919/ELINFOCOM.2018.8330622, Elsevier: Scopus)
41.	Shoichiro Fujisawa, Ranmaru Mandai, Ryota Kurozumi, Shin-ichi Ito and Katsuya SATO : Identification of Visually Impaired Person with Deep Learning, Proceedings of the 1st International Conference on Intelligent Human Systems Integration (iHSI 2018), 601-606, Dubai, Jan. 2018. (Summary) The purpose of this study is to identify visually impaired persons by analyzing still pictures of walking of a visually impaired person and that of a healthy person using deep learning. Still images of walking are taking still pictures from video images. Shoot from sideways and diagonally with two video cameras. The number of images (with 1000 or 2000) and the dropout (three, two, or one time) was changed and analyzed. Because the study focused on only visually impaired persons (totally blind persons) and the healthy person's study machines of two patterns in the experiment, a correct answer rate of 99.9% for every 2000 images and 2 times of the dropout number was obtained. (Keyword) deep learning / Deep convolutional neural networks / Visually impaired person / Blind person (Link to Publication Site) ● Publication site (DOI): 10.1007/978-3-319-73888-8_93 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1007/978-3-319-73888-8_93 (DOI: 10.1007/978-3-319-73888-8_93)
42.	Takashi Higasa, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : A Method to Specify a Region of Character String in Augmented Reality, Proc. of the 5th IIAE International Conference on Intelligent Systems and Image Processing 2017, 25-30, Honolulu, Sep. 2017. (Summary) This paper proposes a method to input characters and/or character string in an augmented reality using gesture motion. The proposed method detects the region of character string using gesture motion. It consists of five phases; template generation, skin detection, hand region detection, gesture motion extraction and designation of character string region. The template image consists of two fingers because a gesture is to take hold the tips of the first and second fingers. In the skin detection, we extract the skin color on the basis of values in saturation by using threshold processing. The hand region is detected by calculating areas and detecting the area with the maximum value as a hand. The gesture motion is extracted using template matching. In order to show the effectiveness of the proposed method, we conduct experiments. (Keyword) Augmented Reality
43.	Kohei Nakanishi, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Biometrics Authentication of Aerial Handwritten Signature Using a Convolutional Neural Network, Proc. of the 5th IIAE International Conference on Intelligent Systems and Image Processing 2017, 19-24, Honolulu, Sep. 2017. (Summary) Recent years, biometrics authentication is receivingattention by development of information society. In thispaper, we propose a personal authentication system, whichuses behavior characteristics among biometrics. We focuson aerial handwritten signature, because it is difficult toforge it, and there is no risk of loss. In this paper, wemeasure signatures using Leap Motion Controller. It canmeasure three dimensional space coordinates with highaccuracy. We divide signature data into three axialdirections of coordinates XYZ in order to use them asone-dimensional data. We carry out preprocessing tosignature data and normalize them. Next, we use deeplearning based on a convolutional neural network forfeature extraction and identification. In this experiment, weprepare data obtained from six subjects. We obtain genuinedata of one subject. The remaining five subjects are used tocreate forgery data. We classify signature data into twoclasses. We conduct deep learning in which convolutionalneural network carries out 10,000 cycles learning in onetrial. We carry out this trial 5 times and evaluate meanaccuracy by cross validation for two types of genuine data.The average discrimination accuracy of this experiment are97.0 % and 95.9%. In addition, the false rejection rates are9.6% and 19.2%. The false acceptance rate are 0.8% and0.1%. (Keyword) neural network / Deep learning
44.	Ryohei Shioji, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Personal Authentication Based on Wrist EMG Analysis by a Convolutional Neural Network, Proc. of the 5th IIAE International Conference on Intelligent Systems and Image Processing 2017, 12-18, Honolulu, Sep. 2017. (Summary) Recent years, biological signals have attracted muchattention as a tool of human interface. Electromyogram(EMG) has been used in a variety of situations in particular.We measure EMG of arms or shoulders in many cases. Inaddition, we often use expensive wet type sensors. However,they are inconvenient and high-cost. On the one hand, therehave been few works of personal authentication using EMG.Therefore, in this paper we measure EMG by attaching drytype sensors to wrist, and carry out personal authentication.The conventional method in this paper is divided into threeunits such as a measuring, a feature extraction, and adiscrimination units. We measure EMG signals with eightdry type sensors on the wrist. After that, we identify amotion opening our hands. We use a convolutional neuralnetwork (CNN) to learning and authentication. Wecollected 40 data for each subject. The average accuracy oftwo-class separation was 94.9 % by CNN. In addition to theconventional method, the proposed method in this paperpreprocesses the data. Large noise was removed using ahigh path filter. By this preprocessing, identificationaccuracy (Two-class classification using CNN) improvedby 1.5%. The true acceptance rate improved by 7.2%, andthe false acceptance rate improved by 0.0067%. (Keyword) electromyogram activity / Deep learning
45.	Ryousuke Takabatake, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Japanese Vowels Recognition Using Linear Discriminant Analysis and Surface Electromyogram Measured with Bipolar Dry Type Sensors, Proc. of the 5th IIAE International Conference on Intelligent Systems and Image Processing 2017, 5-11, Honolulu, Sep. 2017. (Summary) This paper proposes a Japanese vowels recognition method using surface electromyogram (EMG). First, 3 sensors are used to measure surface EMG data at orbicularis oris muscle, zygomatic muscle and depressor angle oris muscle. Next, Fast Fourier Transform (FFT) is applied to all measurement data to calculate power spectra. Linear Discriminant Analysis (LDA) is then used for power spectra of 3 channels and reduce their dimension to 4. Finally, theresult of LDA is recognized by Support Vector Machine (SVM). In experiments, it is assumed that mounting sensors to face, measuring EMG, and demounting them are 1 trial. A subject utters 5 Japanese vowels 3 times. Among 3 trials data, 2 trials data are used to make templates and the remaining are used for test. The subject is a man in twenties. As a result, we obtained 62.3% average recognition accuracy. This result shows the proposed method is better about 2 times than the previous method. (Keyword) electromyogram activity / SVM
46.	Okada Tomohiro, Miyamoto Takashi, Shin-ichi Ito, Katsuya SATO, Ikeda Norihiro, Osamu Sueda and Shoichiro Fujisawa : Research on Blinking-Luminescence Travel Support for Visually Impaired Persons, Proceedings of the AHFE 2017 International Conference on Human Factors in Transportion, 319-324, Los Angeles, Jul. 2017. (Summary) Of the five human senses, vision is said to account for 80% of the in-formation acquired. The presentation of light is an effective way to convey visual information. A blinking presentation especially attracts attention. A low-vision person acquires peripheral information through residual vision. In this case, light effectively supports the travel of low-vision persons. A blinking light stimulates more than a continuous light. However, a blinking light is not sufficiently compatible with the surrounding environment. This research investigates an environmentally friendly blinking pattern to arouse attention. (Keyword) Low-vision / Visual information / coupled systems / Travel support / Sensory test / Brain waves (Link to Publication Site) ● Publication site (DOI): 10.1007/978-3-319-60441-1_32 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1007/978-3-319-60441-1_32 (DOI: 10.1007/978-3-319-60441-1_32)
47.	Tomoyuki Inagaki, Hideaki Nagahama, Norihiro Ikeda, Kazuya Takahashi, Kiyohito Takeuchi, Hiroshi Ogino, Katsuya SATO, Shin-ichi Ito and Shoichiro Fujisawa : Verification of Installed Position of LED Block Equipped with Projections to Indicate Travel Direction, Proceedings of the AHFE 2017 International Conference on Human Factors in Transportion, 817-824, Los Angeles, Jul. 2017. (Summary) A crosswalk entrance is on the boundary of a roadway and is a dangerous area for visually impaired persons. Tactile walking surface indicators (TWSIs) are installed on the road to support independent travel for blind and low-vision individuals. We developed an LED block equipped with projections to strengthen support at the crosswalk entrance. Blind individuals can sense this block through the soles of their shoes. Results indicated that the LED block equipped with projections to locate travel direction effectively supports crossing. The effectiveness of this block is determined using measurement and questionnaire results. (Keyword) LED block / Blind person / Crosswalk / Orientation / Veering distance / Tactile walking surface indicators (Link to Publication Site) ● Publication site (DOI): 10.1007/978-3-319-60441-1_78 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1007/978-3-319-60441-1_78 (DOI: 10.1007/978-3-319-60441-1_78)
48.	Ryohei Shioji, Daiki Hiraoka, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Personal Authentication Based on Wrist EMG Analysis by a Convolutional Neural Network, Proceeding of International Conference on Advanced Technology & Sciences (ICAT'Rome), 335-340, Rome, Nov. 2016. (Summary) In this paper we measure EMG by attaching dry type sensors to wrist, and carry out personal authentication. The proposed method in this paper is divided into three units such as a measuring, a feature extraction, a discrimination units. We measure EMG signals with eight dry sensors on the wrist. After that, we identify a motion opening our hands. We use a convolutional neural network (CNN) to learning and authentication. In addition, we try to use a multilayer perceptron for comparison. Experiments are conducted in two patterns. At first, we carry out two-class classification (the subject and the others). The second is multi-class classification in which the number of subjects is 8 people. We collected 40 data for each subject. The average accuracy of two-class classification was 89.4 % by the multilayer perceptron. That was 94.9 % by CNN. On the other hand, the average accuracy of multi-class classification was 41.2 % by the multilayer perceptron. That was 70.3 % by CNN. In future work, we will improve classification accuracy for two-class and multi-class classification. Futhermore, we try to identify multiple motions.
49.	Kohei Nakanishi, Daiki HIraoka, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Biometric Authentication of Aerial Handwritten Signature Using a Convolutional Neural Network, Proceeding of International Conference on Advanced Technology & Sciences (ICAT'Rome), 329-334, Rome, Nov. 2016. (Summary) In this paper, we propose a personal authentication system which uses behavioral characteristics among biometrics. We focus on aerial handwritten signature, because it is difficult to forge it, and there is no risk of loss. In this paper, we measured signatures using Leap Motion Controller. It can measure three dimensional space coordinates with high accuracy. We divide signature data into three axial directions of coordinates XYZ in order to use them as one dimensional data. We carry out preprocessing to signature data and normalize them. Next, we use deep learning based on a convolutional neural network for feature extraction and identification. Generally, it is necessary to learn a large number of data for deep learning. However, it is difficult to gather many learning data of forgery. Therefore, we transform learning data of forgery at the preprocessing, which generates many pseudo learning data of forgery. In this experiment, we prepare genuine data of nine persons and forgery data of each person. We classify signature data into ten classes. We conduct deep learning in which CNN carries out 10,000 cycles learning in one trial. We conduct this trial 1,000 times. The average discrimination accuracy of this experiment is 98.1 %. This result is better in accuracy than a related research. From the above, we think that the proposed method is useful for the aerial handwritten signature.
50.	Daiki Hiraoka, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Japanese Janken Recognition Based on Wrist EMG Analysis by CNN and SVM, Proceeding of International Conference on Advanced Technology & Sciences (ICAT'Rome), 323-328, Rome, Nov. 2016. (Summary) In this paper, we propose a method which can discriminate hand motions. We measure an electromyogram of wrist by using 8 dry type sensors. We focus on four motions, such as ``Rock-Scissors-Paper'' and ``Neutral''. ``Neutral'' is a state that does not do anything. The proposed method extracts features of EMG by a convolutional neural network (CNN) and discriminate the motions by a support vector machine (SVM). In the CNN, we reduced the full connection layer by adding a convolution layer which has the same size filter of a feature map. CNN has an input layer, 6 convolutional layers, a pooling layer and a full connection layer. We conducted experiments with seven subjects. An average discrimination accuracy of the proposed method was 92.2 %. In the previous method, the discrimination accuracy rate was 76.9%. Therefore, the proposed method is better in accuracy than the previous method. In future work, we will discriminate more detailed hand motions.
51.	Nagahama Hideaki, Inagaki Tomoyuki, Ikeda Norihiro, Takahashi Kazuya, Takeuchi Kiyohito, Ogino Hiroshi, Katsuya SATO, Shin-ichi Ito and Shoichiro Fujisawa : Proof Experiment of LED Block Equipped with Projections to Locate Travel Direction for Blind and Vision Impaired Persons, Proceedings of the AHFE2016 International Conference on Human Factors in Transportation, 159-169, Orlando, Jul. 2016. (Summary) The eyesight of a person with low visual capacity ("LV") might decrease remarkably under low-light conditions such as the morning, evening, and nighttime, decreasing their opportunities to go out. Crosswalk entrances are on the boundary with the roadway and are among the dangerous areas for visually impaired persons. An LV person cannot easily find the entrance of a crosswalk at nighttime. LV persons can detect the developed LED blocks with their residual vision. Moreover, for the blind person to cross the crosswalk safely, it is very important to acquire the direction at the soles of the shoes. The authors have verified the effectiveness of the developed block for visually impaired person by laboratory experiments. In this experiment, both visually impaired persons and blind persons found the developed block to effectively support crossing. Moreover, the time required for locating with the LED block was short . Orientation using the LED block required less time. The LED block was observed to have an orientation accuracy equivalent to that of TWSIs. Moreover, subjects expressed opinions such as "It was easy to locate" and "There is a sense of security when going straight" in interviews after the experiment. Installing LED blocks at crosswalk entrances was found to be useful for guiding visually impaired persons. Moreover, the shape of the projection indicating the direction is easily verified through the soles of the blind person's shoes. A LED block experiment was conducted to determine its actual efficiency. In this research, the visibility of the LED block was verified for different luminescence and illuminance environments. This result clarified how to provide illumination for a visually impaired person. Based on this research, the authors developed a LED block with projections to locate travel direction for a visually impaired person. The developed block can be detected with the LVs residual vision. Blind persons can also detect this block through the soles of their shoes. This block was constructed at the entrance to a crosswalk of an actual intersection, and a proof experiment was conducted. Twenty persons with weak sight and 20 totally blind people verified the effectiveness of this block. Especially, the effectiveness of the experiment on the LED block by the LVs can be verified by doing after the sunset. This experiment measured the visibility of the LED block at the pavement side and the opposite side. In the experiments with blind persons, the time to acquire the direction of the crossing from the direction location block and the veering distance at the crossing was measured to verify its effectiveness. Moreover, low-vision persons and totally blind persons answered a questionnaire concerning the effectiveness of the block. The effectiveness of this block is described from the measurement data and the questionnaire results. (Link to Publication Site) ● Publication site (DOI): 10.1007/978-3-319-41682-3_14 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1007/978-3-319-41682-3_14 (DOI: 10.1007/978-3-319-41682-3_14)
52.	Takahashi Shinji, Ishibashi Tatsuki, Katsuya SATO, Shin-ichi Ito and Shoichiro Fujisawa : Quantitative Evaluation of Orientation Performance of Tactile Walking Surface Indicators for the Blind, Proceedings of the AHFE2016 International Conference on Human Factors in Transportation, 151-158, Orlando, Jul. 2016. (Summary) Tactile walking surface indicators (TWSIs) were invented in Japan in 1965. They are now used around the world to help visually impaired persons. There are two types of TWSIs, attention patterns and guiding patterns. TWSIs are perceived through use of a long white cane or through the soles of the shoes for the blind. The TWSI design of the attention pattern only calls attention to a hazard or to hazards and decision points. This attention pattern is usually constructed at the crosswalk entrance. The direction of the crossing can be acquired by the row of the projection of the attention pattern through the soles of the shoes. TWSIs must have high detection sensitivity and a high ability to distinguish objects as well as excellent direction detection. An international standard for TWSIs was enacted in 2012. However, the international standard organization (ISO) allows a wide-ranging size. The background that allowed the size with a wide-ranging ISO standard was in the adoption of various sizes by each country. For instance, the top diameter of attention patterns was specified as 12 to 25mm. The spacing was specified as 42 to 70mm. In addition, truncated domes or cones of the attention pattern were arranged in a square grid, parallel or diagonal at 45°to the principal direction of travel. This diagonal will not indicate the direction of the crossing. In this research, the performance of indicating the direction was compared at the same intervals for five diameters specified by the international standard. To secure omnidirectional performance, truncated domes were arranged on the disc. The subject enters the disc from a random direction and determines the direction of the row through the soles of their shoes. Five discs with different diameters were selected at random. The spacing of the truncated domes was assumed to be 60mm as adopted by the national standard of Japan (JIS). The experiment was performed by 20 people. For the five kinds of TWSLs, the direction was determined as the direction of the row at the rate from 43 to 54%. Other angles were all less than 7% at a resolution of 5°. The row of 45° forms the row at the spacing of the square root of two times. It was a result similar as for this row. It has been understood to have the orientation performance all five kinds of attention pattern with a different diameter. The attention pattern of JIS was the highest value. Though this experiment does not verify the orientation performance of the difference in spacing, the attention pattern of the diagonal means the performance is not used. The arrangement of JIS is square grid only. In this research, the orientation performance is quantitatively compared by the difference of the diameter. The purpose of this research is to acquire data as a scientific basis for improving the ISO standard. (Link to Publication Site) ● Publication site (DOI): 10.1007/978-3-319-41682-3_13 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1007/978-3-319-41682-3_13 (DOI: 10.1007/978-3-319-41682-3_13)
53.	Taiki Nonoguchi, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : A Method to Detect and Track Mosquitoes Using Orientation Code Matching and Particle Filter, Proceeding of the 2016 International Conference on Electrical Engineering, ID90204-1-6, Naha, Jul. 2016. (Summary) In this paper, we propose a method to detect and track mosquito by using orientation code, labeling, multi-templates matching and particle filter. The mosquito detection method employs an edge detection technique based on orientation code, a labeling technique to detect candidate areas of mosquitoes and multi-templates matching to detect mosquitoes. The particle filter is used to track the mosquitoes. The likelihood in the particle filter is calculated on the basis of the results of the multi-templates matching. In order to show the effectiveness of the proposed method, we conduct experiments using real image data. From experimental results, detection and tracking accuracies were 83.9% and 70.6%, respectively.
54.	Shunsuke Takata, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : A Basic Study for Driver State Estimation Based on Time Series Data Analysis, Proceeding of the 2016 International Conference on Electrical Engineering, ID90096-1-6, Naha, Jul. 2016.
55.	Ryosuke Takabatake, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Vowel sound recognition using electromyogram with dry sensors, Proceeding of the 2016 International Conference on Electrical Engineering, ID90049-1-5, Naha, Jul. 2016. (Summary) This paper proposes a vowels recognition method using electromyogram (EMG). 3 sensors are used to measure EMG data at orbicularis oris muscle, zygomatic muscle and depressor angle oris muscle. Fast Fourier Transform (FFT) is applied to all measurement data and then principal component analysis (PCA) is used for phase spectra and power spectra of 3 channels.Finally, the result of PCA is recognized by k-nearest neighbor. In experiments, it is assumed that mounting sensors to face,measuring EMG, and demounting them are 1 trial. A subject utters 5 Japanese vowels 3 times. Among 3 trials data, 2 trialsdata are used to make templates and the remaining are used for test. The subject is a man in twenties. As a result, we obtained33% average recognition accuracy. As future tasks, the authors think it is necessary to change methods of decision of datarange, feature extraction, recognition and so on.
56.	Takuma Ogawa, Daiki Hiraoka, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Improvement in Detection of Abandoned Object by Pan-tilt Camera, Proceeding of the 2016 8th International COnference on Knowlodge and Smart Technology, 152-157, Chiang Mai, Feb. 2016. (Summary) We propose a method which detects abandoned objects on online by using pan-tilt camera. Above all, we improve problems of the previous method which is based on ST-Patch features and human detection. We make extended ST-Patch features for solving the problem of ST-Patch features. We improve human detection by using deep learning which is based on a convolutional neural network. We conducted preliminary experiments to verify a method of pooling, and then we decided to use Max pooling because its detection accuracy is better than that of Ave booling. We conducted experiments in five situations to verify usefulness of the proposed method. If the proposed method finds an abandoned object, it saves the object image. We define the abandoned object as an object which human does not subsist near. We could detect the abandoned object in each situation. However, we conducted experiments of the proposed method only in a room. We need to conduct experiments in a wide area to find new problem. (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84966644089 (Elsevier: Scopus)
57.	Daiki Hiraoka, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Japanese Janken Recognition by Support Vector Machine Based on Electromyogram of Wrist, Proceeding of the 2016 8th International COnference on Knowlodge and Smart Technology, 114-119, Chiang Mai, Feb. 2016. (Summary) In this study, we propose a method which can discriminate hand motions. We measured an electromyogram of wrist by using 8 dry type sensors. We focused on four motions, such as ``Rock-Scissors-Paper'' and ``Neutral''. ``Neutral'' is a state that does not do anything. In the proposed method, we apply fast Fourier transformation (FFT) to measured EMG data, and then remove hum noise. Next, we combine values of sensors based on a gaussian function. In this gaussian function, variance and mean are 0.2 and 0, respectively. After that, we apply normalization by linear transformation to the values. Subsequently, we resize the values into range from -1 to 1. Finally, support vector machine (SVM) conducts learning and discrimination. We conducted experiments in three subjects. Discrimination accuracy of the proposed method for three subjects was 96.9%, 95.3%, 92.2%, respectively. Therefore, we think that the gaussian function is robust to difference of sensor position because this function combines both adjacent channels. (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84966540059 (Elsevier: Scopus)
58.	Shu Tamura, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Method to Evaluate Similarity of Music by Music Features, 42st Annual Conference of the IEEE Industrial Electronics Society (IECON 2015), 2574-2577, Yokohama, Nov. 2015. (Summary) This paper proposes a method to evaluate similarity of music by music features. A music feature extraction method consists of three phases; chord progression pattern detection, rhythm pattern detection and musical instrument information extraction. The music feature extraction is carried out by using frequency analysis. In the chord progression pattern, we employ three evaluation criteria. In the rhythm pattern detection, we evaluate beat per minutes(BPM) values. In the musical instrument information, we confirm results of musical instrument informationextraction visually. In order to show the effectiveness of the proposed method, we conduct computer simulations of music features extraction. (Keyword) music / chord progression / rhythm pattern (Link to Publication Site) ● Publication site (DOI): 10.1109/IECON.2015.7392490 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84973115805 (DOI: 10.1109/IECON.2015.7392490, Elsevier: Scopus)
59.	Daiki Hiraoka, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Hand Motion Discrimination by Support Vector Machine Based on Electromyography of Wrist, Proceeding of the 2015 International Conference on Engineering and Applied Science, 358-366, Sapporo, Jul. 2015. (Summary) In this study, we propose a method which can discriminate hand motions. We measured an electromyography of wrist by using 8 dry type sensord. We focus on four motions, such as "Rock-Scissors-paper" and "neutral". "neutral" is a state that does not do anything. In the proposed method, we apply fast Fourier transformation (FFT) to the measured EMG data, and then remove hum noise which are caused by AC power source. After that, we apply normalization by linear transformation to FFT spectra.. Finally, SVM learns 4096 data which are all data of 8 channels. an experiment of this study has a specific flow. Star of the experiment is to attach sensors, and the end of experiment is to detach the sensors. We regard this flow as 1 trial. In each trial, a subject conducted each motion 10 times. After the experiment, data of each motion are picked out from measured data, and then these data were used for learning and discrimination. Discrimination accuracy was 98.4%. However the subject of experiment was 1 person. Therefore, in future work we need to increase the number of subjects to validate versatility proposed method. In addition, our proposed method is offline now. Therefore, we will implement this method on online.
