Tokushima University / Educator and Researcher Directory --- Ito, Momoyo

Personal Web Page

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

Field of Study

○	Engineering

Subject of Study

○	Research on intention recognition based on human behavior analysis (evolutionary image processing, medical imaging)

Book / Paper

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.	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)
3.	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)
4.	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)
5.	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)
6.	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)
7.	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)
8.	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)
9.	Kazuhito Sato, Masafumi Sawataishi, Hirokazu Madokoro, Momoyo Ito and Sakura Kadowaki : Modeling of Drivers Distraction State based on Body Information Analysis, International Journal on Advances in Life Sciences, Vol.10, No.1 & 2, 42-53, 2018.
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.	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)
12.	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)
13.	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): 1050302172853026560 ● Search Scopus @ Elsevier (DOI): 10.12792/JIIAE.3.52 (Tokushima University Institutional Repository: 113536, DOI: 10.12792/JIIAE.3.52, CiNii: 1050302172853026560)
14.	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)
15.	Kazuhito Sato, Sakura Kadowaki, Hirokazu Madokoro, Momoyo Ito and Atsushi Inugami : Unsupervised Segmentation of MR Images for Brain Dock Examinations, International Journal of Health Science, Vol.4, No.5, 113-129, 2014. (Summary) We propose an unsupervised segmentation method for magnetic resonance (MR) brain imaging by hybridizing the self-mapping characteristics of 1-D Self-Organizing Maps (SOMs) and by using incremental learning functions of fuzzy Adaptive Resonance Theory (ART). The proposed method requires no operator to specify the representative points. Nevertheless, it can segment the tissues (e.g., cerebrospinal fluid, gray matter, and white matter) necessary for brain atrophy diagnosis. To evaluate the effectiveness of the proposed method, we specifically examine Fuzzy C-means (FCM) and Expectation Maximization Gaussian Mixture (EM-GM) with prior setting of the cluster number, and Mean Shift (MS) without prior setting of the cluster number. These experiments on the two metrics confirmed that our method can achieve higher accuracy than these conventional methods. (Keyword) Medical imaging / Medical image analysis / Image segmentation / Self-Organizing Maps (SOMs) / Adaptive Resonance Theory (ART) / Brain dock examination / Brain atrophy / Computer-Aided Diagnosis (CAD) (Link to Publication Site) ● Publication site (DOI): 10.5923/j.health.20140405.02 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.5923/j.health.20140405.02 (DOI: 10.5923/j.health.20140405.02)
16.	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)
17.	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)
18.	Momoyo Ito, Kazuhito Sato and Minoru Fukumi : A Study of Safety Driving Support System focusing on Driver's Head Posture Categorization, International Journal of Engineering Research and Technology, Vol.2, No.9, 2702-2711, 2013. (Summary) In this paper, we analyze drivers head posture during safety verification and propose a method for classifying head posture using two types of unsupervised neural networks: Self-Organizing Maps (SOMs) and fuzzy Adaptive Resonance Theory (ART). The proposed method can generate the optimal number of cluster-generated labels for the target problem. We experimentally assess the effectiveness of the proposed method by adjusting the fuzzy ART network vigilance parameters. In addition, we indicate that drivers head posture during safety verification can be categorized according to their individual properties.
19.	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.
20.	Yohei Takeuchi, Momoyo Ito and Minoru Fukumi : Novel Approximate Statistical Algorithm for Large Complex Datasets, International Journal of Machine Learning and Computing, Vol.2, No.5, 720-724, 2012. (Summary) In the field of pattern recognition, principal component analysis (PCA) is one of the most well-known feature extraction methods for reducing the dimensionality of high-dimensional datasets. Simple-PCA (SPCA), which is a faster version of PCA, performs effectively with iterative operated learning. However, SPCA might not be efficient when input data are distributed in a complex manner because it learns without using the class information in the dataset. Thus, SPCA cannot be said to be optimal from the perspective of feature extraction for classification. In this study, we propose a new learning algorithm that uses the class information in the dataset. Eigenvectors spanning the eigenspace of the dataset are produced by calculating the data variations within each class. We present our proposed algorithm and discuss the results of our experiments that used UCI datasets to compare SPCA and our proposed algorithm. (Tokushima University Institutional Repository) ● Metadata: 113538 (Link to Publication Site) ● Publication site (DOI): 10.7763/IJMLC.2012.V2.222 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.7763/IJMLC.2012.V2.222 (Tokushima University Institutional Repository: 113538, DOI: 10.7763/IJMLC.2012.V2.222)
21.	Koji Kashihara, Momoyo Ito and Minoru Fukumi : Automatic system to remove unpleasant images detected by pupil-size changes., International Journal of Computer Science Issues, Vol.9, No.1, 68-73, 2012.