60.	Takahide Funabashi, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : On-line Recognition of Finger Motions Using Wrist EMG and Simple-PCA, Proc. of Asian Control Conference 2015 (ASCC'2015),, 2182-2186, Kota Kinabalu, Jun. 2015. (Summary) This paper presents an on-line recognition system of finger motions using wrist EMG (electromyogram) measured by dry-type electrodes attached to wrist and Simple-PCA. The Simple-PCA is an approximated version of principal component analysis and is very fast for eigenvector learning. Target behaviors to be recognized in this paper are four finger motions, which are the Janken (Rock-paper-scissors game) ``rock'', ``scissors'', ``paper'' and ``neutral (non-action)''. We tried to reduce an execution time by using the Simple-PCA in training and recognition, in the viewpoint of implementation of interface which can be utilized in daily life. The computational results show that the present on-line system can achieve high recognition accuracy similar to the conventional system using a neural network classifier. (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84957654675 (Elsevier: Scopus)
61.	Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Method to Detect Impression Evaluation Patterns on Music Listened to Using EEG Analysis Technique, The 10th Asian Control Conference (ASCC 2015), 1848-1853, Kota Kinabalu, May 2015. (Summary) In this paper, we propose a method to detect impression evaluation patterns on music listened to using electroencephalogram (EEG) analysis method considering human personality. The proposed method consists of four phases; EEG recordings and EEG feature extraction, personality quantification, feature vector creation to detect the impression evaluation patterns, and impression evaluation patterns detection. The EEG feature is extracted by calculating the time average of the power spectrum of each frequency band at 1 Hz intervals of the EEG. Egogram, Yatabe-Guilford personality inventory and Kretschmer type personality inventory are using for quantifying his/her character. The feature vector to detect the impression evaluation patterns is created by the EEG feature and the results of his/her character quantification. We regard the matching patterns between music and his/her mood as the impression patterns on music listened to. In order to show the effectiveness of the proposed method, we conduct experiments using real EEG data. (Keyword) impression evaluation pattern / music / electroencephalogram / psychological testing / egogram / Yatabe-Guilford personality inventory / Kretschmer type personality inventory (Link to Publication Site) ● Publication site (DOI): 10.1109/ASCC.2015.7244660 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84957705835 (DOI: 10.1109/ASCC.2015.7244660, Elsevier: Scopus)
62.	Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Feeling Evaluation Detection for Auto-skip Music using EEG Analysis Technique, International workshop on Sensing, Actuation, Motion Control, and Optimization (SAMCON2015), Vol.IS3-2-3, 1-4, Nagoya, Mar. 2015. (Summary) This paper proposes a method to detect feeling evaluation patterns on music listened to using electroencephalogram (EEG) analysis method considering human personality. The proposed method consists of four phases; EEG recordings and EEG feature extraction, personality quantification, feature vector creation to detect the feeling evaluation patterns, and feeling evaluation patterns detection. The EEG feature is extracted by calculating the time average of the power spectrum of each frequency band at 1 Hz intervals of the EEG. Egogram, Yatabe-Guilford personality inventory and Kretschmer type personality inventory are using for quantifying his/her personality. The feature vector to detect the feeling evaluation patterns is created by the EEG feature and the results of his/her personality quantification. We regard inclination patterns, which are listening, not listening to the music and feeling borderline case, respectively, as the feeling patterns. In order to show the effectiveness of the proposed method, we conduct experiments using real EEG data. (Keyword) decision making / pattern recognition / music / electroencephalogram / individual difference / personality / egogram / YG / KT
63.	Nao Tsuzuki, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : A Method to Detect Uncomfortable Feeling of Listeners by Biological Information, Proceeding of the 2015 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP), 481-484, Kuala Lumpur, Mar. 2015. (Summary) In this paper, we propose a method to detect uncomfortable feeling of listeners using biological information analysis techniques. The proposed method supports to hold a conversation for smooth communication. We employ the electroencephalogram (EEG) analysis as the biological information analysis. Independent component analysis (ICA) and fast Fourier transform (FFT) are used to detect specific signals of EEG related to uncomfortable feeling and reduce a noise. In order to show the effectiveness of the proposed method, we conduct experiments using real EEG.
64.	Takuma Ogawa, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Detection of Abandoned Object by Pan-Tilt Camera, Proceeding of the 2015 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP), 278-281, Kuala Lumpur, Mar. 2015. (Summary) In this paper, we propose a method of detection for aban- doned objects by a pan-tilt camera. The final purpose is to do online detection of abandoned objects by using the pan-tilt camera. First, we detects object domains by using ST-Patch features from an obtained moving image. We use these fea- tures to efficiently separate moving objects and background. Next, we focus on the object domains. We obtain a picture that was optically expanded by using zoom function. As a re- sult, we can take a clearer picture. After that, we detect these object domains as human or nonhuman by using HOG fea- tures and Real AdaBoost. The HOG features are converted into one-dimensional histogram corresponding to feature val- ues. Detection of human is carried out by Real AdaBoost using the histogram values. The other objects besides human are regarded as abandoned objects in this paper.
65.	Koji Miyai, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Fundamental study for driving scene classification using Bag of Keypoints, Proceeding of the 2015 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP), 342-345, Kuala Lumpur, Mar. 2015. (Summary) In this study, we define the scene in front of the vehicle as driving scene and aim at the classification of the driving scenes. Bag of Keypoints (BoK) is a technique often used in image classification. BoK's effectiveness has been shown in the field of object recognition. Then, we have performed classification experiments for driving simulator images by the BoK. We examine the significance of applying the BoK for driving scene classification.
66.	Taito Mori, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Analysis of Driver's Eye-gaze Movements at Near-miss Events, Proceeding of the 2015 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP), 330-333, Kuala Lumpur, Mar. 2015. (Summary) In this paper, we analyze the relationship between near-miss events and saliency map. To analyze eye-gaze points of the driver for near-miss events which occur in the intersections, we use saliency map which models the human attention mechanism. We use an eye tracking system (faceLAB) to obtain eye-gaze data. We make target intersection time-series driving scene and made saliency map of the scene. Moreover, the saliency map is divided into three levels and we investigate how bicycle and background have saliency in the driving scene. Experimental result shows that the bicycle's saliency is expressed in the middle and high level maps. Next, we carry out experiments using faceLAB data in order to investigate driver's eye-gaze. From the experimental results, subject gaze to similar area when near-miss event did not exist. However, subjects tracked the bicycle when near-miss event occurred, and gazed carefully to the left or right in the intersection after near-miss event.
67.	Takako Ikuno, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Object Search by Template Matching using Genetic Algorithm and Random Search, Proceeding of the 2015 Joint Conference of the International Workshop on Advanced Image Technology (IWAIT) and the International Forum on Medical Imaging in Asia (IFMIA), No.OS.25, 1-4, Tainan, Jan. 2015. (Summary) In this paper, we propose a method in which pictures of security cameras are administered automatically. The administered target is lost property. In case of searching objects with security camera, there are infinitely various sizes and orientations of the object to be searched. Therefore we propose an object search method which is adapted to transformation of the object. We use a template matching using Genetic Algorithm (GA) for detection of lost property. Moreover GA is not necessarily suitable for local search problems. Therefore the local search technique using random search is included to improve GA property. Object search in our proposed method is divided into two parts, global and local searches. According to experimental results, in the global search, search accuracy is relatively good at experiments other than the specific images. However the local search fell short of our expectations. In the future, we need to improve the local search.
68.	Takako Ikuno, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Lost Property Detection by Genetic Algorithm with Local Search, Proceeding of The 2nd International Conference on Intelligent Systems and Image Processing 2014, 245-249, Kitakyushu, Sep. 2014. (Summary) In this paper, we propose a method in which pictures of security cameras are administered automatically. The administered target is lost property. In case of searching objects with security camera, there are infinitely various sizes and orientations of the object to be searched. Therefore, we propose an object search method which is adapted to transformation of the object. We use a template matching using Genetic Algorithm (GA) for detection of lost property. Moreover, GA is suitable for global search problems, but it is not necessarily suitable for local search problems. Therefore the local search technique is included to improve GA property. Object search in our proposed method is divided into two parts, global search and local search. In the local search, we use a simple random search. According to experimental results, in the global search, search accuracy is relatively good in the almost experiments, but the local search is not so effective in almost experiments. In the future, we need to improve fitness function in the global and local search.
69.	Zhang Peng, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Development of EOG Mouse Using Learning Vector Quantization, Proceeding of the 2nd International Conference on Intelligent Systems and Image Processing 2014, 38-43, Kitakyushu, Sep. 2014. (Summary) Recognition of eye motions has attracted more and more attention of researchers all over the world in recent years. In particular studies to make lives more convenient for patients as ALS who cannot move even their muscles expect eye have been actively done. Many kinds of eye motion recognition methods have been proposed, for example, using infrared to track pupil position, or using image processing technologies to find the pupil. However as they use either infrared or cameras, they may have some effect on the eye. Therefore, in our study, we use an EOG method to recognize eye motions: attaching wet disposable electrodes on the patients' face to obtain eye movement signals. Then Learning Vector Quantization algorithm is used to recognize each eye motion. Finally, corresponding recognition results to various mouse operations can be obtained. In our study, we recognized eye motions of rolling eyes upward, downward, rolling left, rolling right, diagonal eye motions which contain rolling up-left, rolling up-right, rolling down-left, rolling down-right ( the angle of the diagonal motion is 45°), blink, blink string of three times motion, left wink and right wink, in total 12 kinds of eye motions. The average recognition accuracy was over 98%. Using this recognition system, we achieved 8 direction cursor movements and double click action, scroll page upward and downward. This study would be used as a means of communication to help those patients as ALS. This system is implemented on a PC as the on-line system.
70.	Shoichiro Fujisawa, Norihiro Ikeda, Kazuya Takahashi, Tomoyuki Inagaki, Katsuya SATO, Shin-ichi Ito and Motohiro Seimiya : Research on identifying LED blocks mounted on crosswalk boundaries for persons with visually impairment, 5th International Conference on Applied Human Factors and Ergonomics 2014, Krakow, Jul. 2014.
71.	Kyohei Hirono, Tatsuya Miyazaki, Shin-ichi Ito, Katsuya SATO and Shoichiro Fujisawa : Research on continuity among assistive devices for personal mobility of visually impaired persons around crossings, Proc. of 5th Inernational Symposium on Advanced Control of Industrial Processes (ADCONIP 2014), 247-250, Hiroshima, May 2014.
72.	Akiko Sugiyama, Momoyo Ito, Kazuhito Sato, Shin-ichi Ito and Minoru Fukumi : Analysis of Driving Behavior Caused by Hiyari-Hatto Event Focusing on Head Motion, Proceeding of 2014 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing, 705-708, Honolulu, Mar. 2014. (Summary) In this paper, we analyze projected head positions of safety verification on the two-dimensional plane to clarify what head posture changes behavior are contained in the data. In this experiment, we consider the projected coordinate changes due to the presence or absence of hiyari-hatto events, and discuss the relation between the head position changes and the influence of hiyari-hatto event. From the experimental results, the driver becomes cautious after the event, and a change occurs in the safety verification behavior by becoming cautious.
73.	Tomoaki Chika, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Personal authentication system by using Kinect, Proceeding of 2014 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing, 289-292, Honolulu, Mar. 2014. (Summary) In this paper, the objective is to propose a method to perform personal authentication from motion information signed in space obtained using the Kinect sensor. Biometrics authentication is one of the hottest authentication systems all over the world. It is high secure compared with other authentication systems. However, it has an issue that is generally expensive. Therefore, we propose an inexpensive biometrics authentication system by using the Kinect sensor. We can obtain hand positions easily because the Kinect sensor can obtain skeleton data quickly. Therefore, it is thought that we can develop a system to track a fingertip.
74.	Daiki Konishi, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Comparison of Poolong Methods in a Deep Neural Network, Proceeding of 2014 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing, 285-288, Honolulu, Mar. 2014. (Summary) The objective of this study is to construct a personal identification system using face with high recognition performance. We define the high recognition performance as being robust to change of surroundings (light, angle, size, and so on). However it is difficult to achieve that with ordinary image processing methods. Therefore we use the Deep Learning method based on Convolutional Neural Network to achieve our objective. In this paper, we compare the pooling methods (max-pooling and average-pooling) as preliminary step of this study. The pooling is one of elements of Convolutional Neural Network. To simplify a problem, we carry out face recognition using face and object images.
75.	Takahiro Horiuchi, Yohei Takeuchi, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Creation of a Panoramic Image by Genetic Algorithm, Proceeding of 2013 International Conference on System, Process, and Control, 113-116, Kuala Lumpur, Dec. 2013. (Summary) The purpose in this paper is to generate a panoramic image without using special equipments such as a panoramic camera. Our proposed method performs image processing with a computer. This paper therefore presents a new panoramic image generation method that solves drawbacks of conventional generating methods. This paper intends to perform high-speed and highly precise generation of a panoramic image. A template matching method using a genetic algorithm (GA) to make a panoramic image is presented. The effectiveness of the present method is demonstrated by measn of computer simulations. (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84897799121 (Elsevier: Scopus)
76.	Zhang Peng, Yohei Takeuchi, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Implementation of EOG Mouse Using Learning Vector Quantization and EOG-feature Based Methods, Proceeding of 2013 International Conference on System, Process, and Control, 98-102, Kuala Lumpur, Dec. 2013. (Summary) In this paper we proposed a system that uses EOG-feature based methods and Learning Vector Quantization algorithm to recognize eye motions. According to the recognition results, we use API to control cursor movements. We recognized 12 eye motions. 8 directions motions correspond to 8 directions cursor movement in this system. Using this system we have obtained a high recognition accuracy of eye motions (The average correct detection rate on each subject was 97.8%, 97.6% and 92.7%). This EOG Mouse interface would be used as a means of communication to help those patients as ALS. (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84897777164 (Elsevier: Scopus)
77.	Takako Ikuno, Yohei Takeuchi, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Abandoned Object Detection by Genetic Algorithm with Local Search, Proceeding of 2013 International Conference on System, Process, and Control, 113-116, Kuala Lumpur, Dec. 2013. (Summary) In this study, the objective is to propose a method in which pictures of security cameras are administered automatically. The administered target is abandoned objects. We use a template matching using Genetic Algorithm (GA) for detection of abandoned objects. GA is suitable for global search problems, but it is not suitable for local search problems. Therefore the local domain search technique is included to improve GA property. In GA, a chromosome is composed of the coordinates that locates a template image, and template image's scaling rate and rotation angle. In global domain search, the chromosome has the angle of 0 degree. Thresholds for a fitness function and the number of the generations to start the local domain search are set experimentally. In local domain search, the chromosome's scaling rate and rotation angle of the best individual are changed using a random search method. According to experimental results, detection accuracy is relatively good in the global domain search, but the local domain search is not so effective in some images. In future work, we try to improve the local domain search. (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84897749400 (Elsevier: Scopus)
78.	Takako Ikuno, Yohei Takeuchi, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Abandoned Object Detection by Genetic Algorithm with Local Search, Proceeding of 2013 International Workshop on Nonlinear Circuits, Communication and Signal Processing NCSP'13, 261-264, Kona, Mar. 2013. (Summary) In this study, the objective is to propose a method in which pictures of security cameras are administered automatically. The administered target is abandoned objects. We use a genetic template matching method for detection of abandoned objects. In this case, it is necessary to match a whole target image using a template image and it is time-consuming. Therefore we use Genetic Algorithm (GA) with local domain search. GA mainly optimizes the coordinate by which a template image is located, although the size and orientation of the template are fixed for reduction of search time. In addition, those of the template image are charged by the local domain search. In the global domain search using GA, its detection accuracy is relatively good, but the local domain search is not so effective. To solve this problem, we try to improve the local domain search. For this purpose, the template matching of image size and orientation is improved using a deterministic process.
79.	Takashi Fujishima, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : A Method for Detecting Signs of Train Sickness on Tilting Train, Proceeding of 2013 International Workshop on Nonlinear Circuits, Communication and Signal Processing NCSP'13, 341-344, Kona, Mar. 2013. (Summary) This paper proposes a method to detect signs of motion sickness based on electroencephalogram (EEG) and heart rate (HR) analysis techniques. In EEG analysis, the averages of power spectra are calculated as the EEG features. In HR analysis, we adopt Lorenz plot. The proposed method discusses differences among human states; normal, sign of motion sickness and motion sickness states, respectively. In order to show the effectives of the proposed method, we conduct experiments using real EEG and HR data.
80.	Takashi Hamano, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Detection of Abandoned Objects Based on Spatiotemporal Features from Public Stationary Camera, Proceeding of 2013 International Workshop on Nonlinear Circuits, Communication and Signal Processing NCSP'13, 257-260, Kona, Mar. 2013. (Summary) In this paper, we propose a method of searching abandoned objects based on spatiotemporal features. The final purpose is detection of the abandoned objects from public stationary camera. First, Space-Time (ST) Patch features for limited searching region are computed. These features are essentially composed of 6 features. Our approach is used to efficiently separate moving objects and unmoving ones from background. Next, we detect it as a human or not with HOG features and Real AdaBoost into limited searching regions. The HOG features are converted it one-dimensional histogram of the corresponding values. We learn detection of human with Real AdaBoost by these values.
81.	Peng Zhang, Yohei Takeuchi, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Recognition of Eye Motions Using EOG and Statistic Learning, Proceeding of 2013 International Workshop on Nonlinear Circuits, Communication and Signal Processing NCSP'13, 29-32, Kona, Mar. 2013. (Summary) Recognition of eye motions has attracted more and more attention of researchers all over the world in recent years. In order to provide an efficient means of communication for patients as ALS (amyotrophic lateral sclerosis) who cannot move even their muscles except eyes. It is important to pursue such a research. In this paper, we propose a new recognition method that uses the LVQ (Learning Vector Quantization) to recognize a class of each motion in the first step, and then uses the relation between max value and min value of each motion's EOG features to separate similar motions in the same class. Using the new method we have obtained a high recognition accuracy of eye motions.
82.	Tomo Uchiyama, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Database Optimization Technique for Ethical Pharmaceutical Searching System, Proceeding of 2013 International Workshop on Nonlinear Circuits, Communication and Signal Processing NCSP'13, 145-148, Kona, Mar. 2013. (Summary) This paper proposes a method to optimize database for searching ethical pharmaceutical based on multi-objective optimization. The proposed system consists of medicine combination optimization and information visualization to explain intelligible information. The multi-objective genetic programming (MOGP) is used to specify the optimal combinations based on new criteria. By means of computer simulation, the effectiveness of the proposed method is demonstrated.
83.	Nobuhisa Kuramoto, Shin-ichi Ito, Katsuya SATO and Shoichiro Fujisawa : Trigger Pattern Detection Method for Assisting in Ambulation Rehabilitation Based on EEG Analysis, Proc. of 21st IEEE International Symposium on Robot and Human Interactive Communication, Ro-Man2012, 646-652, France, Sep. 2012. (Summary) This paper proposes a method to detect the trigger pattern of walking motion from an electroencephalogram (EEG). Our final goal is to develop a rehabilitation assistance system that can assist in ambulation exercise and be used on a daily basis by watching for the patient's degree of functional recovery. Due to assist in ambulation rehabilitation, it needs to detect the feature signal related to the voluntary walking. As a first step, we detected an EEG feature pattern that has the potential to become the starting point of walking motion. The proposed method involves EEG recording, noise reduction, and EEG pattern classification. The EEG device had dry-type sensors and several electrodes with a headphone. Morphological filters were used to reduce EEG noise. The EEG patterns related to walking motion were classified using a support vector machine. To demonstrate the effectiveness of the proposed method, we conducted experiments using real EEG data. (Keyword) electroencephalogram / pattern classification (Link to Publication Site) ● Publication site (DOI): 10.1109/ROMAN.2012.6343824 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84870817925 (DOI: 10.1109/ROMAN.2012.6343824, Elsevier: Scopus)
84.	Shin-ichi Ito, Katsuya SATO and Shoichiro Fujisawa : Learning Algorithm for Self-Organizing Map Classification of Electroencephalogram Patterns with Individual Differences, Proc. of WCCI 2012 IEEE World Congress Computation Intelligence, 2577-2582, Australia, Jun. 2012. (Summary) This paper introduces a method to classify electroencephalogram (EEG) patterns using a self-organizing map (SOM) with new learning algorithm considering an individual difference in EEG. To classify the EEG patterns with the individual difference, we propose an algorithm to specify the learning area of SOM based on sub-attribute information (SOMSA). The EEG data has two attributes that are the feature indicating the EEG pattern and sub-attribute information meaning the individual difference. The individual difference is quantified by analyzing human character, because this paper defines that one's character affects the individual difference. In the preprocessing, we extract the EEG feature vector by calculating the time average on each frequency band which are theta, alpha and beta, respectively. One's character is analyzed though the ego analysis using psychological testing. The device for recording the EEG uses the band-type device with a few numbers of electrodes. In order to prove the effectiveness of the proposed method, we conduct experiments using real EEG data. The experimental results show that the accuracy rate of the EEG pattern classification using SOMSA improves compared with the standard SOM. (Keyword) electroencephalogram / individual difference / self-organizing map / pattern classification (Link to Publication Site) ● Publication site (DOI): 10.1109/IJCNN.2012.6252723 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84865078199 (DOI: 10.1109/IJCNN.2012.6252723, Elsevier: Scopus)
85.	Kayoko Matsubara, Shin-ichi Ito, Katsuya SATO, Masayuki Booka, Hidehisa Oku and Shoichiro Fujisawa : UD of Road Surface for the Low Vision, --- How Road Surface Condition Affects Visibility of Tactile Walking Surface Indicators(TWSIs)for the Low Vision ---, Universal Design 2012 Oslo, 1, Oslo, Jun. 2012. (Summary) WHO have announced that about 80% of visually impaired persons are the low vision (LVs) in the world. Many LVs can walk safely using the road and the feature things as a key. In Japan, many LVs can walk safety using a Tactile walking surface indicators (TWSIs) because its color is high conspicuous blight yellow and exists most walking place. However, LVs cannot identify the position of TWSIs, because of exists different color of TWSIs or low luminance contrasts between TWSIs and surrounding road. In order to improve these situations, we think ``lateral bands'', which installs neutral lines in both side of TWSIs, as a new method. This method improved the visibility of the TWSIs for LVs, but some LVs miss-recognize such lateral bands as a hole or ditches and they cannot walk safety. The purpose of our research is to find causes miss-recognition when LVs use. In this research, we designed two experiments, cause of we think luminance contrast of lateral bands or relationship of lateral bands and road are very important matter of miss-recognition for LVs. These experiments are to find the view of the band of LVs, and to find relationship between width, luminance contrasts and illumination. Fifty-nine LVs were participated as subjects as these experiments. The most important result was the luminance contrast of a line affects miss-recognition for LVs. I think that these research findings become one indicator of TWSIs with the lateral band of outdoor area or public building which was adapted for the universal design. Moreover, this result makes a big guideline of floor designing not only TWISs but also without. And, this result also makes good effort not only visual impaired persons but also an elderly person with eyesight gets worse.
86.	Nobuhisa Kuramoto, Shin-ichi Ito, Katsuya SATO and Shoichiro Fujisawa : Walking Motion Feature Detection System Based on EEG Analysis, Proceedings of 2012 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP2012), 269-272, Honolulu, Mar. 2012.
87.	Matsubara Kayoko, Shin-ichi Ito, Katsuya SATO, Masayuki Booka, Oku Hidehisa and Shoichiro Fujisawa : Effect of Road Surface Condition for the Low Vision, Proceedings of 2012 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP2012), 96-99, Honolulu, Mar. 2012.
88.	Shin-ichi Ito, Masashi Hamaguchi, Katsuya SATO, Shoichiro Fujisawa and Minoru Fukumi : Variability in EEG with Single Point Sensing as Inter-Individual Difference Measure Using Self-Organizing Map, Proc. of 2011 International Symposium on Nolinear Theory and its Applications, NOLTA2011, 290-293, Kobe, Sep. 2011. (Summary) In this paper, we introduce an EEG analysis technique to confirm an inter-individual difference in prefrontal cortex EEG with a single point sensing. The device for recording the EEG uses the dry-type sensor and a few numbers of electrodes. The EEG analysis adapts the feature mining on EEG pattern using a self-organizing map (SOM). The EEG patterns are determined based on the preference evaluation on sound listened to. In the preprocessing, we extract the EEG feature vector by calculating the time average on each frequency band which are theta, low-alpha, high-alpha, low-beta, high-beta, respectively. To confirm the inter-individual difference, we do experiments using real EEG data. These results show that the learning results by SOM on each human are clearly different when using same initial weight values for the SOM.
89.	Shin-ichi Ito, Yasue Mitsukura, Takafumi Saito, Katsuya SATO and Shoichiro Fujisawa : Visualization on Pareto Solutions in Multi-objective Optimization, Proc. of 4th IASTED International Conference on Artificial Intelligent and Soft Computing, ASC2011, 267-272, Creta, Jun. 2011. (Summary) This paper proposes a method for assisting in the specification of a single optimal solution. The proposed method visualizes the relationship between the gene and fitness values of all individuals in a Pareto solution. The gene and fitness values are expressed as colored and gray scales, respectively. The information visualization technique in this paper has the advantage of indicating the Pareto solution and its details in one frame. To show the effectiveness of the proposed method, we apply the proposed method to benchmark problems. The proposed method visualizes the relationship between the gene and fitness values of all individuals in a Pareto solution. The gene and fitness values are expressed as colored and gray scales, respectively. It is possible to comfirm the Pareto solution and its details easily. (Keyword) genetic algorithm / Information visualization
90.	Ryota Kurozumi, Kosuke Tsuji, Shin-ichi Ito, Katsuya SATO, Shoichiro Fujisawa and Toru Yamamoto : Experimental Validation of an Online Adaptive and Learning Obstacle Avoiding Support System for the Electric Wheelchairs, Proceedings of 2010 IEEE International Conference on Systems, Man and Cybernetics (SMC 2010), 92-99, Istanbul, Oct. 2010. (Summary) 身体に障害を持つ高齢者が，空間が制限された室内において電動車いすの操縦することは，相当のスキルが必要である．この論文では，強化学習を用いて電動車いすの障害物回避の支援システムを述べる．障害物回避は，Minimum Vector Field Histogram(MVFH)法によって半自動で支援する．このMVHFをユーザー操作と障害物回避支援に適用する．この提案手法では，修正割合が環境とユーザーの状態に従って，強化学習によって調整される．この新しい手法をシュミレーション例で数値的に評価した．さらに，提案手法を実際の電動車いすに応用し，実際の操縦環境で提案手法の有効性を検証した． (Link to Publication Site) ● Publication site (DOI): 10.1109/ICSMC.2010.5642211 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-78751537203 (DOI: 10.1109/ICSMC.2010.5642211, Elsevier: Scopus)
91.	Tsukasa Nakamura, Yohei Tomita, Shin-ichi Ito and Yasue Mitsukura : A Method of Obtaining Sense of Touch by Using EEG, Proc. of 19th IEEE International Symposium on Robot and Human Interactive Communication (Ro-Man'10), 296-301, Viareggio, Sep. 2010. (Link to Publication Site) ● Publication site (DOI): 10.1109/ROMAN.2010.5598636 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-78649853029 (DOI: 10.1109/ROMAN.2010.5598636, Elsevier: Scopus)
92.	Shin-ichi Ito, Yasue Mitsukura, Katsuya SATO, Shoichiro Fujisawa and Minoru Fukumi : Association between Ego Scores and Individual Characteristics in EEG Analysis, --- Basic Study on Individual Brain Activity ---, Proc. of 19th IEEE International Symposium on Robot and Human Interactive Communication (Ro-Man'10), 230-235, Viareggio, Sep. 2010. (Summary) This paper introduces a method for discussing an association between ego scores and individual characteristics in EEG analysis. The ego scores express one of the personality and/or nature on a human. The EEG analysis calculates the power spectra of the frequency weighted of the EEG signal, and evaluates through detecting the EEG patterns while listening to the music. A k-nearest neighbor classifier is used to detect the EEG patterns. The specified weight value to weight frequency of EEG and the results of EEG pattern detection express the individual characteristics in EEG analysis. Finally, we do experiment using a real EEG data for discussing the association the ego scores and the individual characteristics in EEG analysis. An interesting tendency was that the subjects who were the combined ego-type tended to have a powerful response to negative stimuli more than positive stimuli. They had the stable filter weighted frequency. (Link to Publication Site) ● Publication site (DOI): 10.1109/ROMAN.2010.5598639 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-78649821619 (DOI: 10.1109/ROMAN.2010.5598639, Elsevier: Scopus)
93.	Shoichiro Fujisawa, Naohiro Yamada, Shin-ichi Ito, Katsuya SATO, Seiji Mitani and Osamu Sueda : Identification of Tactile Walking Surface Indicators by White Cane, Proceedings of 2010 IEEE International Conference on Virtual Environmnets Human-Computer Interfaces and Measurement Systems (VECIMS 2010), 52-55, Taranto, Sep. 2010. (Link to Publication Site) ● Publication site (DOI): 10.1109/VECIMS.2010.5609335 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-78650154443 (DOI: 10.1109/VECIMS.2010.5609335, Elsevier: Scopus)
94.	Shin-ichi Ito, Yasue Mitsukura, Katsuya SATO, Shoichiro Fujisawa and Minoru Fukumi : Study on Association between User's Personality and Individual Characteristic of Left Prefrontal Pole EEG Activity, Proc. of 2010 Sixth International Conference on Natural Computation (ICNC'10), 2163-2166, Yantai, Aug. 2010. (Summary) This paper introduces a method for discussing the association between human personality based on egogram scores and the results of classifying the electroencephalogram (EEG) patterns while listening to the music. The egogram based on psychological testing is used for analyzing his/her personality. The frequencies of the EEG analyzed are the components that contain significant and immaterial information and have different importance. We express the different importance through the weight value on frequencies using real-coded genetic algorithm. Then, the EEG patterns, which are determined based on the evaluation of the impression on the music, are classified using the k-nearest neighbor method. Finally, we discuss the association between his/her personality and the individual characteristic in the EEG analysis. An interesting tendency was that the false-detection accuracy of the EEG pattern, meaning the response on negative stimuli, did not become low when the score and level on ego states of Adult was extreme.
95.	Shin-ichi Ito, Yasue Mitsukura, Katsuya SATO, Shoichiro Fujisawa and Minoru Fukumi : Study on Relationship between Personality and Individual Characteristic of EEG for Personalized BCI, Proc. of IEEE Region 8 SIBIRCON-2010, 106-111, Irkutsuk, Jul. 2010. (Summary) It is an important issue that the relationship between personality and individual characteristic of an electroencephalogram (EEG) for using a personalized brain computer interface on daily basis. In this paper, we introduce a method for discussing the relationship between personality based on egogram scores and the results of the EEG pattern detection. The egogram based on psychological testing is used for analyzing human personality. The frequencies of the EEG analyzed are the components that contain significant and immaterial information and have different importance. We express the different importance through the weight value on frequencies using real-coded genetic algorithm. Then, the EEG patterns, which are determined based on the evaluation of the impression on the music, are classified/detected using the k-nearest neighbor method. Finally, we discuss the relationship between human personality and the individual characteristic in the EEG analysis. An interesting tendency was that the false-detection ratio of the EEG pattern, meaning the response on negative stimuli, did not become low when the score and level on ego states of Adult was extreme. (Link to Publication Site) ● Publication site (DOI): 10.1109/SIBIRCON.2010.5555320 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-77957253414 (DOI: 10.1109/SIBIRCON.2010.5555320, Elsevier: Scopus)
96.	Zhang Le, He Qing, Shin-ichi Ito and Kenji Kita : Euclidean Distance-Ordered Thinning for Skeleton Extraction, Proc. of the 2nd International Conference on Education Technology and Computer, ICETC'10, Vol.1, 311-315, Shanghai, Jun. 2010. (Summary) The skeleton is an important feature for the representation of a shape in image analysis. In this paper, we propose a novel Euclidean distance-ordered thinning algorithm for skeleton extraction. The experiment results show that the algorithm is able to extract the connected and one-pixel wide skeleton that can correctly preserve the topology of the object. Furthermore, the extracted skeleton locates on the accurate position and it is insensitive to boundary noise.
97.	Kayoko Matsubara, Takamaro Hamada, Shin-ichi Ito, Katsuya SATO, Masayuki Booka, Hidehisa Oku, Seiji Mitani, Toshikazu Kato, Osamu Sueda and Shoichiro Fujisawa : Visibility of Lateral Band for Tactile Walking Surface Indicators, Proc. of NCSP'10, 203-206, Hawaii, Mar. 2010.
98.	Shin-ichi Ito, Yasue Mitsukura, Katsuya SATO, Shoichiro Fujisawa and Minoru Fukumi : A Study on Relationship between Personal Feature of EEG and Human's Characteristic for BCI Based on Mental State, Proc. of IECON 2009, 4265-4268, Porto, Nov. 2009. (Summary) This paper discusses the relationship the result classified the electroencephalogram (EEG) patterns while listening to music and the humans nature, which indicates the personal feature of a human, based on the egogram pattern. The EEG analysis calculates the power spectra of the frequency of the EEG signal, divides into the frequency bands based on theta, alpha, and beta rhythms, and evaluates whether the music matches mood of the user or not through EEG pattern classification. A k-nearest neighbor classifier is used to classify the EEG patterns. The egogram is used for detecting nature of the human. Finally, we discuss the relationship the result of EEG pattern classification and the humans nature. An interesting finding was that the recognition accuracy of the EEG pattern meaning the response of them on negative stimuli became high when the subject was classified into the egogram pattern with introverted nature.
99.	Yohei Tomita, Shin-ichi Ito, Yasue Mitsukura, Toshihisa Tanaka and Jianting Cao : The Extraction of Sleep Quality by using the Multi-channel EEG Signal, Proc. of 2009 Asia-Pacific Signal and information Processing Association Annual Summit and Conference, APSIPA ASC 2009, 174-177, Sapporo, Oct. 2009.
100.	Tsukasa Nakamura, Shin-ichi Ito, Yasue Mitsukura and Hiroto Setogawa : A Method for Evaluating the Degree of Human's Preference Based on EEG, Proc. of the Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 732-735, Kyoto, Sep. 2009.
101.	Shoichiro Fujisawa, Naohiro Yamada, Seiji Mitani, Shin-ichi Ito, Katsuya SATO and Osamu Sueda : A study on recognition of tactile walking surface indicator by white cane, 10th Conference of Association for the Advancement of Assistive Technology in Europe, 850, Florence, Sep. 2009. (Summary) 視覚障害者が周囲路面と点ブロックと線ブロックを識別する場合，白杖を使って床情報を入手し床を識別する必要がある．しかし，床やブロックの寸法と識別しやすさの関係や，点字ブロックに触れたときの白杖の挙動は明らかになっていない．白杖が点字ブロックとの接触が始まるとき，量的に手に伝わるパワーを調査するのが必要がある．この研究では被験者が白杖で床を触知した際の白杖の振動加速度と識別しやすさとの関係について検討している．また，白杖を用いた床の識別には床のかたさやすべりに関係した凹凸の変化が大きく寄与していると言われている．本研究ではパラメトリックな寸法配置をもつ点ブロックを用い，白杖が点ブロックに衝突したときの白杖から手に伝わる力も分析する．
102.	Tsukasa Nakamura, Shin-ichi Ito, Yasue Mitsukura and Hiroto Setogawa : Tactile Impression Detection System Using the EEG, Proc. of NCSP'09, 515-518, Hawaii, Mar. 2009.
103.	Yohei Tomita, Shin-ichi Ito, Yasue Mitsukura, Toshihisa Tanaka and Jianting Cao : Rhythmic Component Extraction for Sleeping Electroencephalogram Analysis, Proc. of NCSP'09, 443-446, Hawaii, Mar. 2009.
104.	Taiki Fuji, Yasue Mitsukura, Shin-ichi Ito, Toshio Moriya, Nobutaka Kimura and Takanari Tanabata : Aligning the Real Space with the 2D Model on See-through typed HMD using the GA with Interactive Handling, Proc. of NCSP'09, 573-576, Hawaii, Mar. 2009.
105.	Satoru Suzuki, Yasue Mitsukura, Shin-ichi Ito, Toshio Moriya, Nobutaka Kimura and Takanari Tanabata : A Human Tracking System on the Robot with Face Detection, Proc. of NCSP'09, 399-402, Hawaii, Mar. 2009.
106.	Junko Murakami, Shin-ichi Ito, Yasue Mitsukura, Jianting Cao and Minoru Fukumi : Classification of the vision correction by the EEG, Proc. of NCSP'09, 435-438, Hawaii, Mar. 2009.
107.	Eriko Yokomatsu, Hironobu Fukai, Shin-ichi Ito, Yasue Mitsukura, Hirokazu Watai, Katsumi Tashiro and Kazutomo Murakami : Comfortable Ride Extraction System using the EEG, Proc. of NCSP'09, 507-510, Hawaii, Mar. 2009.
108.	Shin-ichi Ito, Yasue Mitsukura and Minoru Fukumi : A Basic Method for Classifying Humans Based on an EEG Analysis, Proc. of ICARCV'2008, 1783-1786, Hanoi, Dec. 2008. (Summary) This paper introduces a method for extracting and confirming a human feature by analyzing prefrontal cortex electroencephalogram (EEG) activities on listening to music that user feels matching, no matching his mood and otherwise. The proposed method is EEG sensing and analyzing to monitor the human feature and classify the human with the activity of different frequency bands of the power spectrum of the EEG signal, which is simple and basic. Finally, the performance of the proposed method is evaluated using real EEG data. We confirm to be able to classify the human into three or more groups.
109.	Yohei Tomita, Shin-ichi Ito and Yasue Mitsukura : The EEG Analysis Method for Obtaining the Feeling, Proc. of ICCAS'2008, 2555-2558, Seoul, Oct. 2008.
110.	Tsukasa Endo, Shin-ichi Ito, Yasue Mitsukura and Minoru Fukumi : The Music Analysis Method Based on Melody Analysis, Proc. of ICCAS'2008, 2559-2562, Seoul, Oct. 2008. (Summary) Recently, we can have large amounts of music thanks to the development of the computer technology. However, as the data of the music becomes larger, it is a hassle to classify the music based on the music content manually. We think that it is necessary to categorize the music automatically based on our mood. In this paper, we propose a novel method to analyze the music automatically based on melody analysis. The proposed method considers music genres as the measure of music analysis. We extract the acoustic features to characterize the music. Then, we classify music using multi-class classifiers based on the support vector machine (SVM).We adopt two approaches to the multi-class classification method. Furthermore, we propose a visualization method to specify the musical structure on the music genres from the classification result. Finally, computer simulations are done by using real music data in order to prove the effectiveness of the proposed method.
111.	Shin-ichi Ito, Yasue Mitsukura and Minoru Fukumi : A Method for Filtering Frequency of EEG using Real-Coded Genetic Algorithm, Proc. of SCIS&ISIS, 2097-2100, Nagoya (Japan), Sep. 2008. (Summary) This paper proposes a method for filtering frequencies of an electroencephalogram (EEG) on EEG analysis with a real-coded genetic algorithm (RGA). An EEG of humans has the different personal feature that contains an individual error and a change of his/her physical and mental conditions at the occasion arises. The frequencies of the EEG analyzed are the components that contain significant and immaterial information and have different importance. We express the different importance through the weighted value on frequencies. These weighted values are through to express personal feature of EEG signal. The proposed method calculates the power spectra of the frequency of the EEG signal, is weighted these frequencies, divides the frequency bands based on theta, alpha1-3, and beta rhythms, and evaluates whether the music matches mood of the user by EEG pattern classification. A real-coded genetic algorithm is used to specify the weighted value of frequency of the EEG. A k-nearest neighbor classifier is used to classify the EEG patterns. Finally, the performance of the proposed method is evaluated using real EEG data by classifying EEG patterns that listen to music matching, no matching mood of the subject, and feeling otherwise.
112.	Yohei Tomita, Shin-ichi Ito, Yasue Mitsukura, Minoru Fukumi and Takenori Suzuki : Time-series Models of the EEG Wearing Overcorrected Glasses, Proc. of KES'2008, 450-457, Zagreb (Croatia), Sep. 2008. (Summary) 多くの眼鏡ユーザは，過度に補正された眼鏡を使用している場合が少なくない．過度に補正された眼鏡の使用は眼に影響を与える．しかしながら，その影響を測り，評価する方法が確立されていない．そこで，脳波を分析することで，過度の補正が実施されている状態を評価する手法を提案する．脳波で過度の補正状態を分析するために，実験を実施した．実験では，PCゲームを30分間実施しているときの脳波を計測する．過度の補正状態と適正補正状態において，脳波の変化が異なることを確認した．
113.	Yasuyuki Nakamura, Hironobu Fukai, Shin-ichi Ito, Yasue Mitsukura and Minoru Fukumi : The Proposal of the GA with Sex-determination of the Hymenoptera and Its Applications, Proc. of NCSP'08, 443-446, Gold Coast, Mar. 2008.