22.	Momoyo Ito, Nishida Makoto and Namura Ikuro : Extraction Method of Brain Regions Using Balloon models for Diagnosis Support of Alzheimer-Type Dementia, IEEJ Transactions on Electronics, Information and Systems, Vol.129, No.7, 1435-1443, 2009. (Summary) We intend to construct an image diagnosis support system for Alzheimer-type Dementia (ATD) that extracts temporal lobe regions and an intracranial region as interest regions from a T2-weighted MR frontal image and uses the cerebral atrophy rates at the regions of interest. In this paper, we specifically discuss extraction of regions of interest. The proposed method consists of three steps. First, we emphasis features of an obscure T2-weighted brain image. Second, we set a first contour that approximates a shape of the temporal lobe region to a triangle and apply Balloon models with the added presser force that push the initial contour outside in order to extract a temporal lobe region. Finally, we extract an intracranial region using extracted temporal lobe regions. Our proposed method can extract regions of interest along individual brain features by only a few interactions with three points. We demonstrate the potential of our method using actual diagnosis images. Moreover, we show a possibility to use of atrophy rate at the regions of interest for diagnosis support of ATD. (Keyword) MRI / T2-weighted image / Balloon models / Temporal lobe / Atrophy rate / T2-weighted image / Temporal lobe / Atrophy rate (Link to Publication Site) ● Publication site (DOI): 10.1541/ieejeiss.129.1435 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390282679581324288 ● Search Scopus @ Elsevier (DOI): 10.1541/ieejeiss.129.1435 (DOI: 10.1541/ieejeiss.129.1435, CiNii: 1390282679581324288)
23.	Momoyo Ito, Odashima Natsuko, Nishida Makoto, Namura Ikuro and Kageyama Yoichi : A Basic Study on Selection of Main Image for MR Brain Image Set for Supporting Medical Specialists, Journal of the Society od Materials Engineering for Resource of Japan, Vol.20, No.2, 23-28, 2007. (Summary) Multiple MR brain images are acquired at once. To begin with, the medical specialists examine a MR brain image, which shows temporal lobes, a frontal lobe, and some important regions for diagnosis, located near the cerebral center. Automatic selection of the main image from MR brain image set is a useful diagnosis imaging support for medical specialists. This paper proposes a method for extracting a ventricle area in order to select a main image. The proposed method works three steps. First, the process using a mode method that extracts sliced areas from original MR brain images. Second, the Otsu's method performs a binary process. Third, a ventricle area is a unique shape and we therefore defined two rectangles: the frontal horn and the posterior horn of a lateral ventricle region. The experimental results revealed that the proposed algorithm was able to accurately extract the ventricle area from the image set. (Keyword) MRI / brain image / main image / ventricle area / diagnosis imaging support (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1523669554907815936 (CiNii: 1523669554907815936)

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.	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)
3.	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)
4.	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)
5.	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
6.	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)
7.	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)
8.	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)
9.	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)
10.	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)
11.	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)
12.	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
13.	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
14.	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
15.	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
16.	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
17.	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)
18.	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
19.	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
20.	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, 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): 1050583647829633920 (Tokushima University Institutional Repository: 113733, CiNii: 1050583647829633920)
21.	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
22.	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
23.	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
24.	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)
25.	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)
26.	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
27.	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)
28.	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)
29.	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)
30.	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)
31.	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
32.	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
33.	Kamat Rahayu Seri, Ani Firdaus Mohamad, Hadi Aisyah Abd Nur, Rayme Syafiqah Nur, Momoyo Ito and Minoru Fukumi : Redesign the Material Handling System by Using Ergonomic Approaches to Reduce Back Pain Risk, International Conference on Kansei Engineering & Emotion Research 2018, 1-11, Kuching, Malaysia, Mar. 2018. (Summary) This paper is about the redesign material handling device through the study of psychological and biomechanical factors that related the activities push and pull trolley. The project has been conducted in the automotive industry. The data about the problems encountered through a questionnaire among 14 workers at Receiv-ing Area, since it has the highest number of ergonomic risk factors. The result shows the main problem is the existing trolley design seems not suitable and giv-ing musculoskeletal disorder(MSD) of the workers especially back pain. There-fore, the aim of this paper is redesigns the material handling system by using er-gonomic approaches to reduce back pain. The Tekscan software will be used for evaluating hand pressure distribution force on the existing and alternatives trolley. The result obtained is used as a benchmark for the concept of trolley to be rede-signed. The Rapid Upper Limb Assessment (RULA) was applied to analyses the posture of workers on the existing and alternative trolley and the redesign trolley to be proposed by ABC company. Based on the RULA analysis, the redesigned trolley has improved the posture of the workers. Hence, this study concludes that considering ergonomic features for the redesigned trolley contributed to safe body posture. (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85044216424 (Elsevier: Scopus)
34.	Kamat Rahayu Seri, Ani Firdaus Mohamad, Ghazali Athira, Shamsudin Syamami, Momoyo Ito and Minoru Fukumi : Mathematical Modelling of Biomechanics Factors for Push Activities in Manufacturing Industry, Symposium on Intelligent Manufacturing and Mechatornics 2018, 3-14, Pekan, Malaysia, Jan. 2018. (Summary) In manufacturing industries, many working tasks require their workers to perform the works in push-pull activities. The workers need to push or pull the tool or material handling in a long distance in to a workplace and performing these activities continuously throughout the working hours, may lead to an early initiation of musculoskeletal disorders (MSDs) symptoms as workers developed muscle fatigue particularly concerning the hand muscles. Grip strength is the force applied by the hand to pull objects and is a part of hand strength. This paper is about the mathematical model of biomechanical factors that contributes to fa-tigue while worker involved on the push activities in manufacturing industry. The experimental was conducted by using Tekscan system to evaluate the muscle fa-tigue and hand grip pressure force while workers performing pushing excessive loads. The input parameters were time exposure, hand side and body mass index (BMI); while the output responses are muscle fatigue (voltage), hand grip pres-sure force (left hand), and hand grip pressure force (right hand). An important parameter that affects the output response is also identified. The finding result from mathematical model for both factors, show that the muscle fatigue was in-fluenced by time exposure, hand side, BMI, and interaction between hand side and BMI; while hand grip pressure force was influenced by time exposure, hand side, BMI, interaction between time exposure and hand side, interaction between time exposure and BMI, and interaction between hand side and BMI. (Link to Publication Site) ● Publication site (DOI): 10.1007/978-981-10-8788-2_1 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85046258305 (DOI: 10.1007/978-981-10-8788-2_1, Elsevier: Scopus)
35.	Ani Firdaus Mohamad, Kamat Rahayu Seri, Minoru Fukumi, Momoyo Ito, Minhat Mohamad and Rayme Syafiqah Nur : Development of Ergonomic Vehicle Model and Decision Support for Driving Fatigue, Symposium on Intelligent Manufacturing and Mechatornics 2018, 355-369, Pekan, Malaysia, Jan. 2018. (Summary) Driving fatigue has been recognized as one of the significant contrib-utor factor to the road accidents and fatalities in Malaysia. The aim of this study was to develop an ergonomic vehicle model (EVM) and decision support sys-tem (DSSfDF) model for improving the driving fatigue problem among the road users. The ergonomic vehicle model use to capture the user information data, and acts as the database storage to store all the input data and information. While the decision support system provide a systematic analysis and solution to minimize the risk and the number of accidents associated with driving fatigue. There are 6 main components as the pillars for the development of EVM and DSSfDF model; ergonomics evaluation tools, graphical user interface (GUI), ergonomics database, working memory, inference engine, and knowledge base. Both models are essential system and reliable advisory tool for providing analy-sis on risk factors that contribute significantly to driving fatigue, and providing solutions and recommendation to the problem related to driving fatigue. Further analysis and validation is required in future to get the reliable system before be-ing commercialize. (Link to Publication Site) ● Publication site (DOI): 10.1007/978-981-10-8788-2_31 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85046288911 (DOI: 10.1007/978-981-10-8788-2_31, Elsevier: Scopus)
36.	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)
37.	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
38.	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
39.	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
40.	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
41.	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.
42.	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.
43.	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.
44.	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.
45.	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.
46.	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.
47.	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)
48.	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)
49.	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)
50.	Kazuhito Sato, Momoyo Ito, Hirokazu Madokoro and Sakura Kadowaki : Facial Part Effects Analysis using Emotion-evoking Videos: Smile Expression, Proceedings of The Tenth International Multi-Conference on Computing in the Global Information Technology, 30-39, St. Julians, Malta, Oct. 2015.
51.	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.
52.	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)
53.	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)
54.	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
55.	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.
56.	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.
57.	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.
58.	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.
59.	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.
60.	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.
61.	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.