114.	Tsukasa Endo, Shin-ichi Ito, Yasue Mitsukura and Minoru Fukumi : The Music Classification Method by Melody Analysis Using the SVM, Proc. of NCSP'08, 240-243, Gold Coast, Mar. 2008. (Summary) Recently, users can have large amounts of music thanks to the development of the computer technology. However, it becomes hard to identify the music based on the music content manually. We think that it is necessary to categorize the music automatically. We propose the music classification method by melody analysis using the SVM in this paper. We aim at the accomplishment of the automatic music classification. Finally, computer simulations are done by classifying real music data in order to show the effectiveness of the proposed method.
115.	Yohei Tomita, Shin-ichi Ito, Yasue Mitsukura, Naoko Koda, Jianting Cao and Minoru Fukumi : Benefits of the Animal Assisted Therapy -- Comparison the EEG features, Proc. of NCSP'08, 299-302, Gold Coast, Mar. 2008. (Summary) The animal assisted therapy (AAT) has been known to have psychological and social effects on human being. However, scientific research is insufficient yet. Because, it is difficult to have done in the medical clinic. Furthermore, government can not allow as the medical care because of nothing of clear proof. Therefore, in order to show a scientific basis of the AAT effects, we analyze the electroencephalogram (EEG). Previous study using the stuffed dog has a problem that healing effects from the stuffed dog are little. Therefore, we used a real friendly dog at this experiment to give more healing effects on the subjects. As the results, mental conditions of subjects changed and the brain-wave patterns is confirmed. Finally in this paper, we show the relation between the EEG and mental stability.
116.	Junko Murakami, Shin-ichi Ito, Yasue Mitsukura, Jianting Cao and Minoru Fukumi : The Proposal of the EEG Characteristic Extraction Method by Using the FCM, Proc. of MJISAT'2007, T4-4-1-T4-4-6, Kuala Lumpur, Nov. 2007. (Summary) In this paper, we classify the human conditions (before meal, after meal, before smoking, and after smoking). Moreover, we extract the frequency characteristic of conditions. First of all, we measure the EEG data for subjects by using the simple electroencephalograph. Then, the EEG feature is extracted by using the singular value decomposition (SVD). We classify the human conditions by using the fuzzy cmeans (FCM). Moreover, we perform questionnaires for subjects, in order to analyze the data. Then, in order to show the effectiveness of the proposed method, computer simulations are done.
117.	Hayato Kimura, Shin-ichi Ito, Yasue Mitsukura and Minoru Fukumi : The Sound Emphasis Using the Double Structured ICA, Proc. of MJISAT'2007, T3-4-1-T3-4-4, Kuala Lumpur, Nov. 2007. (Summary) Recently, an independent component analysis (ICA) is paid to attention as a method for classifying an independent signal from assembly of different signals. There are a lot of studies using the ICA, for example, the field of image processing, speech recognition, and electroencephalogram (EEG) analysis. Meanwhile, a present musical notation is made by hearing the sound with a person's ear. However, the musical notation is so difficult, because each subject result of the musical notation is different, and the musical notation has many costs. Therefore, we propose a sound emphasis method using the double structured ICA. Then, we aim to obtain the emphasized sounds, for example, the high pitch sound and the low pitch sound. Furthermore, in order to show the effectiveness of the proposed method, we show the computer simulations by using the real data.
118.	Yohei Tomita, Koji Hatayama, Shin-ichi Ito, Yasue Mitsukura, Naoko Koda and Jianting Cao : Analyzing the Effects of Animal Assisted Therapy by the EEG, Proc. of MJISAT'2007, Kuala Lumpur, Nov. 2007.
119.	Eriko Yokomatu, Shin-ichi Ito, Yasue Mitsukura, Minoru Fukumi and Jianting Cao : An Individual Preference Detection System Using the EEG, Proc. of MJISAT'2007, T2-4-1-T2-4-4, Kuala Lumpur, Nov. 2007. (Summary) There are a lot of reports concerning the relation between the EEG and the preference in the analysis of the biosignal. Moreover, attention is currently focused on the aroma, for example, aromatherapy. In this paper, personal preference using olfactory stimulus and the EEG are taken out. We propose the extraction method of the EEG features by the factor analysis. We measure the EEG when smelling favorite odor and dislikeable odor by the simple electroencephalograph. Then, the feature data of the EEG are extracted by the proposed method. Furthermore, in order to show the effectiveness of the proposed method, we demonstrate simulation examples. From the simulation result, it was confirmed that the proposed method works well.
120.	Shin-ichi Ito, Hiroko Nakamura, Yasue Mitsukura, Takafumi Saito and Minoru Fukumi : A Visualization of Genetic Algorithm Using the Pseudo-color, Proc. of ICONIP'2007, Vol.2, 444-452, Kitakyushu, Nov. 2007. (Summary) In this paper, we propose the method for determining the schema interactively based on a visualization method to grasp the search process and results in the binary-coded genetic algorithm. We focus on the chromosome structure, the fitness function, the objective function, and the association among these parameters, because these parameters are decided interactively and very difficult to disentangle their effects. We can indicate the most important parameters optimized in visually. The visualization method is indicated all individuals of the current generation using the pseudo-color. Moreover, the visualization method proposed uses to support for determining the schema. In order to show the effectiveness of the proposed method, we apply the proposed method to the zero-one knapsack problems.
121.	Shin-ichi Ito, Yasue Mitsukura, Jianting Cao and Minoru Fukumi : Detecting Method of Music to Match the User's Mood in Prefrontal Cortex EEG Activity Using the GA, Proc. of ICCAS'2007, 2142-2145, Seoul, Oct. 2007. (Summary) In this paper, we propose a method for detecting the mood much music for prefrontal cortex electroencephalogram (EEG) activity. The analyzed EEG frequencies contain significant and immaterial information components. We focused on the combinations of the significant frequency. These frequency combinations are thought to express personal features of EEG activity. In the proposed method, we calculate the spectrum of these frequency combinations rates that does not include the noise frequency components and evaluates whether the music matches the user's mood through a simple threshold processing. Then, a genetic algorithm (GA) is used to specify the frequency of personal features on the EEG. The threshold vale used the threshold processing is the average value of the spectrum rates specified EEG frequency combinations. Finally, the performance of the proposed method is evaluated using real EEG data.
122.	Junko Murakami, Shin-ichi Ito, Yasue Mitsukura, Jianting Cao and Minoru Fukumi : Detection of the Human-Activity Using the FCM, Proc. of ICCAS'2007, 1995-1998, Seoul, Oct. 2007. (Summary) In this paper, we propose the detection system of the human activity by using the electroencephalograms (EEG). First, we measure the EEG data for subjects. In most of all conventional studies, the EEG having a lot of sensors is used. Therefore, subjects must eat or smoke while using the EEG interface. However, this situation is not practical for subjects. In this study, taking account of the burden of subjects, we use only one measurement point 'FPI'. First, we measure the EEG data and the EMG data for subjects. Then, the EEG feature is extracted by using the singular value decomposition (SVD). From the result, we classify the EEG pattern by the fuzzy c-means (FCM). If we cannot classify the EEG pattern into each activity, the discriminant analysis (DA) is used. We consider the EEG features of activities. Then, in order to show the effectiveness of the proposed method, computer simulations are done.
123.	Shin-ichi Ito, Yasue Mitsukura, Jianting Cao and Minoru Fukumi : Method for Detecting Music to Match the User's Mood in Prefrontal Cortex Electroencephalogram Activity Based on Individual Characteristics, Proc. of 2007 IEEE International Conference System, Man and Cybernetics, 2640-2644, Montreal, Oct. 2007. (Summary) In this article, we propose a method for detecting music to match a users mood in prefrontal cortex electroencephalogram (EEG) activity. The frequencies of the EEG analyzed are the components that contain significant and immaterial information. We focused on the significant frequency combinations. These frequency combinations are thought to express individual characteristics of EEG activity. The proposed method calculates the percentage of the spectrum of these frequency combinations that does not include the noise frequency components and evaluates whether the music matches the users mood through a simple threshold processing. A genetic algorithm (GA) is used to specify the frequency of individual characteristics on the EEG. A threshold value that used the threshold processing is determined in the GA. Finally, the performance of the proposed method is evaluated using real EEG data.} (Link to Publication Site) ● Publication site (DOI): 10.1109/ICSMC.2007.4413830 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-40949119809 (DOI: 10.1109/ICSMC.2007.4413830, Elsevier: Scopus)
124.	Shin-ichi Ito, Yasue Mitsukura, Hiroko Miyamura, Takafumi Saito and Minoru Fukumi : The EEG Feature Extraction Method of Listening to Music Using the Genetic Algorithms and Latency Structure Model, Proc. of SICE'2007, 2823-2826, Takamatsu, Sep. 2007. (Summary) It is known that an electroencephalogram (EEG) is characterized by the unique and personal features of an individual. The EEG frequency components are contained the significant and immaterial information, and then each importance of these frequency components is different. These combinations are often unique like individual human beings and yet they have underlying basic characteristics. We think that these combinations and/or the importance of the frequency components show the personal features. Therefore we propose the two techniques for estimating the personal features. A simple genetic algorithm is used for specifying these frequency combinations. Other technique, a real-coded genetic algorithm is used for estimating the importance of EEG frequency components. Then a latency structure model based on the personal features is used for extracted the feature vector of the EEG. Furthermore, the visualization map is used for evaluating the extracted feature vector of the EEG. In order to show the effectiveness of the proposed methods, the performance of the proposed method is evaluated using real EEG data.
125.	Junko Murakami, Shin-ichi Ito, Yasue Mitsukura, Jianting Cao and Minoru Fukumi : A Design of the EEG feature detection and condition classification, Proc. of SICE'2007, 2798-2803, Takamatsu, Sep. 2007. (Summary) In this paper, we classify the human conditions (before and after meal, before and after smoking) and extract the frequency feature of conditions by using the electroencephalograms (EEG). First, we measure the EEG data. Then, we classify the conditions by using the principal component analysis (PCA). Moreover, the EEG data is reconstructed by using the questionnaires and the result of classification. From the result, we consider ideal circumstance for the EEG measurement. Finally, the EEG data is decompressed to consider the EEG features of conditions. Then, in order to show the effectiveness of the proposed method, computer simulations are done.
126.	Eriko Yokomatsu, Shin-ichi Ito, Yasue Mitsukura, Jianting Cao and Minoru Fukumi : A Design of the Preference Acquisition Detection System, Proc. of SICE'2007, 2804-2807, Takamatsu, Sep. 2007. (Summary) Recently in the world, there are a lot of reports concerning the relation between the EEG and the preference in the analysis of the biosignal. In this paper, personal preference using olfactory stimulus and the EEG are taken out. We measure the EEG when smelling favorite odor and dislikeable odor. Then, the feature data of the EEG are extracted by the proposed method. Furthermore, in order to show the effectiveness, we demonstrate simulation examples. From the simulation result, it was confirmed that the proposed method works well.
127.	Hayato Kimura, Shin-ichi Ito, Yasue Mitsukura and Minoru Fukumi : Music Part Classification Using the ICA, Proc. of 2007 RISP International Workshop on Nonlinear Circuits and Signal Processing, 485-488, Shanghai, Mar. 2007. (Summary) Recently, independent component analysis (ICA) is paid to attention as a method for classifying an independent signal from assembly of different signals, and there are a lot of researches using the ICA. Then, in this paper, we pay attention to the field of voice recognition, and we aim at the achievement of the part classification system of music using the ICA. Furthermore, in order to show the effectiveness of the proposed method, we show the simulation examples.
128.	Eriko Yokomatsu, Shin-ichi Ito, Yasue Mitsukura, Jianting Cao and Minoru Fukumi : Preference Acquisition by Using Olfactory Stimulus and EEG, Proc. of 2007 RISP International Workshop on Nonlinear Circuits and Signal Processing, 381-384, Shanghai, Mar. 2007. (Summary) In this paper, personal preference using olfactory stimulus and the EEG are taken out. We propose the extraction method of the EEG features by the factor analysis. Then, we classify the odor into favorite and dislikeable. Furthermore, in order to show the effectiveness of the proposed method, we demonstrate simulation examples. From the simulation result, it was confirmed that the proposed method works well.
129.	Junko Murakami, Shin-ichi Ito, Yasue Mitsukura, Jianting Cao and Minoru Fukumi : Classification of Conditions by the EEG, Proc. of 2007 RISP International Workshop on Nonlinear Circuits and Signal Processing, 297-300, Shanghai, Mar. 2007. (Summary) In this paper, we classify the human conditions (before meal, after meal, before smoking, and after smoking) by using the electroencephalograms (EEG).We propose the electroencephalograms (EEG) characteristic extraction method by using the principal component analysis (PCA). Then, in order to show the effectiveness of the proposed method, computer simulations are done by classifying the EEG pattern in the proposed method.
130.	Kentaro Tohi, Shin-ichi Ito, Yasue Mitsukura and Minoru Fukumi : Pattern Recognition of EMG Signals by the Evolutionary Algorithms, Proc. of SICE-ICCAS'2006, 2574-2577, Busan, Oct. 2006.
131.	Satomi Ota, Shin-ichi Ito, Yasue Mitsukura and Minoru Fukumi : Proposal for the Extraction Method of Personal Comfort and Preference by the EEG Maps, Proc. of SICE-ICCAS'2006, 604-607, Busan, Oct. 2006.
132.	Shin-ichi Ito, Yasue Mitsukura, Hiroko Miyamura, Takafumi Saito and Minoru Fukumi : The Proposal of the EEG Characteristics Extraction Method in Weighted Principal Frequency Components Using the RGA, Proc. of SICE-ICCAS'2006, 1152-1155, Busan, Oct. 2006. (Summary) An EEG has frequency components which can describe most of the significant features. These combinations are often unique like individual human beings and yet they have underlying basic features. These frequency components are contained the important and/or not so important components, and then each importance of these frequency components are different. The real-coded genetic algorithm (: RGA) is used for selecting and being weighted the principal characteristic frequency components. We attempt to construct mental change appearance model (: MCAM) of only one measurement point. In order to show the effectiveness of the proposed method, computer simulations are carried out by using real data.
133.	Takahiro Ogawa, Satomi Ota, Shin-ichi Ito, Yasue Mitsukura, Minoru Fukumi and Norio Akamatsu : Influence of Music Listening on the Cerebral Activity by Analyzing EEG, Knowledge-Based Intelligent Infomation and Engineering Systems, Melbourne, Sep. 2005.
134.	Satomi Ota, Shin-ichi Ito, Yasue Mitsukura, Minoru Fukumi and Norio Akamatsu : The EEG Feature Extraction Using the Principal Component Analysis, SICE Annual Conference, Okayama, Aug. 2005.
135.	Satomi Ota, Shin-ichi Ito, Yasue Mitsukura, Minoru Fukumi and Norio Akamatsu : Feature Extraction of EEG Patterns Using the Principal Component Analysis and Neural Networks, Proc. of 2005 RISP International Workshop on Nonlinear Circuits and Signal Processing, 319-321, Honolulu, Mar. 2005. (Summary) In this paper, a system automatically selects the suitable music for music therapy from a large database of music by each EEG. By using this system, music therapy is done regardless of time and place, and it is needed for constructing this system to clear relations between the music and the EEG. In this paper, we measure the EEG of subjects in listening to music and extract some features of their patterns by using the Principal Component Analysis (PCA). Then we analyze them by using the neural networks (NN). Finally, in order to demonstrate the effective of the proposed method, we carry out the computer simulation. Then, we show the effectiveness of the proposed method.
136.	Shin-ichi Ito, Yasue Mitsukura and Norio Akamatsu : A Feature Extraction of EEG with Individual Characteristics, Proc. of Knowledge-Based Intelligent Information & Engineering Systems'2004, 853-858, Wellington, Sep. 2004. (Summary) Recently in the world, the research of the electroencephalogram (EEG) interface is done, because it has the possibility to realize an interface that can be operated without special knowledge and technology by using the EEG as a means of the interface. As one of the EEG interface, as for a goal for the final of this research, the EEG control system by any music is constructed. However, the EEG control by music is very difficult because it does not know the music and the causal relation of the EEG clearly. Therefore, the EEG analysis and music analysis is absolutely imperative in this system. In this paper, the EEG analysis method by using the FA and the NN is proposed. The FA is used for extracting the characteristics data of the EEG. The NN is used for estimating extracted the characteristics data of the EEG. Finally, in order to show the effectiveness of the proposed method, classifying the EEG pattern is done computer simulations. The EEG pattern is 4 conditions, which are listening to Rock music, Schmaltzy Japanese ballad music, Healing music, and Classical music.
137.	Shin-ichi Ito, Yasue Mitsukura, Minoru Fukumi and Norio Akamatsu : A Feature Extraction of the EEG Using the Factor Analysis and the Neocognitron, Proc. of International Conf. on Control Automation and Systems'2003, 2217-2220, Gyeonju, Korea, Oct. 2003. (Summary) It is known that an EEG is characterized by the unique and personal characteristics of an individual. Little research has been done to take into account these personal characteristics when analyzing EEG signals. Often the EEG has frequency components which can describe most of the significant characteristics. These combinations are often unique like individual human beings and yet they have an underlying basic characteristic as well. We think that these combinations are the personal characteristics frequency components of the EEG. In this seminar, the EEG analysis method by using the Genetic Algorithms (GA), Factor Analysis (FA), and the Neural Networks (NN) is proposed. The GA is used for selecting the personal characteristic frequency components. The FA is used for extracting the characteristics data of the EEG. The NN is used for estimating the characteristics data of the EEG. Finally, in order to show the effectiveness of the proposed method, classifying the EEG pattern is carried out via computer simulations. The EEG pattern is evaluated under 4 conditions: listening to Rock music, Schmaltzy Japanese ballad music, Healing music, and Classical music. The results, when personal characteristics frequency components are NOT used, gave over 80 % versus 95 % accuracy when personal characteristics frequency components are used. This result of our experiment shows the effectiveness of the proposed method.
138.	Shin-ichi Ito, Kensuke Yano, Yasue Mitsukura, Norio Akamatsu and Rajiv Khosla : Color-Identification System Using the Sandglass-Type Neural Networks, Proc. of Knowledge-Based Intelligent Information & Engineering Systems'2003, 1178-1184, Oxford, Sep. 2003. (Summary) 画像の著作権保護のために，画像自身が持つ特徴を鍵とする，カラーIDを作成する手法を提案した．手法には，砂時計型ニューラルネットワークを用いて，画像情報を圧縮し，カラーIDを作成している．そして提案手法の有効性を検証するために，顔画像を用いて実験を行ない，アプリケーションを開発した．
139.	Shin-ichi Ito, Yasue Mitsukura, Minoru Fukumi and Norio Akamatsu : A Feature Extraction of the EEG Using the Factor Analysis and Neural Networks, Proc. of Knowledge-Based Intelligent Information & Engineering Systems'2003, 609-616, Oxford, Sep. 2003. (Summary) In this paper, the EEG analysis method by using the GA, the FA, and the NN is proposed. The GA is used for selecting the personal characteristics frequency compnents. The FA is used for extracting the characteristics data of the EEG. The NN is used for estimating extracted the characteristics data of the EEG. Finally, in order to show the effectiveness of the proposed method, classifying the EEG pattern does computer simulations. The EEG pattern is 4 conditions, which are listening to Rock music, Schmaltzy Japanese ballad music, Healing music, and Classical music. The result, in the case of not using the personal characteristics frequency components, gave over 80 % using the personal characteristics frequency components, gave over 95 % effectiveness of the proposed method.
140.	Shin-ichi Ito, Yasue Mitsukura, Minoru Fukumi and Norio Akamatsu : The Recognition Method of the EEG Feature Pattern Using the Factor Analysis, Proc. of 34th Annual Conference of the International Simulation and Gaming Association, 987-996, Chiba, Aug. 2003. (Summary) 本稿では，聴取曲のジャンルを脳波で分類する手法を提案する．提案手法は，脳波計測部，脳波の特徴抽出部，聴取曲分類部で構成される．脳波は簡易脳波計を用いて計測する．脳波の特徴は，周波数分析された脳波の周波数成分間の相互関係を因子分析により分析し，定量化することで抽出する．聴取曲は抽出された脳波の特徴をニューラルネットワークに学習し，分類する．提案手法の有効性を検証するために，実験を実施した．その結果，90%以上の精度を得るに至った．
141.	Shin-ichi Ito, Yasue Mitsukura, Minoru Fukumi, Norio Akamatsu and Rajiv Khosla : The EEG Detection System Using the Factor Analysis and Neural Networks, Proc. of 7th World Multi-Conference on Systemics, Cybernetics and Informatics, Vol.IV, 330-333, Orlando, Jul. 2003. (Summary) 本国際会議論文は，音楽鑑賞時の脳波の解析を行うことを目的とし，遺伝的アルゴリズムによる特徴選択とニューラルネットワークによる識別を行う方法を提案している．特に必要な周波数成分を遺伝的アルゴリズムにより決定し，因子分析を適用して特徴抽出を行い，さらにニューラルネットワークにより学習することにより鑑賞している音楽を高速に分類する方法を提案している．数名の被験者から計測された脳波信号に対する計算機シミュレーションにより，本手法の有効性を定量的に検証している．
142.	Shin-ichi Ito, Yasue Mitsukura, Minoru Fukumi and Norio Akamatsu : A Feature Extraction of the EEG during Listening to the Music using the Factor Analysis and Neural Networks, Proc. of International Joint Conference on Neural Network, 2263-2267, Portland, Jul. 2003. (Summary) Recently in the world, the research of the electroencephalogram (EEG) interface is done, because it has the possibility to realize an interface that can be operated without special knowledge and technology by using the EEG as a means of the interface. As one of the EEG interface, as for a goal for the final of this research, the EEG control system by any music is constructed. However, the EEG control by music is very difficult because it does not know the music and the causal relation of the EEG clearly. Therefore, the EEG analysis and music analysis is absolutely imperative in this system. In this paper, the EEG analysis method by using the FA and the NN is proposed. The FA is used for extracting the characteristics data of the EEG. The NN is used for estimating extracted the characteristics data of the EEG. Moreover teacher signal data of the NN uses the data of the characteristics data of the music. The characteristics data of music is extracted by using the Bark scale analysis. Finally, in order to show the effectiveness of the proposed method, classifying the EEG pattern is done computer simulations. The EEG pattern is 4 conditions, which are listening to Rock music, Schmaltzy Japanese ballad music, Healing music, and Classical music.
143.	Shin-ichi Ito, Yasue Mitsukura, Minoru Fukumi, Norio Akamatsu and Rajiv Khosla : An EEG Feature Detection System Using the Neural Networks Based on Genetic Algorithms, Proc. of IEEE International Symposium on Computational Intelliegnce in Robotics and Automation, 1196-1200, Kobe, Jul. 2003. (Summary) In this paper, the EEG analysis method by using the GA, the FA and the NN is proposed. The GA is used for selecting the personal characteristics frequency components. The FA is used for extracting the characteristic data of the EEG. The NN is used for estimating extracted the characteristics data of the EEG. Finally, in order to show the effectiveness of the proposed method, EEG pattern was classified using computer simulations. The EEG pattern has 4 conditions, which are listening to rock music, Schmaltzy Japanese ballad music, healing music and classical music. The result, in the case of not using the personal characteristics frequency components, gave over 95% accuracy. This result of our experiment shows the effectiveness of the proposed method. (Link to Publication Site) ● Publication site (DOI): 10.1109/CIRA.2003.1222167 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-79959960178 (DOI: 10.1109/CIRA.2003.1222167, Elsevier: Scopus)

Proceeding of Domestic Conference:

1.	Naohiro Okubo, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : 筋電に基づく手首の疲労の検知と動作識別, Proceedings of 2023 Annual Conference of Electronics, Information and Systems Society, IEE of Japan, 1567-1570, Sep. 2023.
2.	Tohma Nakagawa, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Effective Feature Selection in Behavior Identification by EMG, Proceedings of 2023 Annual Conference of Electronics, Information and Systems Society, IEE of Japan, 1000-1002, Sep. 2023.
3.	Ryota Miyake, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : A Method to Estimate Presence or Absence of Learning Understanding Based on Analysis of EEG and HRV, Proceedings of 2023 Annual Conference of Electronics, Information and Systems Society, IEE of Japan, 983-987, Sep. 2023.
4.	Daiki Fujiwara, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Modeling of Safety Confirnmation Behavior at Intersections and Identifying Distracted State using Time Series Data Learning Methods, Proceedings of 2023 Annual Conference of Electronics, Information and Systems Society, IEE of Japan, 1017-1022, Sep. 2023.
5.	Kazuki Yoshinaga, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Identification of Learning Progress Based on Skeletal Information for Seated Learners, Proceedings of 2023 Annual Conference of Electronics, Information and Systems Society, IEE of Japan, 1011-1016, Sep. 2023.
6.	Ryo Otsuki, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Recognition of Kanji Characters by Aerial Input Using LeapMotion and Personal Authentication, Proceedings of 2023 Annual Conference of Electronics, Information and Systems Society, IEE of Japan, 1003-1006, Sep. 2023.
7.	Shoichiro Fujisawa, 西森翔矢, 熱田好古, 坂見健二, 森本滋郎, Junji Kawata, Yoshio Kaji, 樋口峰夫, Shin-ichi Ito and 稲垣具志 : Effect of Illumination Environment on Visibility of Blinking Light, Proceedings of the 2023 JSME Conference on Robotics and Mechatronics, 2P2-A261-2P2-A264, Jun. 2023. (Summary) Vision is said to account for 80% of the information acquired of the five human senses. The presentation of light is an effective way to convey visual information. A blinking presentation especially attracts attention. A low-vision person acquires peripheral information through residual vision. In this case, light effectively supports the travel of low-vision persons. A blinking light stimulates more than a continuous light. However, a blinking light is not sufficiently compatible with the surrounding environment. This research investigates the effectiveness of blinking lights under different illuminance. (Keyword) Person with visually impairment / Low vision / LED block / Visibility / Sensory
8.	Shin-ichi Ito : Human Assist System Based on Human Sensing, National Convention Record I.E.E. Japan, S15-1-S15-3, Mar. 2023. (Keyword) human / electroencephalogram / machine learning
9.	Katsumasa Nitta, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Estimation of Mood Change using Smartwatch for Depressive State Detection, 情報処理学会第85回全国大会講演論文集, 4-223-4-224, Mar. 2023. (Summary) 本稿では，スマートウォッチから取得した心拍数などの生体情報から抑うつ状態の推定を試みる．近年，うつ病患者の増加が深刻化している．病状が悪化する前に，まず自身がうつ病の前段階である抑うつ状態にあることを自覚させることで，適切な対応につなげられる可能性があると考えた．そこで本稿では，その基礎的な検討として，日常生活で簡便に利用可能なスマートウォッチで取得される情報を利用し，質問紙によるメンタルヘルス状態算出結果と合わせて解析を実施して，気分の変化と生体情報等との関連性を分析する．
10.	Shoichiro Fujisawa, 坂見健二, 西森翔矢, 熱田好古, 森本滋郎, Junji Kawata, Yoshio Kaji, 樋口峰夫 and Shin-ichi Ito : Development of Blinking Light to Assist Travel for Persons with Low Vision, 第23回システムインテグレーション部門講演会(SI2022), 1328-1332, Dec. 2022. (Summary) The purpose is to guide low vision by laying a blinking light-emitting block at the pedestrian crossing entrance. The stimulation intensity, visibility, and discomfort of the blinking stimulus, which presented the subject with a blinking light, were verified by sensory evaluation. In this verification, we will compare the effects when the environmental illuminance is changed. We consider the most suitable blinking method for assisting people with low vision.
11.	MANA Tahora, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Japanese Sign Language Identification Using Deep Learning with Leap Motion, 電気学会電子·情報·システム部門大会論文集, 1165-1169, Sep. 2022. (Keyword) Leap motion / 日本手話 / deep learning / machine learning / CNN
12.	𠮷川京汰, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Development of a smart glass based grilling evaluation system for biginners, 電気学会電子·情報·システム部門大会論文集, 628-632, Aug. 2022. (Keyword) 集約画像 / deep learning / 手話認識 / カラーリング
13.	畠中健斗, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Development of a smart glass based grilling evaluation system for biginners, 電気学会電子·情報·システム部門大会論文集, 617-622, Aug. 2022. (Keyword) 焼き加減 / smart glass / ヒストグラム / 料理 / 初心者
14.	小柳功王, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : A Method to Measure Respiration Rate in a Smartwatch Based Sleep Monitoring System, 電気学会電子·情報·システム部門大会論文集, 658-663, Aug. 2022. (Keyword) sleep / smartwatch / respiration / ピーク値 / 移動平均
15.	YUKI Saitoh, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : 手首筋電に基づくタッピング動作の認識, 人工知能学会全国大会, 1-4, Jun. 2022. (Keyword) deep learning / machine learning / 手首筋電
16.	Shin-ichi Ito, Miura Takanori, Momoyo Ito and Minoru Fukumi : A Method to Detect a Mood Matching Music Using EEG, 電気学会電子·情報·システム部門大会論文集, 601-604, Sep. 2021. (Keyword) electroencephalogram / mood / music / machine learning
17.	Shin-ichi Ito, Hironori Kadowaki, Momoyo Ito and Minoru Fukumi : Personal Authentication with Walking Motion Based on Gathered Images and Neural Networks, 電気学会電子·情報·システム部門大会論文集, 512-515, Sep. 2021. (Keyword) personal authentication with walking motion / gathered image / deep learning / convolutional neural networks
18.	Ren Nozaki, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : 脳波を用いた面倒な作業に対する感情の検出, 電気学会電子·情報·システム部門大会論文集, 644-648, Sep. 2021. (Keyword) electroencephalogram / emotion / ブレインマシンインタフェース / machine learning / 面倒 / 条件付き刺激
19.	Takeru Oda, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : カフェインの影響を考慮した脳波個人認証の検討, 電気学会電子·情報·システム部門大会論文集, 639-643, Sep. 2021. (Keyword) electroencephalogram / ブレインマシンインタフェース / カフェイン / machine learning / 多数決 / 個人認証
20.	Taiga Sogame, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : 脳波による掌握・発声動作想起を用いた言語選択の提案, 電気学会電子·情報·システム部門大会論文集, 623-627, Sep. 2021. (Keyword) electroencephalogram / k近傍法 / 律動 / 事象関連電位 / 閉じ込め症候群 / 運動想起
21.	Masahiro Asahara, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : 深層学習を用いたてんかん波検出, 電気学会電子·情報·システム部門大会論文集, 617-622, Sep. 2021. (Keyword) てんかん / deep learning / 脳波判読 / 転移学習 / 発作間欠期脳波
22.	Shoya Nakajima, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : サッカー選手の自動的評価のためのスペースの可視化, 電気学会電子·情報·システム部門大会論文集, 535-539, Sep. 2021. (Keyword) image processing / human interface / machine learning / 情報可視化 / サッカー
23.	Junpei Fusanobori, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Kinectを用いた歩行動作に基づく個人認証, 電気学会電子·情報·システム部門大会論文集, 528-531, Sep. 2021. (Keyword) 個人認証 / Kinect / 歩行動作
24.	Ryo Karamon, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : 眼鏡型カメラを用いた夜間及び暗所における視覚障がい者の視覚的支援システムの検討, 電気学会電子·情報·システム部門大会論文集, 518-523, Sep. 2021. (Keyword) deep learning / 点字ブロック / YOLOv3
25.	Hiroki Yano, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : 防犯カメラを用いたひったくり犯の検出・追跡, 電気学会電子·情報·システム部門大会論文集, 889-891, Sep. 2021. (Keyword) deep learning / machine learning / 引ったくり
26.	Ryota Wabuchi, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : 深層学習を用いた画像識別による不良品検知システムの開発, 電気学会電子·情報·システム部門大会論文集, 906-911, Sep. 2021. (Keyword) deep learning / machine learning / 不良品検知
27.	Takeru Mutoh, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : 物体検出アルゴリズムを用いた廃棄物識別システム, 電気学会分野横断型新システム創成研究会論文集, 1-5, Jan. 2021. (Summary) 現在，多くのリサイクルセンターでは，ペットボトル，缶類，ガラス瓶などといった再資源化可能な大量の廃棄物が送出されている．本研究では，分別作業における負担軽減を目的とし，物体検出ネットワークと回転角算出ネットワークを連結およびデータ拡張手法を多段的に用いて実環境に最適化した合成画像を生成することで，高精度かつリアルタイムで物体認識及び回転角を算出するモデルを構築した．特に，データ拡張方法を工夫することにより学習に利用できる画像枚数を増加させ，物体識別と回転角度推定の精度を改善する． (Keyword) machine learning / deep learning / 廃棄物識別 / データ拡張 / 物体検出
28.	Takeru Mutoh, Minoru Fukumi, Shin-ichi Ito and Momoyo Ito : 物体検出アルゴリズムを用いた廃棄物識別システム, ViEW2020講演論文集, 1-7, Dec. 2020. (Summary) 現在，多くのリサイクルセンターでは，ペットボトル，缶類，ガラス瓶などといった再資源化可能な大量の廃棄物が送出されている．本研究では，分別作業における負担軽減を目的とし，物体検出ネットワークと回転角算出ネットワークを連結およびデータ拡張手法を多段的に用いて実環境に最適化した合成画像を生成することで，高精度かつリアルタイムで物体認識及び回転角を算出するモデルを構築した． (Keyword) 物体検出 / deep learning / 廃棄物識別 / データ拡張
29.	Shin-ichi Ito : 意思伝達BCIのための脳活動分析と個人差, 電気学会, Jul. 2020. (Keyword) electroencephalogram / ブレインコンピュータインタフェース / 脳活動 / 個人差
30.	CHUNYU GUO, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Personal authentication by walking motion using Kinect, 電気学会電子·情報·システム部門大会論文集, 719-721, Sep. 2019. (Keyword) human sensing / machine learning / Kinect
31.	Shan Xian, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Study on Discrimination of Finger Motions Based on EMG Signals, 電気学会電子·情報·システム部門大会論文集, 715-718, Sep. 2019. (Keyword) human sensing / machine learning / electromyogram activity
32.	Takeru Wasaki, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Direction Discrimination by Vehicle Lamp, 電気学会電子·情報·システム部門大会論文集, 861-865, Sep. 2019. (Keyword) machine learning / Vehicle Lamp
33.	Kazuki Shimamoto, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Nail Image Analysis Method for Accumulation Stress Evaluation, 電気学会電子·情報·システム部門大会論文集, 855-860, Sep. 2019. (Keyword) human sensing / Nail image / Accumulation Stress
34.	Takuya Kimura, Momoyo Ito, 佐藤和人, Shin-ichi Ito and Minoru Fukumi : Estimation of Moving Direction of Objects in Driving Scene, 電気学会電子·情報·システム部門大会論文集, 1374-1377, Sep. 2018. (Keyword) 移動物体識別 / 運転シーン / machine learning / 単眼カメラ
35.	Tomoyuki Sakabe, Momoyo Ito, 佐藤和人, Shin-ichi Ito and Minoru Fukumi : Identification of Driver Status based on Difference of Visibility using Sparse Structure Learning, 電気学会電子·情報·システム部門大会論文集, 1368-1373, Sep. 2018. (Keyword) ドライバー状態 / スパース構造学習 / 視認性
36.	Teru Ando, Minoru Fukumi, Momoyo Ito and Shin-ichi Ito : Persona Identification in monitoring system using Kinect, 電気学会電子·情報·システム部門大会論文集, 1342-1345, Sep. 2018. (Keyword) human sensing / machine learning / Kinect
37.	Yurika Fujii, Minoru Fukumi, Momoyo Ito and Shin-ichi Ito : Detection of Dangerous Objects by Pan-tilt Camera, 電気学会電子·情報·システム部門大会論文集, 1516-1517, Sep. 2018. (Keyword) 危険物検出 / パンチルトカメラ / machine learning
38.	Shion Morikawa, Minoru Fukumi, Momoyo Ito and Shin-ichi Ito : Personal Authentication Using lip EMG by Dry Electrode, 電気学会電子·情報·システム部門大会論文集, 1571-1572, Sep. 2018. (Keyword) electromyogram activity / machine learning / Deep learning
39.	Shun Yamamoto, Shin-ichi Ito, Momoyo Ito, Minoru Fukumi and Kamat Rahayu Binti Seri : Recognition of Aerial Numerals by Leap Motion and CNN, National Convention Record I.E.E. Japan, 173, Mar. 2018. (Keyword) neural network / Leap Motion / CNN / human sensing
40.	Shion Morikawa, Minoru Fukumi, Momoyo Ito, Shin-ichi Ito and Kamat Rahayu Binti Seri : Personal Authentication Using EMG by Dry Electrodes, National Convention Record I.E.E. Japan, 185, Mar. 2018. (Keyword) electromyogram activity / SVM / human sensing
41.	Ryosuke Takabatake, Minoru Fukumi, Momoyo Ito, Shin-ichi Ito and Kamat Rahayu Binti Seri : Dataset making for Japanese Vowels Recognition using Surface electromyogram measured with Bipolar Dry Type Sensors, National Convention Record I.E.E. Japan, 180-181, Mar. 2018. (Keyword) electromyogram activity / SVM
42.	Konomi Takeyasu, Momoyo Ito, Kazuhito Sato, Shin-ichi Ito and Minoru Fukumi : Detection of Inattentive State Based on Change of Driving Behavior: Study on Driving Behavior Model using GGM, Conference record of HUMAN COMMUNICATION GROUP SYMPOSIUM 2017, HCG2017-I-1-3, Dec. 2017. (Summary) In recent years traffic fatality accidents caused by accidental driving are increasing. If it is possible to detect an inattentive state, not only reduction of traffic fatal accidents but also state monitoring of drivers for takeover in automatic operation is realized. In this research, we model driving behavior measured by a driving simulator based on GGM and sparse structural model. In addition, we discus usefulness of the generated driving behavior model.
43.	Tomoyuki Sakabe, Momoyo Ito, Kazuhito Sato, Shin-ichi Ito and Minoru Fukumi : Analysis of Inattentive State by Driving Information using Sparse Structure Learning: Consideration Based on Difference in Visibility, Conference record of HUMAN COMMUNICATION GROUP SYMPOSIUM 2017, HCG2017-C-3-3, Dec. 2017. (Summary) The aimless driving is one of the most common cause of the traffic accidents. If we can detect a change of driver's behavior according to the aimless driving, the detection will achieve prevention of traffic accidents and safety takeover of autonomous driving. In this paper, we analyze a deference between normal condition and distracted condition by using sparse structure learning based on correlation of driver's information. We use eye gaze, face direction, heartbeat, vehicle velocity, acceleration, and tilt of handle. We analyze the change of driving information according to the visibility.
44.	Shoichiro Fujisawa, 三原佑太, Shin-ichi Ito and Katsuya SATO : VR技術を用いた視覚障害体験システムの開発, 第22回日本バーチャルリアリティ学会大会論文集, No.1B3-06, 1-4, Sep. 2017. (Summary) 我が国の視覚障害者数は164万人に上る．そのうちの144万9千人がロービジョン者であり, 緑内障, 糖尿病網膜症, 加齢性黄斑変性, 色弱等の症状を抱えている．そこで本研究では, ステレオカメラを内蔵したヘッドマウントディスプレイを用いて多種多様な視覚障害の症例を体験できるシステムを構築した．カメラから取り込んだ画像にPC上で画像処理を行い, ヘッドマウントディスプレイに出力することで様々な視覚障害の症状を再現する．特に本報ではシステム開発に向けた第一歩として色弱の見え方を再現することを目指した． (Keyword) 色覚異常 / ユニバーサルデザイン
45.	Tanaka Tomoya, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : A Method to Evaluate Stress Level by Using Expression Analysis, 電気学会電子情報システム部門大会論文集, 1136-1139, Sep. 2017. (Keyword) 表情分析 / ストレス評価 / アミラーゼ
46.	Omae Hisaki, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : A Method of Evaluate Understanding of Learning Using Electroencephalogram, 電気学会電子情報システム部門大会論文集, 464-467, Sep. 2017. (Keyword) 脳波 / 信号処理 / 簡易脳波計 / 学習理解
47.	Higasa Takashi, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Character Input System using Gesture Motion in Augmented Reality, 電気学会電子·情報·システム部門大会論文集, 1683-1684, Sep. 2017. (Keyword) Augmented Reality / HSV color system / Gesture motion / Character string detection / Optical character reader
48.	Teru Ando, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Respiration detection in monitoring system using Kinect, 電気学会電子情報システム部門大会論文集, No.PS3-7, 1561-1562, Sep. 2017. (Keyword) Kinect / SVM
49.	劉楠, Shin-ichi Ito, Katsuya SATO and Shoichiro Fujisawa : 視覚障害者のための階段昇降支援ロボットの開発, 平成29年電気学会電子・情報・システム部門大会講演論文集, 529-531, Sep. 2017.
50.	萬代蘭丸, Ryota Kurozumi, Shin-ichi Ito, Katsuya SATO and Shoichiro Fujisawa : Recognition of Visually Impaired Person with Deep Learning, 平成29年電気学会電子・情報・システム部門大会講演論文集, 523-528, Sep. 2017. (Summary) The purpose of this research is to identify the visually impaired person by learning the still picture of walking of a visually impaired person and a healthy person by the deep learning. The still picture of walking has taken out the still picture of animation from side from on. The identify rate of 99.9% every 2000 image numbers and 2 times of the drop number was obtained. (Keyword) deep learning / Visually Impaired Person / Blind Person / Independent Travel
51.	Ryouhei Shioji, Minoru Fukumi, Momoyo Ito and Shin-ichi Ito : Personal Authentication Based on Wrist EMG Analysis by a Convolutional Neural Network, 電気学会情報システム研究会, Vol.IS-17, 57-61, Aug. 2017. (Summary) 手首で計測された筋肉電位を用いて個人認証を高精度に行ったことの報告である．特に，畳み込み型の深層学習ネットワークを用いることにより，ジャンケン動作に伴う筋電に基づいて個人認証を行う方法を提案し，計算機シミュレーションにより高精度認証が可能であることを定量的に評価している． (Keyword) electromyogram activity / CNN
52.	Ryousuke Takabatake, Minoru Fukumi, Shin-ichi Ito and Momoyo Ito : Japanese Vowel Recognition Using Surface Electromyogram Measured with Bopolar Dry type Sensors, 電気学会情報システム研究会, Vol.IS-17, 23-27, Aug. 2017. (Keyword) electromyogram activity / SVM
53.	永濱秀明, Shoichiro Fujisawa, Katsuya SATO, Shin-ichi Ito, 稲垣具志, 池田典弘, 竹内聖人, 荻原弘 and 高橋和哉 : 方向定位付きLED発光ブロックの全盲者向け敷設位置の検証, ロボティクス・メカトロニクス講演会2017講演論文集, 2A2-M03(1)-2A2-M03(4), May 2017.