62.	Momoyo Ito, Kazuhito Sato and Minoru Fukumi : Driver Body Information Analysis with Near-miss Events, Proceedings of AMBIENT 2014: The Fourth International Conference on Ambient Computing, Applications, Services and Technologies, 43-46, Rome, Aug. 2014. (Summary) This study specifically examines safety verification behaviors and near-miss events at non-regulated intersection with poor visibility. Assessing the drivers physical information (i.e., eye-gaze movements and face orientation) and the sudden appearance of bicycles that the driver would encounter while approaching a non-regulated intersection, we attempt to analyze the causal relation of workload sensitivity and driving style and the distinctive motion of the safety verification behaviors before and after near-miss events.
63.	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.
64.	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.
65.	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.
66.	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)
67.	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)
68.	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)
69.	Momoyo Ito, Kazuhito Sato and Minoru Fukumi : Classification of Driver's Head Posture by using Unsupervised Neural Networks, Proceedings of The Third International Conference on Ambient Computing, Applications, Services and Technologies, 50-57, Porto, Oct. 2013. (Summary) We analyze drivers head posture during safety verification at an unsignalized intersection with poor visibility and propose a method for classifying head posture using two types of unsupervised neural networks: Self-Organizing Maps (SOMs) and fuzzy Adaptive Resonance Theory (ART). The proposed method can generate the optimal number of cluster-generated labels for the target problem. We experimentally assess the effectiveness of the proposed method by adjusting the fuzzy ART network vigilance parameters.
70.	Momoyo Ito, Kazuhito Sato and Minoru Fukumi : Analysis of Safety Verification Behavior and Classification of Drivers Head Posture, Proceedings of 2013 IEEE International Conference on Mechatronics and Automation, 884-889, Takamatsu, Aug. 2013. (Summary) In this paper, we analyze drivers head posture of safety verification at the unsignalized blind intersection, and propose a classification method of head posture using two kinds of unsupervised neural networks: SOMs and Fuzzy ART to quantize drivers head motion for construct a driving assist system which is able to detect the continual deviation signals. The proposed method is able to categorize head posture roughly. (Link to Publication Site) ● Publication site (DOI): 10.1109/ICMA.2013.6618032 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84887909647 (DOI: 10.1109/ICMA.2013.6618032, Elsevier: Scopus)
71.	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.
72.	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.
73.	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.
74.	Momoyo Ito, Kazuhiro Sato, Koichiro Mori and Minoru Fukumi : A Basic Study for Quantification of Driving Behaviors and Estimation of Driving Psychology, Proceeding of 2013 International Workshop on Nonlinear Circuits, Communication and Signal Processing NCSP'13, 21-24, Kona, Mar. 2013. (Summary) In this paper, we propose a quantification method of driving behavior using two kinds of unsupervised neural networks: Self-Organizing Maps and Fuzzy Adaptive Resonance Theory, and discuss the relation between safety verification motion and driving psychology. Finally, we show that driving behavior expresses the driving psychology.
75.	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.
76.	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.
77.	Yohei Takeuchi, Momoyo Ito and Minoru Fukumi : Nonlinear Eigenspace Models Based on Fast Statistical Learning Algorithm, Proceedings of IASTED International conference on Software Engineering and Applications, 274-278, Las Vegas, Nov. 2012. (Summary) In the field of pattern recognition, feature extraction plays an important role prior to classification in order to filter out the background noise, reduce the dimensionality for input and so on. Fisher Linear Discriminant Analysis (FLDA) is well-known as one of the most famous feature extraction methods. In recent years, FLDA has been improved in various ways because an eigenspace is learned faster and/or the classification performance is improved. Simple-FLDA (SFLDA) has been proposed to speed up the learning by improving FLDA algorithm. However, the above methods are calculated in input space. Thus, it might not be efficient in cases where data distribution is complex. Then, Simple Kernel Discriminant Analysis (SKDA), which is an improved version of Kernel Discriminant Analysis (KDA), has been proposed to acquire a better performance for classification by applying kernel trick. Whereas a better performance is acquired by SKDA than that by SFLDA, its learning speed has increased instead. In this paper, an additional improvement is applied to SKDA algorithm and the improved version of SKDA (SIKDA) is introduced. The performance of SIKDA is as same as that of SKDA. In addition, learning speed has become faster than that by SKDA. These are shown in the experiment, especially, the influence of proposed method has seen in a specified dataset. (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84876568238 (Elsevier: Scopus)
78.	Momoyo Ito, Kazuhito Sato and Minoru Fukumi : Optimization of Categorizing Driver's Head Motionfor Driving Assistance Systems, Proceedings of SICE Annual Conference 2012 Final Program and Papers, 471-474, Akita, Aug. 2012. (Summary) Our system needs quantization of drivers 3D head motions in safety verification only phase variation on 2D image taken by monocular in-vehicle camera, and modeling of head motion information. In this paper, we optimize categorization of drivers head motion using two kinds of unsupervised neural networks: Self-Organizing Maps (SOMs) and Fuzzy Adaptive Resonance Theory (ART).
79.	Yoshimi Miki, Yohei Takeuchi, Momoyo Ito and Minoru Fukumi : Decision of Two Alternatives by EEG using Genetic Algorithm, Proceedings of International Technical Conference on Circuits/Systems, Computers and Communications (CD-R), D-T3-05, Sapporo, Jul. 2012. (Summary) The electroencephalograms (EEGs) are a biological signal that represents the electrical activity of the brain. There is a tendency to quantify the human mind and its changes based on preference, feeling, and impression by analyzing electroencephalograms (EEGs) taken on the human scalp. Recently, it is expected that EEG will promote special man-machine interface. In this study we try to estimate human mind and emotion from EEG which becomes popular by development of measurement techniques and analysis of brain activity. In this paper, detection of frequencies, which expressly shows two tendencies to be distinguished while a human subject is in low and high concentration states, is carried out. The purpose of this study is to construct a tool for alternative decision with the frequency difference in two states. Two concentration states can be classified using specified frequencies measured when a human subject is in low and high concentration states. Severe disorders such as Amyotrophic Lateral Sclerosis (ALS) make it hard for motion-impaired people to communicate with others by talking and writing. Therefore, support systems that can help them to establish communication can be constructed with less mental load by utilizing the analyzed data of this study.
80.	Momoyo Ito, Kazuhito Sato and Minoru Fukumi : Classification of Head Motions for Estimation of Driver's Internal States, Proceedings of International Technical Conference on Circuits/Systems, Computers and Communications (CD-R), D-T3-01, Sapporo, Jul. 2012.
81.	Yohei Takeuchi, Momoyo Ito and Minoru Fukumi : Kernel Discriminant Analysis Based on Iterative Calculations, Proceedings of International Technical Conference on Circuits/Systems, Computers and Communications (CD-R), E-T1-04, Sapporo, Jul. 2012. (Summary) Fisher Linear Discriminant Analysis (FLDA) is well-known as one of the most famous feature extraction methods in the research field of pattern recognition. In recent years, FLDA has been developed with various techniques to improve its effectiveness for classification, learning speed and generality for any problems. Simple-FLDA (SFLDA), which is a faster version of FLDA, has been proposed to extract features from input datasets in an efficient way. However, it is not always efficient in cases where the datasets are complex, because eigenvectors spanning an eigenspace are acquired in just the input space. In this paper, we proposed a new feature extraction algorithm derived by expanding SFLDA to a non-linear space for effective classification. The algorithm is constructed with just simple calculations, but it is capable of obtaining effective features. In some experiments, better classification accuracies have been acquired with a few specified datasets.
82.	Ryo Yoshioka, Yohei Takeuchi, Momoyo Ito and Minoru Fukumi : Object Search Using a Rough Sketch, Proceedings of International Technical Conference on Circuits/Systems, Computers and Communications (CD-R), E-T1-02, Sapporo, Jul. 2012. (Summary) It is difficult to search for lost property in extensive grounds. Therefore, we often have to give up finding it out. To solve this problem, search methods using a security camera have been proposed. It is necessary to not increase the administrative burden when you search an object by using security cameras. In this study, we develop a system for searching an object from camera images automatically. When searching the object, we need to input appropriate information of the object to the system. In this paper, a ``hand-drawn rough sketch'' image is used for this purpose. The present system can identify the object by analyzing the object shape in the image.
83.	Fumitoshi Taoka, Momoyo Ito, Koji Kashihara and Minoru Fukumi : Detection of Abandoned Objects in Public Facilities, Proceeding of 2012 International Workshop on Nonlinear Circuits, Communication and Signal Processing NCSP'12, 619-622, Honolulu, Mar. 2012. (Summary) This paper presents a new method to detect abandoned objects. Our approach consists of the following 5 steps; Dynamic background modeling by using the Gaussian mixture model based on features obtained by space-time patches, human region detection based on Support Vector Machine, movement region detection based on matching of regions histogram features, construction of region database from region features, and abandoned objects detection from the region database. The proposed method adds color information on the features obtained using dynamic background modeling to enhance the effectiveness of detection for an object that is assimilated into background. Experimental results prove the efficiency of our algorithm on PETS2006 and AVSS2007 benchmark data.
84.	Kazuya Yaegashi, Momoyo Ito, Koichiro Mori, Kazuhito Sato, Koji Kashihara and Minoru Fukumi : Fundamental Study on EEG Analysis for Safety Driving Support System, Proceeding of 2012 International Workshop on Nonlinear Circuits, Communication and Signal Processing NCSP'12, 587-590, Honolulu, Mar. 2012. (Summary) In this paper, as a fundamental study of relationship between drivers internal state and the safety verification motion, we analyze four drivers EEG (electroencephalogram) at unsignalized intersections, and discuss effects of drivers internal state changes on safety verification.
85.	Takahide Funabashi, Yohei Takeuchi, Momoyo Ito, Koji Kashihara and Minoru Fukumi : Recognition of FingerMotion by Wrist EMG, Proceeding of 2012 International Workshop on Nonlinear Circuits, Communication and Signal Processing NCSP'12, 433-436, Honolulu, Mar. 2012. (Summary) In this paper finger motions were recognized by EMG measured using dry-type electrodes attached to wrist. Target behaviors to be recognized are four motions that the Janken ``rock'', ``scissors'', ``paper'' and when not doing anything ``neutral''. We tried to reduce an execution time by the simple-PCA in training and recognition, with a view to implementation of interface which can be utilized in daily life.
86.	Yuki Ikami, Koji Kashihara, Momoyo Ito and Minoru Fukumi : Visual Illusion of Depth Percception during Car Driving, Proceeding of 2012 International Workshop on Nonlinear Circuits, Communication and Signal Processing NCSP'12, 293-296, Honolulu, Mar. 2012.
87.	Yusuke Yamamura, Yohei Takeuchi, Momoyo Ito, Koji Kashihara and Minoru Fukumi : Classification of Motions by EMG of Ankle, Proceeding of 2012 International Workshop on Nonlinear Circuits, Communication and Signal Processing NCSP'12, 285-288, Honolulu, Mar. 2012.
88.	Natsumi Ohtani, Yohei Takeuchi, Momoyo Ito, Koji Kashihara and Minoru Fukumi : Speech Recognition of Whisper Voice Based on EMG Signals, Proceeding of 2012 International Workshop on Nonlinear Circuits, Communication and Signal Processing NCSP'12, 281-284, Honolulu, Mar. 2012. (Summary) Speech recognition using surface electromyographic (EMG) is regarded as one of substitute or support for situations that cannot obtain a clear sound. This research aims at Japanese speech recognition to a whisper voice using EMG signals. In this paper, as the first step, we recognized Japanese 5 vowels of the prolonged sound. In order to recognize them, FFT was carried out for the EMG signals obtained from 2 muscular parts. After that, the features were extracted by Simple-PCA, and 1-NN was used to recognize them. The average recognition accuracy of 5 vowels was about 79%.
89.	Kentaro Mori, Satoru Tsuge, Shingo Kuroiwa, Momoyo Ito, Koji Kashihara and Minoru Fukumi : Acoustic Model Selection Method for Speaker Dependent Speech Recognition, Proceeding of 2012 International Workshop on Nonlinear Circuits, Communication and Signal Processing NCSP'12, 158-161, Honolulu, Mar. 2012. (Summary) In this paper, we investigate an intra-speaker's variability using a specific speaker's speech data witch are collected over long time period. Especially, we investigate a relationship between a speech recognition performance and a distance calculated by dynamic programming matching algorithm (we called ''DP distance'' hereafter), and a time dif- ference of a speech recognition day and an acoustic model training day. From the investigation results, we can see that the intra-speaker's speech variability varies the recognition performance. Hence, for restricting the variation of speech recognition performances caused by intra-speaker's speech variability, we proposed an acoustic model selection method in this paper. The proposed method decides candidates of optimal acoustic model by using DP distance, and selects an optimal acoustic model by using likelihood. For evaluating the proposed method, we conduct speech recognition experiment using a male speaker's speech data collected over long time period. As a result, speech recognition accuracy of selected model by the proposed method is slightly degraded than that of the conventional method. However, the proposed method can select an optimal acoustic model with smaller calculation costs than the conventional method.
90.	Den Nagarekawa, Koji Kashihara, Momoyo Ito and Minoru Fukumi : A Cloth Simulation System to Select the Right SIze, Proceeding of 2012 International Workshop on Nonlinear Circuits, Communication and Signal Processing NCSP'12, 49-51, Honolulu, Mar. 2012.