54.	永濱秀明, Shoichiro Fujisawa, Katsuya SATO, Shin-ichi Ito, 清山幹弘, 稲垣具志, 池田典弘, 竹内聖人, 荻野弘 and 高橋和哉 : 視覚障害者用方向定位付きLED 発光ブロックの実証実験, 第17回計測自動制御学会システムインテグレーション部門講演会(SI2016), 2822-2825, Dec. 2016.
55.	石橋樹, Shoichiro Fujisawa, Katsuya SATO and Shin-ichi Ito : 視覚障害者誘導用ブロックの方向定位性能の定量的評価, 第17回計測自動制御学会システムインテグレーション部門講演会(SI2016), 2819-2821, Dec. 2016.
56.	Momoyo Ito, Kazuhito Sato, Shin-ichi Ito and Minoru Fukumi : Safety Confirmation Behavior Analysis Based on Driver's Posture Change, 日本機械学会第25回交通・物流部門大会(TRANSLOG2016)講演論文集/ 1306, Nov. 2016.
57.	Taiki Nonoguchi, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : A System to Detect and Track Mosquitoes, 平成28年度電気学会電子・情報・システム部門大会講演論文, 412-415, Sep. 2016. (Keyword) Mosquito
58.	石橋樹, Shoichiro Fujisawa, Katsuya SATO and Shin-ichi Ito : Quantitative Evaluation of Orientation Performance of Tactile Walking Surface Indicators for the Blind, 2016 JSME Conference on Robotics and Mechatronics, 2P1-03a4(1)-2P1-03a4(3), Jun. 2016.
59.	永濱秀明, Shoichiro Fujisawa, Katsuya SATO, Shin-ichi Ito, 清山幹弘, 稲垣具志, 池田典弘, 竹内聖人, 荻野弘 and 高橋和哉 : Proof experiment of LED block equipped with projections to locate travel direction for blind and vision impaired persons, 2016 JSME Conference on Robotics and Mechatronics, 2P1-03a3(1)-2P1-03a3(3), Jun. 2016. (Summary) Crosswalk entrances are on the boundary with the roadway and are among the dangerous areas for visually impaired persons. Tactile walking surface indicators (TWSIs) are installed on the road to support independent travel for visually impaired persons. We developed an LED block equipped with projections to strengthen the support of the crosswalk entrance. Visually impaired persons found the LED block equipped with projections to locate travel direction to effectively support crossing. The effectiveness of this block is described from the measurement date and the questionnaire results. (Keyword) visual impaired person / crosswalk / TWSIs / LED block
60.	Daiki Konishi, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : 空中署名と深層学習によるバイオメトリクス認証, DIA'2016, IS2-A10-1-IS2-A10-7, Mar. 2016. (Summary) 本稿では，空中署名によるバイオメトリクス認証についての研究成果を報告する．署名の計測にはLeap Motion Controller を使用する．そして，畳み込みニューラルネットワークによる深層学習により，署名データからの特徴抽出と識別を行う．
61.	Takuma Ogawa, 平岡大樹, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : パンチルトカメラを用いたオンライン放置物検出, DIA'2016, ISC-C10-1-ISC-C10-8, Mar. 2016. (Summary) 本稿ではパンチルトカメラを用いたオンライン放置物検出の手法を提案する．提案手法として，まずパンチルトカメラで動画像の取得を行う．その動画像に対して放置物があると考えられる対象領域の抽出をST-Patch 特徴量を用いて行う．そして抽出された対象領域に対して放置物の存在を確かめる物体識別を行い，放置物の検出を行う．提案手法の有効性を検証するために複数の場面を想定した実験を行う．
62.	Ryosuke Naitoh, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : 画像特徴量とサポートベクターマシンを用いた運転シーン分類に関する検討, DIA'2016, ISC-C8-1-ISC-C8-4, Mar. 2016. (Summary) 交通事故の主要因の一つは漫然運転であり，発生箇所が最も多いのは交差点である．ドライバが漫然運転をしている時，内部状態(注意散漫状態，焦り，眠気など)に変化があると考えられる．また，内部状態は車両前方の情景(運転シーン)の影響を受けると考えられる．つまり，運転行動を解析するためには運転シーンがどのような状況であるかを理解する必要がある．そこで，本研究では運転シーンの理解を目的とし，画像特徴量を用いた運転シーン分類について検討する．具体的には，運転シーン画像をBag of Keypoints(BoK)により表現し，分類器にサポートベクターマシン(SVM)を用いて直線区間と交差点を分類した．その結果，78.2%の精度で分類可能であった．
63.	Kenta NUNOBIKI, Ryo FUKUOKA, Kazuyuki MINAMI, Shin-ichi Ito, Shoichiro Fujisawa and Katsuya SATO : Measurement of intracellular strain distribution by the real time observation of stretched cells, 第28回バイオエンジニアリング講演会講演論文集, 2D13, Jan. 2016.
64.	Manabu KATAYAMA, Ryo FUKUOKA, Kazuyuki MINAMI, Shin-ichi Ito, Shoichiro Fujisawa and Katsuya SATO : In situ observation of intracellular calcium singaling response to stretch with video rate temporal resolution, 第28回バイオエンジニアリング講演会講演論文集, 2D12, Jan. 2016.
65.	糸永昇平, Shoichiro Fujisawa, Katsuya SATO, Shin-ichi Ito and 上田喜敏 : 介助作業における腰部負担評価に関する研究, 第16回システムインテグレーション部門講演会, 99-101, Dec. 2015.
66.	石橋樹, 吉村祥吾, Shoichiro Fujisawa, Katsuya SATO, Shin-ichi Ito and 前野哲哉 : 知的・精神障害者の作業分析に関する研究, 第16回システムインテグレーション部門講演会, 96-98, Dec. 2015.
67.	中島紘一, Shin-ichi Ito, Katsuya SATO and Shoichiro Fujisawa : 重度障害者を対象とした意思伝達装置に関する研究, 第16回システムインテグレーション部門講演会, 93-95, Dec. 2015.
68.	Hiroshi Aoki, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Pose Estimation of Hand for AR, 平成27年度電気関係学会四国支部連合大会講演論文集, 188, Sep. 2015.
69.	Takahiro Toyokawa, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Relevance Analysis of Driver's Gaze and Gaze Object, 平成27年度電気関係学会四国支部連合大会講演論文集, 239, Sep. 2015.
70.	Oki Matsubara, Eiji Hase, Takeshi Yasui, Shin-ichi Ito, Shoichiro Fujisawa and Katsuya SATO : 経時的その場観察における繰り返し伸縮刺激下の骨芽細胞産生コラーゲン評価, 日本機械学会2015年度年次大会, S0210102, Sep. 2015.
71.	Souma YAMAMOTO, Ryo FUKUOKA, Kazuyuki MINAMI, Shin-ichi Ito, Shoichiro Fujisawa and Katsuya SATO : Observation of intracellular Ca2+ signaling response to cyclic stretch in osteoblastic cells, 日本機械学会第26回バイオフロンティア講演会講演論文集, Sep. 2015.
72.	Koki TAKAHASHI, Ryo FUKUOKA, Kazuyuki MINAMI, Shin-ichi Ito, Shoichiro Fujisawa and Katsuya SATO : Time lapse imaging of cellular morphology and intracellular strain distribution in cycliccaly stretched osteoblastic cell, 日本機械学会第26回バイオフロンティア講演会講演論文集, Sep. 2015.
73.	syu Tamura, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : A Method to Best Music Selection System by EEG, 平成27年度電気学会電子・情報・システム部門大会講演論文, 379-384, Aug. 2015. (Keyword) music analysis / spectrum analysis / Fourier transform
74.	Takuma Ogawa, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Improvement of ST-Patch Features for Detection of Abandaned Object, 平成27年度電気学会電子・情報・システム部門大会講演論文, 1490-1491, Aug. 2015. (Keyword) pan-tilt camera / ST-Patch feature / HOG feature / Real AdaBoost
75.	Daiki Hiraoka, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Discrimination of Japanese Janken by Support Vector Machine Based on Electromyogram of Wrist, 平成27年度電気学会電子・情報・システム部門大会講演論文, 1514-1515, Aug. 2015. (Keyword) electromyogram activity / suppor vector machine / biometrics
76.	Shoichiro Fujisawa, Katsuya SATO, Shin-ichi Ito, 清山幹弘, 稲垣具志, 池田典弘 and 高橋和哉 : 横断歩道口に敷設した発光ブロックの視認性, ロボティクス・メカトロニクス講演会2015講演論文集, No.1P2-D08, 1-3, May 2015. (Keyword) 視覚障害者誘導用ブロック / 視覚障害者 / 支援機器 / LEDブロック / 交差点
77.	松原央樹, Shin-ichi Ito, Shoichiro Fujisawa and Katsuya SATO : Time lapse in situ observation and long-term culturing of osteoblastic cells under cyclic stretch, 日本機械学会中国四国学生会第45 回学生員卒業研究発表講演会CD-ROM, Mar. 2015.
78.	山本蒼馬, 宮崎裕孝, 福岡諒, 南和幸, Shin-ichi Ito, Shoichiro Fujisawa and Katsuya SATO : Real time in situ observation of intracellular Ca2+ signaling response to cyclic stretch in osteoblastic cell, 日本機械学会中国四国学生会第45 回学生員卒業研究発表講演会CD-ROM, Mar. 2015.
79.	Ogawa Yuki, Miyazaki Hirotaka, Fukuoka Ryo, Minami Kazuyuki, Shin-ichi Ito, Shoichiro Fujisawa and Katsuya SATO : Osteoblastic calsium signaling response to different magnitude of stretching stimuli, Procddeings of the 27th Bioengineering conference 2014 annual meeting of BED JSME, 113-114, Jan. 2015.
80.	田辺猛, Shoichiro Fujisawa, Katsuya SATO and Shin-ichi Ito : A study on road traffic noise reproduction by wave field synthesis, 第15回計測自動制御学会システムインテグレーション部門講演会, 2686-2689, Dec. 2014.
81.	大嶋昇, Shoichiro Fujisawa, Katsuya SATO, Shin-ichi Ito and Masayuki Booka : Research of the oscillating reduction effect due to tire pressure during wheelchair acceleration running, 第15回計測自動制御学会システムインテグレーション部門講演会, 2677-2679, Dec. 2014.
82.	清山幹弘, 志賀大輔, Shoichiro Fujisawa, Katsuya SATO, Shin-ichi Ito, 稲垣具志, 池田典弘 and 高橋和哉 : Research on identifying LED blocks mounted on crosswalk boundaries, 第15回計測自動制御学会システムインテグレーション部門講演会, 2671-2674, Dec. 2014.
83.	Kenta Nunobiki, 宮崎裕孝, 福岡諒, 南和幸, Shin-ichi Ito, Shoichiro Fujisawa and Katsuya SATO : Measurement of intracellular strain distribution in single cell level under tensile, Proceedings of the 25th JSME conference on frontiers in bioengineering, 43-44, Oct. 2014.
84.	Manabu Katayama, 宮崎裕孝, 福岡諒, 南和幸, Shin-ichi Ito, Shoichiro Fujisawa and Katsuya SATO : Observation of calcium signaling response to stretching in osteoblastic cells using fluorescence ratiometry method, Proceedings of the 25th JSME conference on frontiers in bioengineering, 41-42, Oct. 2014.
85.	Keiichi Tsuzuki, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Basic Study for Categorization of Driving Scene using Deep Learning, Proceedings of 2014 Shikoku-Section Joint Convention Record of the Institute of Electrical and Related Engineers, 185, Sep. 2014. (Summary) 本研究では，危険運転に繋がる運転行動を解析するために，車両前方の情景の時系列変化を運転シーンと定義し，運転シーンの意味付けのための分類を目的としている．本稿では，実車の運転シーンにおける直進クラスと交差点クラスの分類にDeep Learningを採用し，2クラスの分類における有効性を検討する．Deep Belief Nets(DBN)と誤差逆伝播法との比較実験を行った結果，DBNの方が識別率の高いことが明らかとなった．
86.	Ryosuke Oka, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Extraction of Safety Confirmation Behavior Based on Drivers Posture, Proceedings of 2014 Shikoku-Section Joint Convention Record of the Institute of Electrical and Related Engineers, 184, Sep. 2014. (Summary) 本研究では，ドライバが安全確認を行っている区間を対象とした実験や解析を可能とするために，見通しの悪い無信号交差点における，ドライバの安全確認行動区間の抽出を行う．実験では，安全確認行動区間の抽出に用いるドライバの姿勢変化に着目した特徴量の検討を行った．その結果，ドライバを包含する領域の画像を粗視化したデータを用いる有用性が示唆された．
87.	Shin-ichi Ito, Momoyo Ito, Shoichiro Fujisawa and Minoru Fukumi : Method for EEG Pattern Classification Considering Human Character, 平成26年電気学会電子・情報・システム部門大会講演論文集, 637-640, Sep. 2014.
88.	Shin-ichi Ito : EEG Analysis Method Based on Individual Differences and Uncertainties of EEG Activities, 平成26年電気学会産業応用部門大会, シンポジウム, Vol.2 S13 2, No.2, 51-54, Aug. 2014.
89.	Akiko Sugiyama, Momoyo Ito, Kazuhito Sato, Shin-ichi Ito and Minoru Fukumi : Difference Analysis of Safety Verification Behavior Based on Near-Miss Event Using Head Motion, Proceedings of 2014 JSAE Annual Congress (Spring), Vol.11-14, 13-18, May 2014. (Summary) 本稿では，安全確認行動のどのような頭部姿勢変化に注意散漫状態推定のための情報が含まれているかを，安全確認行動時の頭部の位置情報を2次元平面上に投影し，その座標値の統計量から解析する．特に，ヒヤリハットイベントの有無による変化について考察し，注意資源と頭部の軌跡との関係について議論する．
90.	Momoyo Ito, Kazuhito Sato, Shin-ichi Ito and Minoru Fukumi : Influence Analysis of Near-Miss Event on Change of Safety Verification Behavior, Proceedings of 2014 JSAE Annual Congress (Spring), Vol.12-14, 7-10, May 2014. (Summary) 本稿では，これまでに提案した2 種類の教師なしニューラルネットワークをハイブリッド化した頭部姿勢定量化手法を用い，ヒヤリハットイベントが安全確認行動の時系列変化に与える影響を解析する．特に，ドライビングシミュレータ環境において，ヒヤリハットが生じなかった場合と生じた場合の安全確認行動時の頭部姿勢カテゴライズ結果について考察する．
91.	Yashuyuki Ishihara, Shoichiro Fujisawa, Katsuya SATO and Shin-ichi Ito : 内外反変形をダイナミックに矯正可能な装具の開発, 第14回計測自動制御学会システムインテグレーション部門講演会講演論文集, 2073-2075, Dec. 2013.
92.	池田典弘, 高橋和哉, 木村有希, 稲垣具志, Shoichiro Fujisawa, Katsuya SATO, Shin-ichi Ito and 志賀大輔 : 横断歩道口用発光ブロックの認識に関する研究, 第14回計測自動制御学会システムインテグレーション部門講演会講演論文集, 2027-2029, Dec. 2013.
93.	廣野恭平, Shin-ichi Ito, Katsuya SATO and Shoichiro Fujisawa : 視覚障害者のための交差点周辺における移動支援機器間の連続性に関する研究, 第14回計測自動制御学会システムインテグレーション部門講演会講演論文集, 2023-2026, Dec. 2013.
94.	Yusuke Yamamura, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : 足首の筋電による足首の動作の分類, 日本生体医工学会中国四国支部大会, 11, Oct. 2013.
95.	Zhang Peng, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : LVQとEOG法を用いる眼電マウスの開発, 日本生体医工学会中国四国支部大会, 34, Oct. 2013.
96.	Katsuya SATO, 門司亮, 中島雄太, 南和幸, Shin-ichi Ito and Shoichiro Fujisawa : ストレッチ刺激に対する培養骨芽細胞のカルシウム応答のその場観察, 第36回日本生体医工学会中国四国支部大会講演抄録, 20, Oct. 2013.
97.	Takanori Suzuki, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Detection of Drivers Distraction based on Sparse Structure Learning, 平成25年度電気関係学会四国支部連合大会講演論文集, 219, Sep. 2013. (Summary) 本稿では，スパース構造学習による運転行動からの異常値検出について提案した．今回提案した手法を用いることで，頭部姿勢，顔向き，視線の相対的な時系列変化の相関関係を求めることができ，各データ単体のデータを見ただけでは検出されない異常を相関関係から求めることが可能となる．これによりドライバの運転行動の組み合わせからドライバの内部状態の推定を行うことができ，ドライバの運転行動の癖(注意散漫状態時)を推定できる可能性がある．
98.	Taito Mori, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Extraction of Gaze Targets from Driving Scene using Saliency Map, 平成25年度電気関係学会四国支部連合大会講演論文集, 217, Sep. 2013. (Summary) 本研究では，注視対象物と定義した自動車，二輪転歩行者の道路での相対的な関係をモデル化するために，運転シーンにおける注視対象物の領域抽出を目的とする．具体的には，人間の視覚特性を模した顕著マップ上での注視対象の抽出についての検討を行う．
99.	Kazuya Yaegashi, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : An Analysis of Changes between Biological Information and Driving Behavior from Traffic Event, 平成25年度電気関係学会四国支部連合大会講演論文集, 221, Sep. 2013. (Summary) 本研究では，予防安全の立場から，ドライバの交差点での安全確認行動に着目し，平常状態からの逸脱信号を検出するシステムの構築を目指している．システムの構築に当たっては，運転者の精神状態の変化と運転動作の変化についての関連性を調査する必要がある．ドライバの安全確認行動と，人間の精神的負荷に影響を受けるとされている生体信号との関連性を調査する．
100.	Koji Miyai, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Feature Extraction of Car Front View for Categorization of Driver Scene, 平成25年度電気関係学会四国支部連合大会講演論文集, 216, Sep. 2013. (Summary) 本研究では，顕著性マップによる注視対象領域の特定後，注視対象物の局所特徴量を検出することを目的とする．本稿では，顕著性マップにより得られた注視対象領域に対するSIFTの抽出結果について評価する．
101.	Akiko Sugiyama, Momoyo Ito, Shin-ichi Ito and Minoru Fukumi : Evaluation of Scenarios on Driving Simulator for Analysis of Safety Verification, 平成25年度電気関係学会四国支部連合大会講演論文集, 222, Sep. 2013. (Summary) 本研究では生体信号の中でも心拍に注目する．心拍と運転行動の関係を分析し，それを根拠として用いることで内部状態の変化が運転行動へ及ぼす影響を明らかにする．ドライビングシミュレータ上でヒヤリハットイベントを発生させ，見通しの悪い信号機のない交差点での，ドライバの心拍変化と安全確認行動の変化の関係を分析する．本稿では，安全確認行動分析の予備実験として，発生させるイベントが運転行動に与える影響を調査するため，シナリオの評価を行う．
102.	倉本展尚, Shin-ichi Ito, Katsuya SATO and Shoichiro Fujisawa : 随意運動における脳活動のメカニズム, 平成25年度電気学会電子・情報・システム部門大会講演論文CD-ROM, 1101-1106, Sep. 2013.
103.	Shoichiro Fujisawa, 前野哲哉, 小川武紘, 石本晃大, Shin-ichi Ito and Katsuya SATO : Job Evaluation Method in Group Work in Mentally Retared Persons/Mental Disorder Persons, 日本機械学会2013年度年次大会講演論文集, No.13-1, 1-5, Sep. 2013. (Summary) The percentage of company employment as advance into society in the intellectually/mentally disabled is almost the same as that in the physically disabled, and moreover is getting higher every year. The first objective is to analyze the characteristics of the intellectually/mentally disabled more deeply in order to help the government and companies to understand them and the employment rate to increase. The second objective is to review the work evaluation method of the intellectually/mentally disabled anew through this evaluation in order to trigger the research on the intellectually/mentally disabled to be activated. The result of the verification performed by this research has showed that the success or failure in a continuous company employment is more significantly related to the factors of "surrounding healthy persons" which are external factors than the factors arising from the disability of a disabled person. According to the result of this study, if they work with healthy persons in the same place, their job performance actually decreases. This shows that disabled person's own willingness to work and job performance ability is not much influenced by individual disability characteristics, but they are very likely to be significantly influenced by whether the human environment around them in the workplace is good or not. (Keyword) Environment in the workplace / healthy persons and disabled persons / Analysis of the movement / Support of the working
104.	Takahiro Horiuchi, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : 遺伝的アルゴリズムを用いたパノラマ画像の生成, 電気学会電子·情報·システム部門大会論文集, OS10-3, Sep. 2013.
105.	Peng Zhang, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Recognition of Continuous Eye Motions Using Learning Vector Quantization and EOG-feature Based Methods, 電気学会電子·情報·システム部門大会論文集, GS12-2, Sep. 2013.
106.	Takako Ikuno, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Abandoned Object Detection by Genetic Algorithm with Local Search, 電気学会電子·情報·システム部門大会論文集, OS4-6, Sep. 2013.