91.	Yohei Takeuchi, Momoyo Ito, Koji Kashihara and Minoru Fukumi : A Novel Nonlinear Discriminant Analysis by Iterative Operations, Proceeding of 2012 International Workshop on Nonlinear Circuits, Communication and Signal Processing NCSP'12, 104-107, Honolulu, Mar. 2012. (Summary) In the past, Principal Component Analysis (PCA), Fisher Linear Discriminant Analysis (FLDA) and other feature analyses have been widely used as statistical techniques in pattern recognition. Furthermore, these techniques have been improved to extract better features for both classification and learning. For instance, Simple-PCA (SPCA) and Simple-FLDA (SFLDA) have been proposed to learn an eigenspace not only easier but also faster by iterative operations. However, the eigenspace by these feature extraction algorithms might not be adequate to every dataset, because these are linear feature extraction methods. In this paper, we propose a new Simple-FLDA algorithm applied Kernel method, which leads nonlinear feature extraction. Therefore, the proposed algorithm is simply constructed. In addition, kernel base vectors, which are projected from high-dimensional space to inner product space, are used for feature extraction. The same criterion for discriminant analysis is used by using these kernel base vectors. In the experiment, 10% better classification accuracy is acquired when one dataset has high dimensionality.
92.	Koji Kashihara, Momoyo Ito and Minoru Fukumi : Estimation of venous shapes acquired from CMOS camera images., Proceedings of the Eighteenth Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV2012), 47-52, Kawasaki, Feb. 2012.
93.	Momoyo Ito, Kazuhito Sato, Minoru Fukumi and Ikuro Namura : Brain Tissues Segmentation for Diagnosis of Alzheimer-Type Dementia, Proceedings on IEEE Nuclear Science Symposium, Medical Imaging Conference, 3847-3849, Valencia, Oct. 2011. (Summary) In this paper, we specifically discuss segmentation of brain tissues which are used for calculation of atrophy rate. We proposed a brain tissue segmentation method using two kinds of unsupervised neural networks: Self-Organizing Maps (SOMs) and Fuzzy Adaptive Resonance Theory (ART). The performance of proposed method was tested in two brain MR images used in daily diagnosis. Proposed method could segment CSF accurately with continuity of brain tissues. (Link to Publication Site) ● Publication site (DOI): 10.1109/NSSMIC.2011.6153731 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84863349876 (DOI: 10.1109/NSSMIC.2011.6153731, Elsevier: Scopus)
94.	Momoyo Ito, Kazuhito Sato, Hirokazu Madokoro, Koji Kashihara and Minoru Fukumi : Basic Studies for Estimation of Driver's Internal States Using Head Positions, Proceedings on 4th International Symposium on Applied Sciences In Biomedical and Communication Technologies (ISABEL), Barcelona, Oct. 2011. (Summary) In this paper, we analyze driving movies taken by monocular in-vehicle camera, and examine drivers head position category in safety verification at intersections for quantification of head motion information. Moreover, we propose a quantifiable categorizing algorithm of head motion using two kinds of unsupervised neural networks. Through an experiment on actual driving data, the results provide a possibility of quantification of individual head position in safety verifications.
95.	Koji Kashihara, Momoyo Ito and Minoru Fukumi : Development of automatic filtering system for individually unpleasant data detected by pupil-size change., Proceedings of 2011 IEEE International Conference on Systems, Man, and Cybernetics, 3311-3316, Anchorage, Oct. 2011. (Link to Publication Site) ● Publication site (DOI): 10.1109/ICSMC.2011.6084180 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-83755228678 (DOI: 10.1109/ICSMC.2011.6084180, Elsevier: Scopus)
96.	Koji Kashihara, Momoyo Ito and Minoru Fukumi : An analytical method for face detection based on image patterns of EEG signals in the time-frequency domain., Workshop on Brain-Machine Interfaces, Proceedings of 2011 IEEE International Conference on Systems, Man, and Cybernetics, 25-29, Oct. 2011. (Link to Publication Site) ● Publication site (DOI): 10.1109/ICSMC.2011.6083637 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-83755228783 (DOI: 10.1109/ICSMC.2011.6083637, Elsevier: Scopus)
97.	Koichirou Mori, Momoyo Ito, Kazuhito Sato, Koji Kashihara and Minoru Fukumi : Analysis of Relationship between Head Motion Information and Driving Scene for Dangerous Driving Forecast, Proc. of SICE Annual Conference 2011, 2705-2709, Tokyo, Sep. 2011. (Summary) In this study, we are aiming to detect deviation signal which has a possibility to cause car accidents, using drivers head motion information. The head motion information is extracted from only 2D camera images. Our method is able to extract the information without alignment of facial positions. In this paper, SOMs categorization results of our method are discussed and evaluated.
98.	Yohei Takeuchi, Momoyo Ito, Koji Kashihara and Minoru Fukumi : Supervised Iterative Learning Algorithm for Eigenspace Models, Proc. of SICE Annual Conference 2011, 2361-2365, Tokyo, Sep. 2011. (Summary) In pattern recognition, the principal component analysis (PCA) is one of the most famous feature extraction methods for dimensionality reduction of high-dimensional datasets. Furthermore, Simple-PCA (SPCA) which is a faster version of the PCA, has been carried out effectively by iterative operated learning. However, in SPCA, when input data are distributed in a complex way, SPCA might not be efficient because it is learned without class information of the dataset. Thus, SPCA cannot be said that it is optimal for classification. In this paper, we propose a new learning algorithm, which is learned with the class information of the dataset. Eigenvectors spanning eigenspace of the dataset are obtained by calculation of data variations belonging to each class. We will show the derivation of the proposed algorithm and demonstrate some experiments to compare the SPCA with the proposed algorithm by using UCI datasets. (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-81255138733 (Elsevier: Scopus)
99.	Yohei Takeuchi, Momoyo Ito, Koji Kashihara and Minoru Fukumi : Novel Supervised Feature Extraction Algorithm Based on Iterative Calculations, Proc. of The IEEE International Conference on Information Reuse and Integration (IRI2011), 304-308, Las Vegas, Aug. 2011. (Summary) In pattern recognition, the principal component analysis (PCA) is one of the most famous feature extraction methods for dimensionality reduction of high-dimensional datasets. Furthermore, Simple-PCA (SPCA) which is a faster version of the PCA, has been carried out effectively by iterative operated learning. However, in SPCA, when input data are distributed in a complex way, SPCA might not be efficient because it is learned without class information of the dataset. Thus, SPCA cannot be said that it is optimal for classification. In this paper, we propose a new learning algorithm, which is learned with the class information of the dataset. Eigenvectors spanning eigenspace of the dataset are obtained by calculation of data variations belonging to each class. We will show the derivation of the proposed algorithm and demonstrate some experiments to compare the SPCA with the proposed algorithm by using UCI datasets. (Link to Publication Site) ● Publication site (DOI): 10.1109/IRI.2011.6009564 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-80053163893 (DOI: 10.1109/IRI.2011.6009564, Elsevier: Scopus)
100.	Yohei Takeuchi, Momoyo Ito, Koji Kashihara and Minoru Fukumi : Supervised Feature Extraction Algorithm by Iterative Calculations, Proc. of The 2nd Conference on Next Generation Information Technology (ICNIT2011), 46-49, Gyounju, Jun. 2011. (Summary) In pattern recognition, the principal component analysis (PCA) is one of the most famous feature extraction methods for dimensionality reduction of high-dimensional datasets. Furthermore, Simple-PCA (SPCA) which is a faster version of the PCA, has been carried out effectively by iterative operated learning. However, in SPCA, when input data are distributed in a complex way, SPCA might not be efficient because it is learned without class information of the dataset. Thus, SPCA cannot be said that it is optimal for classification. In this paper, we propose a new learning algorithm, which is learned with the class information of the dataset. Eigenvectors spanning eigenspace of the dataset are obtained by calculation of data variations belonging to each class. We will show the derivation of the proposed algorithm and demonstrate some experiments to compare the SPCA with the proposed algorithm by using UCI datasets.
101.	Masato Miyoshi, Satoru Tsuge, Tadahiro Oyama, Momoyo Ito and Minoru Fukumi : Feature Selection Method for Music Mood Score Detection, Proc. of ICMSAO'2011, 713-718, Kuala Lumpur, Apr. 2011. (Summary) In general, music retrieval and classification methods using music moods use a lot of acoustic features similar to music genre classification. These features are used as the spectral features, the rhythm features, the harmony features, and so on. In this paper, we propose a feature selection method for detecting music mood scores. In the proposed method, features which have strong correlation with mood scores are selected from a lot of features. Then, these are input into Multi-Layer Neural Networks (MLNNs) and mood scores are detected every mood labels. For evaluating the proposed method, we conducted the music mood score detection experiments. Experimental results show that the proposed method improves the detection performance compared to not use the feature selection. (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-79959649866 (Elsevier: Scopus)
102.	Takuya Shiraishi, Atsushi Ishitani, Momoyo Ito, Stephen Githinji Karungaru and Minoru Fukumi : Operation Improvement of Indoor Robot by Gesture Recognition, Proc. of ICMSAO'2011, 572-575, Kuala Lumpur, Apr. 2011. (Summary) Recently, the demand for the indoor robots has increased. Therefore, increased opportunities for many people to operate the robots have emerged. However, for many people, it is often difficult to operate a robot using the conventional methods like remote control. To solve this problem, we propose a robot operation system using the hand gesture recognition. Our method pays attention to the direction and movement of the hand. We were able to recognize several gestures in real-time. (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-79959640449 (Elsevier: Scopus)
103.	Yohei Takeuchi, Momoyo Ito and Minoru Fukumi : Novel Approximate Stastical Learning Algorithm for Large Complex Datasets, Proc. of ICMLC'2011, Vol.3, 236-239, Singapore, Feb. 2011. (Summary) In pattern recognition, simple Principal Component Analysis (SPCA) method is one of the most famous feature extraction methods for dimensionality reduction of high-dimensional image datasets. However, when input data are distributed in a complex way, SPCA which is a linearly feature extraction method, might not be efficient. In such cases, kernel method, which is nonlinear feature extraction method, was shown its high generalization capability. In this paper, we apply kernel method to SPCA, and show the derivation. Finally, it was tested and compared with original SPCA algorithm by using Japanese paper currency images.
104.	Atsushi Ishitani, Takuya Shiraishi, Stephen Githinji Karungaru, Momoyo Ito and Minoru Fukumi : A Simple Interface for Mobile Robot Using Motion Stereo Vision, Proc. of ICMLC'2011, Vol.2, 174-178, Singapore, Feb. 2011. (Summary) In remote control, user interfaces are very important and it is desired that they have intuitive operability and be simple systems. However, presently, most of interfaces have not satisfied these needs. Therefore, we propose a simple interface to control mobile robots equipped with a camera by instructing it to move to a desired location using the image set to a screen. For the robot to control its movement, it needs to calculate the three-dimensional information of the instructed point. In this work, we calculate the three-dimensional position using motion stereo vision, and perform experiments using a wheeled mobile robot with a single camera in a real environment.
105.	Hitoshi Takano, Stephen Githinji Karungaru, Momoyo Ito and Minoru Fukumi : Stop Sign Recognition from Drive Scenes, Proc. of ICMLC'2011, Vol.1, 549-552, Singapore, Feb. 2011.
106.	Momoyo Ito, Kazuhito Sato, Ikuro Namura and Minoru Fukumi : Extraction of Brain Regions for Image Diagnosis of Alzheimer-type Dementia Based on Atrophy Progress Speeds, Conference Record of 2010 IEEE Nuclear Science Symposium and Medical Imaging Conference, M19-375, Knoxville, Tennessee, USA, Nov. 2010. (Summary) In this paper, we propose an extraction method of the ROI fitting to individual brain shape and size using two deformable models hierarchically. Moreover, we discuss diagnostic application using atrophy progress speeds briefly. (Link to Publication Site) ● Publication site (DOI): 10.1109/NSSMIC.2010.5874476 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-79960338890 (DOI: 10.1109/NSSMIC.2010.5874476, Elsevier: Scopus)
107.	Kazuhito Sato, Sakura Kadowaki, Hirokazu Madokoro, Momoyo Ito and Atsushi Inugami : Unsupervised Segmentation of MR Images for Brain Dock Examinations, Conference Record of 2010 IEEE Nuclear Science Symposium and Medical Imaging Conference, M09-421, 2370-2371, Knoxville, Tennessee, USA, Nov. 2010. (Link to Publication Site) ● Publication site (DOI): 10.1109/NSSMIC.2010.5874210 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-79960326679 (DOI: 10.1109/NSSMIC.2010.5874210, Elsevier: Scopus)
108.	Masahito Miyoshi, Hillary Kipsang Choge, Satoru Tsuge, Tadahiro Oyama, Momoyo Ito and Minoru Fukumi : Music Impression Detection Method for User Independent Music Retrieval System, Proc. of KES'2010, 612-621, Wales (U.K.), Sep. 2010. (Summary) In previous work, we have proposed the automatic sensitive word score detection system for a user dependent music retrieval system. However, the user dependent method causes a lot of burdens to the user because the system requires a lot of data for adapting it to each user. Hence, in this paper, we propose an automatic sensitive word score detection method for a user independent music retrieval system and evaluate the proposed method using 225 music data. Experimental results show that 87.5% of music patterns succeeded in detection of sensitive word score in the case that the difference between estimated and evaluated score is 1 (Error 1 rate). Moreover, we conduct subjective evaluation experiments to evaluate the proposed method as a utility method. From this experiment, it is observed that the user satisfaction level of the proposed method is higher than random selection impression detection.