107.	木村有希, Shoichiro Fujisawa, Katsuya SATO, Shin-ichi Ito, 高橋和哉, 池田典弘 and 荻野弘 : 高齢者・視覚障がい者のための横断歩道口発光ブロックの検証, 日本福祉のまちづくり学会第16回全国大会, No.1A-1, 1-2, Aug. 2013.
108.	加地純也, 廣野恭平, Shin-ichi Ito, Katsuya SATO, Junichi Hino and Shoichiro Fujisawa : 座圧分散クッションを使用した車椅子走行時の振動伝達特性と乗り心地評価, ロボティクス・メカトロニクス講演会2013講演論文集, No.2A2_C01, 1-2, May 2013. (Summary) In this study, we evaluated riding comfortableness of the attendant propelled wheelchair when using pressure redistributing cushions. Three types, a form type, a gel type and an air type cushions were respectively tested. The vibration propagating characteristics of these cushions were evaluated by measuring vibration of the dummy weight which was put on the pressure redistributing cushion during wheelchair running on tactile surface indicator road. In addition, a sensory test was conducted to compare the comfortableness of these cushions. Vibration wave form was analyzed by FFT, and sensory test was conducted by using questionnaire for subjects riding on the wheelchair with pressure redistributing cushion. As the result, the air type cushion indicated highest vibration damping performance, and the gel type was second, the form type cushion was third, respectively. The total result of the sensory test questionnaire correlated with the vibration damping performance. The cushion which has higher vibration damping performance provides better riding comfortableness to the occupant. (Keyword) Wheelchair / Pressure Redistributing Cushion / Riding Comfortableness / Vibration Measurement
109.	Katsuya SATO, 門司亮, 中島雄太, 南和幸, Shin-ichi Ito and Shoichiro Fujisawa : In situ observation of calcium signaling response to stretch in osteoblast, 第25回バイオエンジニアリング講演会講演論文集, 449-450, Jan. 2013.
110.	Shin-ichi Ito, Katsuya SATO, Shoichiro Fujisawa and Minoru Fukumi : 心理学的個人差を導入した脳波パターン分類, 第13回計測自動制御学会システムインテグレーション部門講演会論文集, 2660-2663, Dec. 2012.
111.	宮崎達也, Shin-ichi Ito, Katsuya SATO and Shoichiro Fujisawa : 視覚障害者のための交差点周辺における移動支援機器間の連続性に関する研究, 第13回計測自動制御学会システムインテグレーション部門講演会論文集, 2638-2639, Dec. 2012.
112.	上田喜敏, Shin-ichi Ito, Katsuya SATO and Shoichiro Fujisawa : 介助作業実態分析から考えられるベッドでの安全な患者/利用者介助に関する人間工学的手法の研究, 第13回計測自動制御学会システムインテグレーション部門講演会論文集, 2610-2615, Dec. 2012.
113.	Shoichiro Fujisawa, 三原佑太, Shin-ichi Ito and Katsuya SATO : Development of visual impairment mimetic system using AR technology, 生活生命支援医療福祉工学系連合大会2012(LIFE2012), No.OS2-4-3, 1-4, Nov. 2012. (Summary) We developed the visual impairment mimetic system to provide a visual impairment experiment system for visually non-handicapped person. Our system using AR technology with head mounted displays and buil-in stereo cameras. Captured images from the stereo cameras are processed in the PC to mimic a visual impairment and displayed on the head mounted display. In this study, we demonstrate the color-blindness experiment system as the one example of our system. Three types of color-blindness are mimicked, protanope, deuteranope and achromat. And we simulate several kind of color-blindness with slightly or severely. (Keyword) Universal Design
114.	Katsuya SATO, 門司亮, 中島雄太, 南和幸, Shin-ichi Ito and Shoichiro Fujisawa : Observation and evaluation of osteoblastic cellular response to stretch loading, M&M2012 材料力学カンファレンス講演論文集CD, OS1506, Sep. 2012.
115.	Nobuhisa Kuramoto, Shin-ichi Ito, Katsuya SATO and Shoichiro Fujisawa : 歩行運動に関連した脳波の特徴抽出, 平成24年電気学会電子・情報・システム部門大会講演論文集, 1051-1056, Sep. 2012.
116.	Kazuya Yaegashi, Koichirou Mori, Momoyo Ito, Shin-ichi Ito, Kazuhito Sato and Minoru Fukumi : ドライバの安全確認動作に着目した脳波分析, 平成24年電気学会電子・情報・システム部門大会講演論文集, 1031-1034, Sep. 2012. (Summary) 本研究では，ドライバの交差点での安全確認動作に着目した平常状態からの逸脱信号を検出するシステムの構築を目指す．そのためには，安全確認行動の変化とドライバの内部状態の変化の関連性について調査する必要がある．本稿では，信号機の無い見通しの悪い交差点での脳波に焦点を当て，安全確認動作の前後での脳波を解析することで，安全確認行動がドライバに及ぼす影響を考察する．また，安全確認を行う交差点内の注意対象物の状況の差異が脳波に与える影響についても議論する． (Keyword) driving support system / safety verification / inattentive driving / active safety technology / EEG
117.	Shin-ichi Ito : 日常生活場利用を目指した個人差を考慮した脳波センシング技術, 平成24年電気学会産業応用部門大会, シンポジウム, Vol.2 S10 2, No.2, II-5-II-8, Aug. 2012.
118.	椿卓也, Jiro Yonezaki, Katsuya SATO, Shin-ichi Ito and Shoichiro Fujisawa : ALS患者を対象とした重度障害者用意思伝達装置適正処方システムの開発, ロボティクス・メカトロニクス講演会2012講演論文集, No.2A2-T09, 1-2, May 2012.
119.	三原佑太, Katsuya SATO, Shin-ichi Ito and Shoichiro Fujisawa : AR技術を応用した視覚障害体験システムの開発, ロボティクス・メカトロニクス講演会2012講演論文集, No.1A1-N04, 1-4, May 2012.
120.	Shoichiro Fujisawa, 池田典弘, 高橋和哉, 山本源治, 木村有希, 三宅章郁, Shin-ichi Ito and Katsuya SATO : 視覚障害者のための横断歩道口発光ブロックの検証, ロボティクス・メカトロニクス講演会2012講演論文集, No.1A1-N03, 1-3, May 2012.
121.	Katsuya SATO, 門司亮, 中島雄太, 南和幸, Shin-ichi Ito and Shoichiro Fujisawa : Development of micro device for real time in situ observation of stretched cell, 日本生体医工学会第51回大会プロシーディングスCD-ROM, May 2012.
122.	池田典弘, 高橋和哉, 山本源治, 木村有希, 三宅章郁, Shin-ichi Ito, Katsuya SATO and Shoichiro Fujisawa : Verification of luminous block used for crosswalk entrance for persons with visually impairment, 日本機械学会第24回バイオエンジニアリング講演会論文集, 7B16, Jan. 2012.
123.	三原佑太, Katsuya SATO, Shin-ichi Ito and Shoichiro Fujisawa : Development of visual impairment mimetic system using AR technology, 日本機械学会第24回バイオエンジニアリング講演会講演論文集 CD-ROM, 7B15, Jan. 2012.
124.	見方俊介, Katsuya SATO, 門司亮, 中島雄太, 南和幸, Shin-ichi Ito and Shoichiro Fujisawa : Evaluation of threshold substrate strain for osteoblastic cellular response to stretch, 日本機械学会第24回バイオエンジニアリング講演会講演論文集CD-ROM, 8C15, Jan. 2012.
125.	Shin-ichi Ito, Yasue Mitsukura, Katsuya SATO and Shoichiro Fujisawa : 自己組織化マップを用いた個体差を考慮した脳波分析, 計測自動制御学会システムインテグレーション部門講演会, 2208-2211, Dec. 2011.
126.	谷林宏紀, Shin-ichi Ito, Katsuya SATO and Shoichiro Fujisawa : 指向性スピーカーを用いた広域音場再現手法の開発, 第12回公益社団法人計測自動制御学会システムインテグレーション部門講演会論文集, 2187-2188, Dec. 2011.
127.	三宅章郁, 高橋慎治, Shin-ichi Ito, Katsuya SATO and Shoichiro Fujisawa : Capability for directional indication on tactile walking surface indicators for the visually impaired, 第12回公益社団法人計測自動制御学会システムインテグレーション部門講演会論文集, 2179-2180, Dec. 2011.
128.	椿卓也, Jiro Yonezaki, Katsuya SATO, Shin-ichi Ito and Shoichiro Fujisawa : ALS患者を対象とした重度障害者用意思伝達装置適正処方システムの開発, 第12回公益社団法人計測自動制御学会システムインテグレーション部門講演会論文集, 2185-2186, Dec. 2011.
129.	Shin-ichi Ito, Masashi Hamaguchi, Katsuya SATO and Shoichiro Fujisawa : 自己組織化マップを用いた個人差を考慮した脳波分析法, 計測自動制御学会四国支部学術講演会, CD-ROM, Nov. 2011.
130.	Shin-ichi Ito, Masashi Hamaguchi, Katsuya SATO, Shoichiro Fujisawa and Toru Yamamoto : 自己組織化マップによる前頭極脳波の個人差分析~個人差を考慮した生活支援機器の開発を目指して~, 生活生命支援医療福祉工学系連合大会2011, OS2-3-1-OS2-3-2, Nov. 2011.
131.	Shin-ichi Ito, Yasue Mitsukura, Katsuya SATO and Shoichiro Fujisawa : 単一探査センサセンシングによる個人差を考慮した脳波分析法, 第34回日本生体医工学会中国四国支部大会講演抄録集, 24, Oct. 2011.
132.	三宅章郁, 高橋慎治, Shin-ichi Ito, Katsuya SATO and Shoichiro Fujisawa : 視覚障害者誘導用ブロック等における方向提示性能の評価, ロホティクス・メカトロニクス講演会2011講演論文集, No.2P1-D06, 1-2, May 2011.
133.	濱田隆麿郎, 松原加代子, Shin-ichi Ito, Katsuya SATO, Masayuki Booka, 奥英久 and Shoichiro Fujisawa : ロービジョン者の歩行面における誤識別に関する研究, ロホティクス・メカトロニクス講演会2011講演論文集, No.2P1-D10, 1-3, May 2011.
134.	Shin-ichi Ito, Takafumi Saito, Yasue Mitsukura, Katsuya SATO and Shoichiro Fujisawa : 最適解探索支援のための遺伝的アルゴリズムの可視化技術, 動的画像処理実利用化ワークショップ(DIA2011), 244-247, Mar. 2011.
135.	椿卓也, 北川博巳, 池田典弘, 山本源治, 最所裕二, 前田耕造, 矢野秀樹, Shin-ichi Ito, Katsuya SATO and Shoichiro Fujisawa : LED誘導鋲を用いた視覚障害者歩行支援システムの評価, 第11回システムインテグレーション部門講演会, 1824-1825, Dec. 2010.
136.	谷林宏紀, 北川博巳, 池田典弘, 山本源治, 最所裕二, 前田耕造, 矢野秀樹, Shin-ichi Ito, Katsuya SATO and Shoichiro Fujisawa : 路面埋め込み型スピーカーを用いた音響誘導装置の特性評価, 第11回システムインテグレーション部門講演会, 1822-1823, Dec. 2010.
137.	三宅章郁, 北川博巳, 池田典弘, 山本源治, 最所裕二, 前田耕造, 矢野秀樹, Shin-ichi Ito, Katsuya SATO and Shoichiro Fujisawa : LED境界表示灯を用いた視覚障害者歩行支援システムの評価, 第11回システムインテグレーション部門講演会, 1820-1821, Dec. 2010.
138.	Shoichiro Fujisawa, 松原加代子, 濱田隆麿郎, Shin-ichi Ito, Katsuya SATO, Masayuki Booka and 奥英久 : ロービジョン者の歩行面の御識別に関する研究, 第31回バイオメカニクス学術講演会SOBIM2010予稿集, 307-310, Nov. 2010.
139.	Shoichiro Fujisawa, Ryota Kurozumi, 辻洸介, Shin-ichi Ito, Katsuya SATO and 山本透 : 電動車いすのオンライン学習による障害物回避支援システムの研究, 第53回自動制御連合講演会, 1174-1177, Nov. 2010.
140.	Ryota Kurozumi, 辻洸介, Shin-ichi Ito, Katsuya SATO, Shoichiro Fujisawa and 山本透 : オンライン適応学習を用いた電動車いすの障害物回避補助システムの実験的評価, 第20回インテリジェント·システム·シンポジウム講演論文集, S5-3-2-1-S5-3-2-4, Sep. 2010.
141.	Shin-ichi Ito, Yasue Mitsukura, Katsuya SATO, Shoichiro Fujisawa and Minoru Fukumi : 脳波の個人特性とエゴ得点との関連性考察, 第20回インテリジェント·システム·シンポジウム講演論文集, S5-3-4, Sep. 2010. (Keyword) electroencephalogram
142.	立石広志, 上田喜敏, Katsuya SATO, Shin-ichi Ito and Shoichiro Fujisawa : 介助者の重心位置とベッド高の関係が腰部負荷に及ぼす影響, 生活生命支援医療福祉工学系学会連合大会(WWLS2010)講演論文集CD-ROM, Sep. 2010.
143.	毛利太一, 橋詰努, Shin-ichi Ito, Katsuya SATO and Shoichiro Fujisawa : スロープ登坂時における車いす利用者の身体的負荷の評価, 生活生命支援医療福祉工学系学会連合大会(WWLS2010)講演論文集CD-ROM, Sep. 2010.
144.	松原加代子, 濱田隆磨郎, Shin-ichi Ito, Katsuya SATO, 坊岡正之, 奥英久 and Shoichiro Fujisawa : ロービジョン者における視覚障害者誘導用ブロックの視認性向上に関する研究-側帯付加による視認性向上と誤認識発生に関する実験的検討-, 生活生命支援医療福祉工学系学会連合大会(WWLS2010)講演論文集CD-ROM, Sep. 2010.
145.	Shoichiro Fujisawa, 辻洸介, Ryota Kurozumi, Shin-ichi Ito, Katsuya SATO and 山本透 : 強化学習を用いた電動車椅子の障害物回避補助システムに関する研究, 平成22年度第15回知能メカトロニクスワークショップ講演論文集, 1-5, Sep. 2010.
146.	原田敏司, 米津金吾, 上田喜敏, Shoichiro Fujisawa, Katsuya SATO and Shin-ichi Ito : ベッド高の相違による重量物持上げ動作時の背筋筋電図測定と疲労解析, ロボティクス・メカトロニクス講演会'10講演論文集, Vol.1P1, No.G07, 1-4, Jun. 2010.
147.	濱田隆麿郎, 松原加代子, Shin-ichi Ito, Katsuya SATO, Masayuki Booka, 奥英久 and Shoichiro Fujisawa : ロービジョン者の歩行面の誤識別に関する研究, ロボティクス・メカトロニクス講演会'10講演論文集, Vol.1A1, No.D10, 1-3, Jun. 2010.
148.	Shin-ichi Ito, Yasue Mitsukura, Katsuya SATO, Shoichiro Fujisawa and Minoru Fukumi : 脳波の個人特性とユーザの心理特性との関連性に関する一考察, 第10回(社)計測自動制御学会システムインテグレーション部門講演会論文集, 1595-1598, Dec. 2009.
149.	原田敏司, Shoichiro Fujisawa, Katsuya SATO and Shin-ichi Ito : 交差点の音場空間再現と評価手法の検討, 第10回(社)計測自動制御学会システムインテグレーション部門講演会論文集, 1593-1594, Dec. 2009.
150.	米津金吾, 原田敏司, 上田喜敏, Shoichiro Fujisawa, Katsuya SATO and Shin-ichi Ito : ベッド高の相違による重量物持ち上げ動作時の生理学的検証, 第7回生活支援工学系学会連合大会講演予稿集, 28-29, Sep. 2009.
151.	原田敏司, Shoichiro Fujisawa, Katsuya SATO and Shin-ichi Ito : 波面合成法を用いた交差点音場環境の再現, 第7回生活支援工学系学会連合大会講演予稿集, 25-26, Sep. 2009.
152.	米津金吾, 原田敏司, 上田喜敏, Shoichiro Fujisawa, Katsuya SATO and Shin-ichi Ito : ベッド高の相違による重量物持ち上げ動作時の筋疲労解析, 平成21年電気学会電子・情報・システム部門大会講演論文集, 701-704, Sep. 2009.
153.	Nakamura Tsukasa, Shin-ichi Ito, Yasue Mitsukura and Setogawa Hiroto : 脳波を用いた触覚に関する嗜好の分類, 平成21年電気学会電子・情報・システム部門大会講演論文集, 737-740, Sep. 2009.
154.	Shin-ichi Ito, Yasue Mitsukura, Katsuya SATO, Shoichiro Fujisawa and Minoru Fukumi : ヒトの心理特性と左前頭前野の活動との関連性に関する一考察, 平成21年電気学会電子・情報・システム部門大会講演論文集, 751-754, Sep. 2009.
155.	Shin-ichi Ito, Yasue Mitsukura, Katsuya SATO, Shoichiro Fujisawa and Minoru Fukumi : BCI構築のための個人特性とエゴグラムの関連性に関する一考察, 電気学会産業応用部門大会講演論文集, II-199-202, Sep. 2009.
156.	Shin-ichi Ito, Yasue Mitsukura and Minoru Fukumi : 左前頭極脳波センシングによる気分と聴取音楽の関連性に関する一考察, シミュレーション&ゲーミング学会秋期大会, 105-106, Oct. 2008.
157.	Shin-ichi Ito and Yasue Mitsukura : 左前頭極脳波センシング技術とその応用, 平成20年電気学会産業応用部門大会, シンポジウム, Vol.2 S1 5, No.2, 27-30, Aug. 2008.
158.	Shin-ichi Ito, 宮村浩子, Yasue Mitsukura, 斎藤隆文 and Minoru Fukumi : 遺伝的アルゴリズムの可視化に基づく対話的スキーマ決定法, シミュレーション&ゲーミング学会秋期大会, 83-86, Oct. 2007.
159.	横松恵理子, Shin-ichi Ito, Yasue Mitsukura, 曹建庭 and Minoru Fukumi : 因子分析を用いた嗜好の取得, Proceedings of the Japan Industry Applications Society Conference, II_407-410, Aug. 2007.
160.	村上純子, Shin-ichi Ito, Yasue Mitsukura, 曹建庭 and Minoru Fukumi : 脳波による周波数特徴抽出システム, Proceedings of the Japan Industry Applications Society Conference, II_403-406, Aug. 2007.
161.	Shin-ichi Ito, Yasue Mitsukura, 宮村浩子 and Minoru Fukumi : 脳波に混入するアーチファクトの検出, Proceedings of the Japan Industry Applications Society Conference, II_95-98, Aug. 2007.
162.	Yusaku Kishimoto, Shin-ichi Ito, Hiroko Miyamura, Takafumi Saito and Yasue Mitsukura : 音楽聴取時の脳波特性の可視化, 情報処理学会全国大会講演論文集, Vol.69, No.4, 125-126, Mar. 2007.
163.	Shin-ichi Ito, Yasue Mitsukura, 宮村(中村) 浩子, 斎藤隆文 and Minoru Fukumi : 個人特性を考慮した脳波出現モデルと客観的評価方法, 第15回計測自動制御学会中国支部学術講演会論文集, 128-129, Nov. 2006.
164.	Shin-ichi Ito, Yasue Mitsukura, 宮村(中村) 浩子, 斎藤隆文 and Minoru Fukumi : 音楽聴取時における脳波の特徴抽出, 日本シミュレーション&ゲーミング学会秋期全国大会, 107-110, Nov. 2006. (Keyword) electroencephalogram
165.	Shin-ichi Ito, Yasue Mitsukura and Norio Akamatsu : 因子分析を用いた脳波の特徴抽出, 第46回自動制御連合講演会講演論文集, 1034-1037, Nov. 2003.
166.	Shin-ichi Ito, 矢野謙典, Yasue Mitsukura and Norio Akamatsu : セキュリティ保護のためのカラーIDの作成, 第12回計測自動制御学会中国支部学術講演会論文集, 178-179, Nov. 2003.
167.	Shin-ichi Ito, Yasue Mitsukura, Minoru Fukumi and Norio Akamatsu : The EEG Detection System Uisng the Factor Analysis, 第12回インテリジェント·システム·シンポジウム講演論文集, 413-414, Nov. 2002. (Keyword) Genetic Algorithm / Factor Analysis / neural network / electroencephalogram
168.	Shin-ichi Ito, Yasue Mitsukura, Minoru Fukumi and Norio Akamatsu : 進化型コンピューティングによる特徴脳波スペクトラム領域の抽出, 電気•情報関連学会中国支部連合大会, Vol.212233, 599-600, Oct. 2002.