109.	Momoyo Ito, Makoto Nishida and Ikuro Namura : Extraction Method of Brain Regions with Balloon models for Imaging Diagnosis Support of Alzheimer-Type Dementia, The 6th Inter. Conf. on Materials Engineering for Resources, 324-327, Oct. 2009. (Summary) In this paper, we propose an extraction method of the ROI fitting to individual brain shape and size using two deformable models hierarchically. Moreover, we discuss diagnostic application using atrophy progress speeds briefly.

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.	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．
8.	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
9.	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
10.	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
11.	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
12.	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
13.	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
14.	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
15.	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
16.	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
17.	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
18.	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
19.	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
20.	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
21.	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
22.	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
23.	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
24.	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
25.	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
26.	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
27.	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
28.	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
29.	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
30.	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
31.	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
32.	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
33.	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
34.	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
35.	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
36.	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
37.	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
38.	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.
39.	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.
40.	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
41.	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
42.	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
43.	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
44.	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
45.	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
46.	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.
47.	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
48.	Shohei Umino, Kazuhito Sato, Momoyo Ito, Hirokazu Madokoro and Sakura Kadowaki : Classification of Driving Scenes Using Recurrent-SOMs, 自動車技術会2016年春季大会学術講演会講演予稿集, 156-161, May 2016.
49.	Masahumi Sawataishi, Kazuhito Sato, Momoyo Ito, Hirokazu Madokoro and Sakura Kadowaki : Development of Driver Monitoring Tool: Focused on Driver's Body Information, 自動車技術会2016年春季大会学術講演会講演予稿集, 150-155, May 2016.
50.	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．
51.	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．
52.	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．
53.	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.
54.	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.
55.	Kazuhito Sato, Kentaro Katsumata, Momoyo Ito, Hirokazu Madokoro and Sakura Kadowaki : Driver Body Information Analysis for Distraction State Detection, FIT2015第14回情報科学技術フォーラム講演論文集, 35-42, Sep. 2015.
56.	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
57.	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
58.	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
59.	Kazuhito Sato, Daiki Kato, Momoyo Ito, Hirokazu Madokoro and Sakura Kadowaki : Driver Body Information Analysis: Near-miss in a Non-regulated Intersection (Second Report), Proceedings of Society of Automotive Engineers of Japan Spring Conference, 2228-2231, May 2015. (Summary) In this study, for non-regulated intersections with poor visibility, we focus on safety verification behaviors and near-miss events by cross over and sudden appearance of bicycles. By paying attention to time-series changes of face orientations and eye-gaze movements in before and after of near-miss events, we carry out the comparative analysis of safety verification behaviors and eye-gaze modes in distraction and driving concentration states.
60.	Momoyo Ito, Kazuhito Sato and Minoru Fukumi : A Time - series Analysis of Head Posture based on Traffic Events, Proceedings of Society of Automotive Engineers of Japan Spring Conference, 2232-2235, May 2015. (Summary) This study aims to estimate drivers state using driving behaviors. In this paper, we set a bicycle near-miss event on driving simulator: sudden appearance of bicycle. We examine safety verification behaviors associated with near-miss events at nonregulated intersections with poor visibility. From an assessment of a drivers head movements associated with the sudden appearance of bicycle encountered while approaching a nonregulated intersection, we attempt to analyze the causal relation of the changes of frequency analysis results of safety verification behaviors before and after near-miss events.
61.	Kentaro Katsumata, Kazuhito Sato, Momoyo Ito, Hirokazu Madokoro and Sakura Kadowaki : Analysis of Safety Verification Behavior by Gaze Timing Based on Eye-gaze Movement and Face Orientation, Proceedings of Society of Automotive Engineers of Japan Spring Conference, 1111-1114, May 2015. (Summary) We designed four running scenarios to control the weather (i.e.,sunny/rainy) and time (i.e.,day/night) using driving simulator. Moreover, we analyzed gaze timing using eye-gaze movement and face orientation for the sudden appearance of the bicycle. As a result, the timing discovering the bicycle differed according to weather and time. Therefore, we confirmed delay discovering the bicycle for a rainy day and nighttime when drivers feel to be a burden for searching routes.
62.	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．
63.	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．
64.	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.
65.	Kazuhito Sato, Daiki Kato, Kentaro Katsumarta, Momoyo Ito, Hirokazu Madokoro and Sakura Kadowaki : Analysis of Driver Body Information and Driving Style with Near-miss Events, Proceedings of Forum on Information Technology 2014, Part 3, 5-12, Sep. 2014.
66.	Momoyo Ito, Kazuhito Sato and Minoru Fukumi : Analysis of Time-Series Changes of Head Posture for Estimation of Drivers States, Proceedings of Forum on Information Technology 2014, Part 3, 256-258, Sep. 2014. (Summary) 本研究では，ドライバの運転行動を用いた危険予測モデルの作成を目指している．本稿では，その基礎的検討として，見通しの悪い無信号交差点を対象とし，交差点における安全確認行動を構成する頭部姿勢の時系列変化について考察する．具体的には，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評価する．
67.	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．
68.	Daiki Kato, Kazuhito Sato, Momoyo Ito, Hirokazu Madokoro and Sakura Kadowaki : Design and Evaluation of Running Scenarios for Near-Miss Events Verification, Proceedings of 2014 JSAE Annual Congress (Spring), Vol.6-14, 13-18, May 2014.
69.	Kazuhito Sato, Kentaro Katsumata, Momoyo Ito, Hirokazu Madokoro and Sakura Kadowaki : Driver Body Information Analysis: Near-miss in a Non-regulated Intersection, Proceedings of 2014 JSAE Annual Congress (Spring), Vol.12-14, 1-6, May 2014.
70.	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．
71.	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.
72.	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.
73.	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．
74.	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．
75.	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．
76.	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．
77.	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．
78.	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.
79.	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.
80.	Momoyo Ito, Kazuhito Sato and Minoru Fukumi : 教師なしニューラルネットワークによるドライバの頭部姿勢分類, Proceedings of Forum of Information Technology 2013, 501-506, Sep. 2013. (Summary) 本研究では，ドライバの頭部姿勢の3次元情報から，安全確認動作のモデル化を行うことで，危険運転を引き起こすきっかけとなる平常状態からの継続的逸脱を検出するシステムの構築を目的とする．本稿では，安全確認動作のモデル化のための頭部姿勢定量化手法である，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手法について，ニューラルネットワークのパラメータ調整による安全確認行動時の頭部姿勢カテゴリ分類結果について考察を加える．
81.	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.
82.	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
83.	Momoyo Ito, 佐藤和人 and Minoru Fukumi : 安全確認動作の状況分析に基づく頭部姿勢空間のカテゴライズ, 第11回情報科学技術フォーラム講演論文集, 407-412, Sep. 2012. (Summary) 本研究では，ドライバの安全確認動作のモデル化を行うことで，危険運転を引き起こすきっかけとなる平常状態からの継続的逸脱を検出するシステムの構築を目的とする．本稿では，安全確認動作のモデル化のための頭部姿勢定量化手法として，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手法を提案し，その分類特性について考察を加えた．
84.	Yohei Takeuchi, Momoyo Ito and Minoru Fukumi : Iterative Discriminant Analysis in Non-linear Space, IEICE Technical Report, Vol.112, No.69, 59-64, May 2012.
85.	佐藤和人, Momoyo Ito, 間所洋和 and 門脇さくら : Quantification of Psychological Stress Using Expressive Tempos and Rhythms, IEICE Technical Report, Vol.112, No.69, 39-44, May 2012.
86.	Momoyo Ito, 佐藤和人 and Minoru Fukumi : Evaluation of Self Mapping Characteristics for Quantification of Head Motion, IEICE Technical Report, Vol.112, No.69, 17-20, May 2012. (Summary) 本研究では，ドライバの安全確認動作定量化を目的とし，2種類の教師なしニューラルネットワークを用いて，個人ごとに異なる頭部姿勢数に適応したカテゴライズを目指している．本稿では，実車環境で取得されたドライバの画像を対象に，ドライバ固有の安全確認動作に適したSOMsの自己写像特性について検討を加えた．
87.	Takahide Funabashi, Momoyo Ito, Koji Kashihara and Minoru Fukumi : EMGを用いた指の動作識別, 計測自動制御学会四国支部講演会, No.PS2-23, Nov. 2011.
88.	Koji Kashihara, Keisuke Takahashi, Momoyo Ito and Minoru Fukumi : 微小レベルの映像信号から静脈形状を高精度に推定するための解析手法の検討, IEICE Technical Report, Vol.111, No.257, 49-54, Oct. 2011.
89.	高橋啓介, Koji Kashihara, Momoyo Ito and Minoru Fukumi : CMOSイメージセンサを用いた近赤外線カメラによる静脈画像の解析, 第34回日本生体医工学会中国四国支部大会プログラム講演抄録, 51, Oct. 2011.
90.	Yohei Takeuchi, Momoyo Ito, Koji Kashihara and Minoru Fukumi : 繰り返し演算による教師あり特徴抽出手法の開発, 高速信号処理応用技術学会研究会, 10-11, Aug. 2011.
91.	Momoyo Ito, 盛紘一朗, 井口祐也, 佐藤和人 and Minoru Fukumi : 危険運転予測のための頭部動き情報と運転シーンの関連性の分析, 動的画像処理実利用化ワークショップ2011講演概要集, 198-201, Mar. 2011. (Summary) 本研究では，ドライバの頭部姿勢に着目した危険運転予測を目指している．頭部動き情報をモデル化するにあたり，運転時の頭部の動きには，運転シーンとどのような関連性を有するか，また，どのようなカテゴリが存在するかを分析し，その結果に基づいて定量化を行う必要がある．本稿では，交差点での安全確認動作に着目して運転シーンと頭部の動きの関連性について考察を加えた．
92.	Masato Miyoshi, Satoru Tsuge, Hillary Kipsang Choge, Tadahiro Oyama, Momoyo Ito and Minoru Fukumi : 音楽検索のための楽曲印象値の自動付与手法, 第89回情報処理学会音楽情報科学研究会, Feb. 2011.
93.	Hiroyuki Mitsuhara, Masami Shishibori, Hiroaki Ogata, Masao Fuketa, Hitoshi Tokushige, Kazuhiro Morita, Kazuyuki Matsumoto, Shun Watanabe and Momoyo Ito : Integrating Entertainment in Software Design and Experiment and its Effect, 日本教育工学会第26回全国大会講演論文集, 787-788, Sep. 2010.
94.	森健太郎, Satoru Tsuge, Momoyo Ito and Minoru Fukumi : 話者依存音声認識のための発音辞書・音響モデル適応手法, 平成22年電気学会電子・情報・システム部門大会講演論文集, 698-701, Sep. 2010.
95.	石谷淳, 白石卓也, Momoyo Ito, Stephen Githinji Karungaru, Satoru Tsuge and Minoru Fukumi : モーションステレオを用いた移動ロボット向けユーザインタフェース, 平成22年電気学会電子・情報・システム部門大会講演論文集, 694-697, Sep. 2010.
96.	田岡文利, Momoyo Ito, Stephen Githinji Karungaru, Satoru Tsuge and Minoru Fukumi : 証明変動に頑健な放置物検出法, 平成22年電気学会電子・情報・システム部門大会講演論文集, 689-693, Sep. 2010.
97.	Momoyo Ito, 佐藤和人, Minoru Fukumi and 苗村育郎 : 脳萎縮の進行速度に基づく認知症診断支援のための脳領域抽出法, 平成22年電気学会電子・情報・システム部門大会講演論文集, 683-688, Sep. 2010. (Summary) 本稿では，個人の脳構造に沿った側頭葉領域抽出の高精度化を目的に，AAM探索時の初期位置に再利用する再帰型領域抽出法を提案する．評価実験では読影医がATD患者の脳萎縮状況に着目し重症度を分類した34例(MR脳画像34枚)を対象とし，提案手法による関心領域の抽出精度について評価を行った．さらに，提案手法により抽出された関心領域と手動抽出された関心領域における萎縮率の比較を行い，萎縮率の再現性についても有用性を示す．

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.	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
5.	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
6.	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
7.	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
8.	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
9.	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.
10.	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.
11.	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.
12.	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.
13.	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.
14.	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.
15.	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.
16.	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.
17.	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.
18.	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.
19.	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.
20.	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.
21.	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.
22.	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.
23.	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.
24.	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.
25.	Hiroyuki Mitsuhara, Masami Shishibori, Hiroaki Ogata, Masao Fuketa, Hitoshi Tokushige, Kazuhiro Morita, Kazuyuki Matsumoto, Shun Watanabe and Momoyo Ito : 知能情報工学科``ソフトウェア設計及び実験''のエンタテインメント化の試行, 平成22年度全学FD大学教育カンファレンス in 徳島発表抄録集, 12-13, Jan. 2011.

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

Construction of Distraction State Estimation System focusing on Driver's Posture at Intersection (Project/Area Number: 16K16101 )
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Associate Professor : Ito, Momoyo

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