169.	Shin-ichi Ito, Yasue Mitsukura, Minoru Fukumi and Norio Akamatsu : ニューラルネットワークを用いた脳波解析, 日本シミュレーション&ゲーミング学会秋期全国大会, 98-103, Sep. 2002. (Keyword) electroencephalogram / 因子分析 / neural network

Et cetera, Workshop:

1.	Kyota Yoshikawa, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : 合成画像と深層学習を用いた手話認識手法の検討について, 電気学会・産業計測制御研究会, IIC-23-010-1-IIC-23-010-5, Aug. 2023. (Keyword) 合成画像 / 集約画像 / deep learning / 手話認識 / カラーリング / 可視化
2.	MIYAKE Ryota, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : 問題に対する理解有無の推定における心拍変動分析, 電気学会・産業計測制御研究会, IIC-23-006-1-IIC-23-006-4, Aug. 2023. (Keyword) 心拍変動 / 学習理解 / 機械学習
3.	Kotaroh Hosono, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : ひらがな空中手書き文字の分割と認識, 電気学会・産業計測制御研究会, Aug. 2023. (Keyword) Leap Motiuon / ひらがな認識 / 空中入力
4.	西森翔矢, 熱田好古, 森本滋郎, Junji Kawata, Yoshio Kaji, 樋口峰夫, 坂見健二, Shoichiro Fujisawa and Shin-ichi Ito : Visibility of Blinking Light due to Differences in Illuminance, 電気学会・制御研究会, CT-22-100~1091-CT-22-100~1095, Dec. 2022. (Keyword) 視覚障害者 / amblyopia / 発光ブロック / 覚醒 / 順応 / 視認性 / 官能評価
5.	浜野凌, Sota Shimizu and Shin-ichi Ito : 脳波を用いた聴取する音楽ジャンル切り替え時のα波・β波の評価, 電気学会・産業計測制御研究会, IIC-21-029, Nov. 2021. (Keyword) electroencephalogram / music / evaluation
6.	Takuma Yoshida, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : MediaPipeを利用したハンドジェスチャーの範囲選択による文章認識, 電気学会・産業計測制御研究会, IIC-21-044, Nov. 2021. (Keyword) ハンドジェスチャー / 文章認識
7.	Shoichiro Fujisawa, 佐々木昌浩, 桶川誠貴, 坂見健二, 森本滋郎, 河田淳治, 加地芳夫, 樋口峰夫 and Shin-ichi Ito : 発光ブロックの点滅による刺激と視認性について, The Papers of Technical Meeting, IEE JAPAN, No.CT-21, 35-38, Jan. 2021. (Keyword) 視覚障害者 / amblyopia / 発光ブロック / 覚醒 / 順応 / 視認性 / 官能評価
8.	Taiga Sogame, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Discriminate H Motor Imaginations of Holding hands and Vocalization Using EEG, 電気学会・産業計測制御研究会, 55-60, Nov. 2020. (Summary) This paper proposes a method to discriminate motor imaginations of holding hands and vocalization. The proposed method consists of EEG measurement, EEG feature extraction, and motor imaginations classification. In EEG measurement, simple electroencephalograph is employed. In EEG feature, event-related synchronization (ERS) and event-related desynchronization (ERD) are extracted using fast Fourier transform (FFT). Support Vector Machine (SVM) is used to classify the motor imaginations of holding hands and vocation. In order to show the effectiveness of the proposed method, we conducted experiments using real EEG data. (Keyword) electroencephalogram / alpha wave / event-related synchronization / event-related desynchronization / locked-in syndrome
9.	Takeru Oda, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Development of EEG Personal Authentication System Considering the Effect of Caffeine, 電気学会・産業計測制御研究会, 41-46, Nov. 2020. (Summary) Changes in brain activity during caffeine intake are an issue for the spread of brain machine interface (BMI) devices to consumers. We attempt to develop an electroencephalogram (EEG) discrimination method considering the effects of caffeine. Also, this paper focuses on a personal authentication system based on EEG analysis technique. As a preliminary step to consider the influence of caffeine, this paper proposes a method to discriminate the presence or absence of caffeine and construct an authentication system. In order to show the effectiveness of the proposed method, we conducted experiments using real EEG data. (Keyword) electroencephalogram / brain machine interface / caffeine / machine learning / majority vote
10.	Takuma Yoshida, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Range Recognition Using Hand Gestures, 電気学会・産業計測制御研究会, 9-12, Nov. 2020. (Summary) This paper proposes a method to detect a range using hand gestures. The proposed method consists of hand region detection, hands recognition and gesture detection. In the hand region detection, the skin color is detected on the basis of HSV colors. In order to show the effectiveness of the proposed method, we conducted experiments with hand gestures using a web camera. The experimental results show that mean of the detection ratio for the range detection was 32.5%. (Keyword) AR / image processing / hand gesture
11.	Hironori Kadowaki, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Gait Recognition using Gathered Images and Deep Learning, 電気学会・産業計測制御研究会, 27-31, Nov. 2020. (Summary) We propose a method to recognize the gait using gathered images and deep learning. The proposed method consists of preprocessing, gathered images creation, and identification. The gathered image is created by comparing brightness values from one steps walking images. This paper employs a convolutional neural network (CNN) to extract features for gait recognition and recognize a person using them. The CNN consists of an input, three hidden, a full-connection and output layers. The hidden layers have convolutional and pooling layers. The full-connection layer has a dropout layer. Finally, we conducted experiments for gait recognition. (Keyword) gathered image / deep learning / gait recognition / convolutional neural network
12.	Shoichiro Fujisawa, 坂見健二, 坂口友哉, 青木隆功, 森本滋郎, 河田淳治, 樋口峰夫 and Shin-ichi Ito : LEDブロックの点滅効果による視認性の向上について, The Papers of Technical Meeting, IEE JAPAN, No.CT-20, 5-7, Oct. 2020. (Keyword) 視覚障害者 / amblyopia / 発光ブロック / 覚醒 / 順応 / 視認性 / 官能評価
13.	Shun Yamamoto, Minoru Fukumi, Momoyo Ito and Shin-ichi Ito : 空中入力数字の時系列データに対するCNNの有用性の検証, 電気学会・産業計測制御研究会, 1-4, Nov. 2019.
14.	Kazuki Shimamoto, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : 爪画像を用いた蓄積ストレス評価に関する一考察, 電気学会・産業計測制御研究会, 11-14, Nov. 2019.
15.	Ren Nozaki, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : 簡易脳波計を用いた面倒感情の検出, 電気学会・産業計測制御研究会, 23-29, Nov. 2019.
16.	Hironori Kadowaki, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : 集約画像を用いた歩容認証, 電気学会・産業計測制御研究会, 39-43, Nov. 2019.
17.	Shunsuke Takata, Momoyo Ito, Kazuhito Sato, Shin-ichi Ito and Minoru Fukumi : Evaluation of Driver Behavior Quantification using RSOM for Driver State Estimation, Conference record of 2017 Taiwan and Japan Conference on Circuits and Systems, 13, Aug. 2017.
18.	Momoyo Ito, Kazuhito Sato, Shin-ichi Ito and Minoru Fukumi : A Proposal of Suitable Driving Behavior Model Selection according to Driving Scene, Conference record of 2017 Taiwan and Japan Conference on Circuits and Systems, 15, Aug. 2017.
19.	萬代蘭丸, Ryota Kurozumi, Shin-ichi Ito, Katsuya SATO and Shoichiro Fujisawa : ディープラーニングによる視覚障害者の識別, The Papers of Technical Meeting, IEE JAPAN, No.CT-17_52, 19-24, Jun. 2017. (Keyword) deep learning / Visually impaired person / Blind person / Independent travel
20.	永濱秀明, Shoichiro Fujisawa, Katsuya SATO, Shin-ichi Ito, 稲垣具志, 池田典弘 and 高橋和也 : 方向定位付きLED発光ブロックの敷設位置の検証, The Papers of Technical Meeting, IEE JAPAN, No.CT-16, 33-35, Dec. 2016.
21.	石橋樹, Shoichiro Fujisawa, Katsuya SATO and Shin-ichi Ito : 注意喚起ブロックの突起直径と間隔の違いによる方向定位性能の評価, The Papers of Technical Meeting, IEE JAPAN, No.CT-16, 13-16, Dec. 2016.
22.	山本宰, Shoichiro Fujisawa, Katsuya SATO, Shin-ichi Ito, 樫山直樹, 藤本陽亮 and 田中義浩 : 農作業補助具の開発及び装着時の評価, The Papers of Technical Meeting, IEE JAPAN, No.CT-16, 9-11, Dec. 2016.
23.	Tomoya Tanaka, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Evaluation of the accumulated stress by expression analysis, 瀬戸内信号処理研究会 SSS2016, 14, Sep. 2016.
24.	Takashi Higasa, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : String Search in the Augmented Reality Space Using the Gesture Motion, 瀬戸内信号処理研究会 SSS2016, 13, Sep. 2016.
25.	Taiki Nonoguchi, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : A method to Track Mosquitoes using Orientation Code Matching and Particle Filter, 瀬戸内信号処理研究会 SSS2016, 9, Sep. 2016.
26.	Shoichiro Fujisawa, Katsuya SATO, Shin-ichi Ito and Osamu Sueda : Research on Continuity between Each Assistive Device for Personal Mobility of Persons with Visually Impairment around Crossing., 電気学会研究会資料制御研究会, No.CT-16-032, 75-79, Mar. 2016. (Summary) Crossing an intersection is one of the most risky actions for a person with visually impairment. A visually impaired pedestrian is guided to the crosswalk entrance by the tactile walking surface indicators "TWSIs". TWSIs are necessary for persons with visually impairment to walk independently. Audible pedestrian signals are also necessary for them to cross intersections. TWSIs support approaching the intersection, and audible pedestrian signals support crossing the intersection. However, the support of audible pedestrian signals may not necessarily be related to the support of the TWSIs. This research sought to verify how visually impaired persons are guided by different positions where the audible pedestrian signals are set up. (Keyword) Tactile walking surface indicators / Audible pedestrian signals / Person with visually impairment / Veering distance / Cintinuity
27.	大嶋昇, Shoichiro Fujisawa, Katsuya SATO, Shin-ichi Ito and 坊岡正之 : 介助用手動車いすのタイヤ空気圧調整時における振動計測, 電気学会研究会資料制御研究会, No.CT-16-013, 25-28, Jan. 2016.
28.	永濱秀明, 清山幹弘, Shoichiro Fujisawa, Katsuya SATO, Shin-ichi Ito, 稲垣具志, 池田典弘 and 高橋和哉 : 視覚障害者のための方向定位付きLED発光ブロックの実証実験, 電気学会研究会資料制御研究会, No.CT-16-010, 15-17, Jan. 2016.
29.	石橋樹, Shoichiro Fujisawa, Katsuya SATO and Shin-ichi Ito : 視覚障害者誘導用ブロックの方向提示性能の定量的評価, 電気学会研究会資料制御研究会, No.CT-16-009, 11-13, Jan. 2016.
30.	石橋樹, 吉村祥吾, Shoichiro Fujisawa, Katsuya SATO, Shin-ichi Ito and 前野哲哉 : 知的・精神障害者の作業分析に関する研究, 電気学会研究会資料制御研究会, Vol.CT15, No.134, 13-15, Sep. 2015.
31.	中島紘一, Shoichiro Fujisawa, Katsuya SATO and Shin-ichi Ito : 重度障害者を対象とした意思伝達装置に関する研究, 電気学会研究会資料制御研究会, Vol.CT15, No.133, 9-11, Sep. 2015.
32.	Taiki Nonoguchi, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Image Processing Method to Detect and Track Mosquito using Backgraound Subtraction, 瀬戸内信号処理研究会 SSS2015, 17, Sep. 2015.
33.	Shu Tamura, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : A Novel Music Database Based on Chord Pattern, Rhythm and Music Feature Extraction Method to Smilarity Evaluation, 瀬戸内信号処理研究会 SSS2015, 15, Sep. 2015.
34.	Shin-ichi Ito : 脳波に基づいた情報処理, 電気学会研究会資料制御研究会, No.CT 15 088, 19-234, Aug. 2015. (Keyword) electroencephalogram / external stimuli
35.	清山幹弘, Shoichiro Fujisawa, Katsuya SATO, Shin-ichi Ito, 稲垣具志, 池田典弘 and 高橋和哉 : 横断歩道口用発光ブロックの識別に関する研究, 電気学会研究会資料制御研究会, No.CT-15-021, 5-8, Mar. 2015.
36.	Hikaru Shouda, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Supporting System to Grow in Description Ability for Beginners, 瀬戸内合同信号処理研究会 SSS2014, 16, Sep. 2014.
37.	Nao Tsuzuki, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Method to Detect Uncomfortable Feeling of Listeners by Biological Information - Supporting System to Hold a Conversation for Smooth Communication -, 瀬戸内合同信号処理研究会 SSS2014, 15, Sep. 2014.
38.	Shu Tamura, Shin-ichi Ito, Momoyo Ito and Minoru Fukumi : Method to Construct Database for Best Music Selection System by EEG, 瀬戸内合同信号処理研究会 SSS2014, 14, Sep. 2014.
39.	Takashi Fujishima, Shin-ichi Ito, Momoyo Ito, Minoru Fukumi and Shoichiro Fujisawa : 振り子特急列車乗車時における酔いの予兆検出のための生体情報分析, 電気学会研究会資料知覚情報研究会, No.PI 14 056∼080 082∼093, 113-116, Sep. 2014.
40.	Tomo Uchiyama, Shin-ichi Ito, Momoyo Ito, Minoru Fukumi and Shoichiro Fujisawa : 薬効に基づく裁量医薬品組み合わせの決定, 電気学会研究会資料知覚情報研究会, No.PI 14 056∼080 082∼093, 31-34, Sep. 2014.
41.	田辺猛, Shoichiro Fujisawa, Katsuya SATO and Shin-ichi Ito : 破面合成法による道路交通騒音再現に関する研究, 電気学会研究会資料制御研究会, No.CT-14, 35-38, Mar. 2014.
42.	廣野恭平, Shin-ichi Ito, Katsuya SATO and Shoichiro Fujisawa : 交差点周辺の視覚障害者の単独歩行のための移動支援機器間の連続性, 電気学会研究会資料制御研究会, No.CT-14, 29-32, Mar. 2014.
43.	清山幹弘, 志賀大輔, 池田典弘, 高橋和哉, 稲垣具志, Katsuya SATO, Shin-ichi Ito and Shoichiro Fujisawa : 横断歩道口用発光ブロックの識別に関する研究, 電気学会研究会資料制御研究会, No.CT-14, 5-8, Mar. 2014.
44.	Takamaro Hamada, Kayoko Matsubara, Shin-ichi Ito, Katsuya SATO, Masayuki Booka, Hidehisa Oku and Shoichiro Fujisawa : ロービジョン者の歩行面の誤識別に関する研究, 産業計測制御研究会, 7-10, Mar. 2010.
45.	Shin-ichi Ito, Yasue Mitsukura, Katsuya SATO, Shoichiro Fujisawa and Minoru Fukumi : 脳波の個人特性とユーザのエゴグラム得点との関連性, 産業計測制御研究会, 17-20, Mar. 2010.
46.	原田敏司, 米津金吾, 上田喜敏, Shoichiro Fujisawa, Katsuya SATO and Shin-ichi Ito : Measurement and fatigue analysis of the Musculus erector spinae EMG at the time of weight lifting, 第79回パターン計測部会研究会資料, 45-50, Jul. 2009.
47.	Shin-ichi Ito, Yasue Mitsukura, Katsuya SATO, Shoichiro Fujisawa and Minoru Fukumi : A Study on Pattern Classification of Left Prefrontal Cortex EEG, 第79回パターン計測部会研究会資料, 39-44, Jul. 2009.
48.	朱景華, Shin-ichi Ito, Yasue Mitsukura, 渡井宏和, 田代勝巳 and 村上和朋 : 路面の違いが乗り心地に与える影響の検証, 産業計測制御研究会, 109-112, Mar. 2009.
49.	冨田洋平, Shin-ichi Ito, Yasue Mitsukura, 田中聡久 and 曹建庭 : 適応律動成分抽出法を応用した睡眠脳波分析, 産業計測制御研究会, 105-108, Mar. 2009.
50.	Endo Tsukasa, Shin-ichi Ito, Yasue Mitsukura and Minoru Fukumi : 曲調解析に基づく音楽分析法の提案, IEICE Technical Report, Vol.108, No.101, 51-54, Jun. 2008.
51.	Tomita Yohei, Shin-ichi Ito, Yasue Mitsukura and Minoru Fukumi : 脳波を用いた過矯正におけるテクノストレスへの影響解析, IEICE Technical Report, Vol.108, No.101, 47-50, Jun. 2008.
52.	鈴木聡, 深田陽介, 深井寛修, Shin-ichi Ito, Yasue Mitsukura and Minoru Fukumi : 画像による質感解析手法の検討, 産業計測制御研究会, 7-10, Mar. 2008.
53.	Shin-ichi Ito, Yasue Mitsukura and Minoru Fukumi : 脳波センシングによる気分一致・不一致刺激の検出, 産業計測制御研究会, 67-70, Mar. 2008.
54.	遠藤司, 深井寛修, Shin-ichi Ito, Yasue Mitsukura and Minoru Fukumi : SVMを用いた時変複素音声分析による曲調解析, 産業計測制御研究会, 61-64, Mar. 2008.
55.	冨田洋平, Shin-ichi Ito, Yasue Mitsukura, 甲田菜穂子 and Minoru Fukumi : 脳波センシングシステムを用いた感覚の分類, 産業計測制御研究会, 71-74, Mar. 2008.
56.	中村泰行, 深井寛修, Shin-ichi Ito, Yasue Mitsukura and Minoru Fukumi : ハチ目の性決定システムを取り入れた遺伝的アルゴリズムの提案, 電子情報通信学会ニューロコンピューティング研究会, Vol.107, No.413, 131-134, Jan. 2008.
57.	村上純子, 横松恵理子, Shin-ichi Ito, Yasue Mitsukura and Minoru Fukumi : 環境状況分類における脳波特徴抽出システム, 電子情報通信学会ニューロコンピューティング研究会, Vol.107, No.92, 1-4, Jun. 2007.
58.	Shin-ichi Ito, Yasue Mitsukura, Takafumi Saito, Hiroko Miyamura and Minoru Fukumi : 実用的脳波計測装置を用いた音楽聴取時における脳波分析, 電気学会産業計測制御研究会, Vol.IIC-07-79, 53-58, Mar. 2007.
59.	土肥賢太郎, Shin-ichi Ito, Yasue Mitsukura, 矢間優希 and Minoru Fukumi : 遺伝的特徴解析によるEMG信号解析, IEICE Technical Report, Vol.HIP2006, Jul. 2006.
60.	Shin-ichi Ito, Yasue Mitsukura, 宮村浩子, 斎藤隆文 and Minoru Fukumi : The Proposal of the Feature Extraction Method in Weighted Principal Frequency Components Using the RGA, IEICE Technical Report, Vol.HIP2006 37, 73-78, Jul. 2006. (Summary) An EEG has frequency components which can describe most of the significant features. These combinations are often unique like individual human beings and yet they have underlying basic features. These frequency components are contained the important and/or not so important components, and then each importance of these frequency components is different. The real-coded genetic algorithm (: RGA) is used for selecting and weighting the principal characteristic frequency components. We attempt to construct mental change appearance model (: MCAM) of one measurement point only. In order to show the effectiveness of the proposal method, computer simulations are carried out by using real data. (Keyword) Electroencephalogram / Real-coded Genetic Algorithms / Structure Equation Model / Mental Change (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1520290882390881920 (CiNii: 1520290882390881920)
61.	Takahiro Ogawa, Shin-ichi Ito, Yasue Mitsukura, Minoru Fukumi and Norio Akamatsu : Classification of the EEG in listening the music by using multivariate analysis, IEICE Technical Report, Vol.HIP2005-33, 77-81, Jul. 2005. (Keyword) electroencephalogram / principal component analysis / canonical discriminant analysis / neural network
62.	Shin-ichi Ito, 矢野謙典, Yasue Mitsukura and Norio Akamatsu : 画像セキュリティのためのカラーIDの開発, ヒューマンインタフェースシンポジウム2003論文集, 561-564, Oct. 2003.
63.	Shin-ichi Ito, Yasue Mitsukura, Minoru Fukumi and Norio Akamatsu : Detection of the Features of EEG Pattern Using the Factor Analysis and Neural Networks, ヒューマンインタフェース学会研究報告集, Vol.5, No.1, 127-130, Jan. 2003. (Keyword) electroencephalogram / Factor Analysis / neural network / Bark Scale Analysis
64.	Shin-ichi Ito, Yasue Mitsukura, Minoru Fukumi and Norio Akamatsu : GAとNNを用いた有効脳波スペクトラム領域の抽出, 電子情報通信学会NC研究会, 1-5, Jun. 2002.

Patent:

1.	Shin-ichi Ito and Minoru Fukumi : 信号処理装置，信号処理方法および信号処理プログラム, (Sep. 2020), 2020-151110 (Sep. 2020).

Grants-in-Aid for Scientific Research (KAKEN Grants Database @ NII.ac.jp)

BCI system without special training to communicate degree of feeling and/or KANSEI (Project/Area Number: 22K12221 )
Study on blinking light of LED guiding block for person with visually impairement (Project/Area Number: 20K12736 )
A Study on Electroencephalogram Analysis Method Considering Individual Differences to Communication BCI (Project/Area Number: 17K12768 )
Evaluation of Orientation Performance of Tactile Walking Surface Indicators (Project/Area Number: 15K01458 )
Analysis Methods for Electroencephalogram Related to Preference and Decision-making Considering Human Character (Project/Area Number: 24700210 )
Research on continuity among assistive devices for personal mobility of persons with visually impairment around crossings (Project/Area Number: 24500646 )
Modification of indistinguishable colors based on personal characteristic model for people with color defective vision (Project/Area Number: 22700578 )
Miss-recognition of Load Surface by The Low Vision (Project/Area Number: 21500516 )
Search by Researcher Number (90547655)

Associate Professor : Ito, Shin-ichi

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Grants-in-Aid for Scientific Research (KAKEN Grants Database @ NII.ac.jp)