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


Stimulation and Visibility of Flashing Lights With Different Illuminance

Proceedings of the AHFE 2023 International Conference on Human Factors in Transportion

This study aimed to present a demonstration experiment to determine a highly visible light emission pattern while feeling that the stimulus is suppressed using a sensory evaluation. A sensory evaluation of stimulus intensity and visibility of simple blinking that repeats on and off and fade-in/fade-out blinking that gradually brightens andfades was performed to investigate the trade-off relationship between stimulus adaptation and arousal. Light-emitting blocks that are constantly lit, simple blinking, and fade-in/fade-out blinking were compared, and the relationship between the blinking stimulus and visibility was clarified. The subjects underwent a sensory evaluation forthree types of environmental illuminance (20, 50, and 100 lx) and were asked to evaluate the strength of stimulation, visibility, and discomfort of each blinking pattern using normal light as a reference. When the surrounding illuminance was low (20 lx), the overall evaluation value was higher because the fade-in/fade-out flashing was less stimulating and easier to find. When the surrounding illuminance was high (50 lx), simple blinking was more stimulating and easier to find, and the overall evaluation value was higher. Although it was a simple blinking, it seemed that the visibility forpeople with visual impairment had been secured. Moreover, when the surrounding illuminance was 100 lx, the difference in the illuminance between the surroundings and the light-emitting block was small, the light-emitting block in both simple blinking and fade-in/fade-out blinking was difficult to find, and the overall evaluation value was very low. Simple or fade-in/fade-out blinking is selected based on the surrounding illuminance. By installing light-emitting blocks that blink in an optimal blinking pattern at the entrance of a crosswalk, it is expected that the blinking will be effective in guiding people with visual impairment while considering pedestrians and the surrounding environment.


On the Stimulation and Visibility by Blinking Light Emitting Block for Low Vision

Proceedings of the AHFE 2022 International Conference on Human Factors in Transportion

Approximately 80% of people with visual impairment have residual visual acuity. As such, the authors have developed a light-emitting block that is placed at the entrance of a pedestrian crossing to support those with visual impairment at night. A blinking light has generally been used for selectively distinguishing the target light source from the surrounding light source. However, this type of blinking (i.e., simple blinking) can be stimulating, making it suitable for night-time road construction sites but not for normal road environments. Therefore, the authors have focused on phase-in-phase-out blinking, which gradually brightens and darkens with each blink, thereby suppressing stimulation. This blinking pattern has been considered to reduce visibility just by suppressing the stimulus. However, focusing on the human sensory characteristics of adaptation and arousal, evidence has suggested the existence of a blinking cycle and pattern that awakens while suppressing stimuli. Therefore, the current study sought to identify blinking cycles and patterns that awaken while suppressing light stimulation. The intensity of light stimulation is evaluated via sensory evaluation. Based on the intensity of the stimulus and degree of arousal, we searched for the optimum blinkingcycle that promotes adaptation, a human sensory characteristic, and arousal from the blinking pattern and verified the visibility of the obtained blinking pattern in individuals with visual impairment and healthy subjects. Previously, we had conduct-ed an experiment in a region with a period of 47 s. This time, however, we con-ducted an experiment in a region with a period of 2 3.5 s. This study verified that phasein-phase-out blinking is effective for pedestrians while considering the surroundings. The purpose of this experiment was to determine the optimal blinking method for supporting people with visual impairment. Although it is desirable to provide support for people with visual impairment by promoting stronger visibility, introducing flashing blinks in public facilities may cause discomfort to healthy people around them. For instance, introducing a blinking light of approximately 1 Hz, which has a fast blinking cycle, at the entrance of a pedestrian crossing can enter the drivers field of vision, causing discomfort and making the driver look away, thereby leading to accidents. The ideal blinking light for support is that which limits arousal to some extent anddoes not induce discomfort. Based on the blinking patterns used in this experiment under the aforementioned condition, we believe that a fade-in-fade-out type pattern with a blinking cycle of 2 s and a lighting time of 2 and 3 s would be effective. The reason for this is that the intensity of the stimulus obtained from the sensory evaluation of healthy subject and those with visual impairment is suppressed, visibility is secured to some extent, and the blinking method does not induce discomfort. Thus, the fade-in-fade-out blinking method can be expected to suppress discomfort.


Visual Guidance by Blinking Light of LED Block for Individuals Affected with Low Vision

Proceedings of the AHFE 2021 International Conference on Human Factors in Transportion

Approximately 80% of the visually impaired are composed of individuals with low vision. As people with low vision have residual visual acuity, light guidance has been deemed effective. Thus, the authors have developed a light-emitting block that is placed at the entrance to a pedestrian crosswalk to support the visually impaired at night. Generally, blinking light is accepted as a method to selectively distinguish a target light source from an ambient light source. However, on/off blinking lights, such as those used at a road construction site at night, are unsuitable for normal road environments. Therefore, the authors focused on fade-in/fade-out blinking, which gradually brightened and darkened, suppressing inappropriate visual stimulation. Therefore, this study aimed to search for blinking cycles and patterns that ensured visibility while suppressing light stimulation and reducing discomfort.


Manual Wheelchair Tire Pressure and Ride Comfort When Pushed by a Caregiver

Wheelchairs are very convenient for the elderly who cannot walk, but the vibration generated during use can cause discomfort to the rider and can have consequences, such as convulsions. Tire pressure is one of the factors thataffects the ride quality of wheelchairs. Wheelchairs are often used indoors, in which case they travel on hard flooring or on carpet. The purpose of this study was to investigate how the road surface on which the wheelchair travels and thetire pressure of the wheelchair contribute to the vibration experienced by the rider, and the influence of the vibration on the human body. We used an air pressure indicator that measured an English valve. The vibration generated was measured and the sensory perception of comfort and security were evaluated on hard and soft surfaces at different tire pressures. The vibration test revealed that vibration tends to increase as tire pressure increases and that ride comfortdecreases as tire pressure increases.


Personal Authentication and Recognition of Aerial Input Hiragana Using Deep Neural Network

Proceedings Volume 11794, Fifteenth International Conference on Quality Control by Artificial Vision

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.


Separation of Compound Actions with Wrist and Finger Based on EMG

Proc. SPIE 11794, Fifteenth International Conference on Quality Control by Artificial Vision

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%.


Motion Identification of fingerspelling by Wrist EMG Analysis

Proc. 2020 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE

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.


Finger Motion Identification Based on Wrist EMG Analysis Using Machine Learning

Proc. of International Conference on System Science and Engineering 2020

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%.


Effects of Tire Pressure on Wheelchairs When Riding on Uneven Ground

Proceedings of the AHFE 2020 International Conference on Human Factors in Transportion

Wheelchairs for the elderly and people with disabilities are very convenient, but vibration generated during movement may cause user discomfort and even motion sickness. Tire pressure is one factor affecting wheelchair ride quality. The commonly used English-style valve for wheelchairs allows air to be filled into the tire but cannot be used to adjust tire air pressure. Therefore, it is unclear how wheelchair tire pressure affects the vibration of the wheelchair.Additionally, it is necessary to consider the burden on the assistant. Wheelchairs are often used indoors on hard floors and relatively flexible carpets. The purpose of this study is to clarify the relationship between vibrations generated in a wheelchair, the surface on which a wheelchair travels, tire pressure, and the influence of these vibrations on the human body. By using an air pressure indicator suitable for use with the English-style valve to measure tire air pressure, vibration measurements were conducted under controlled conditions by rolling a manual wheelchair at constant speed. The vibration test revealed that the vibration tends to increase as tire pressure increases. Additionally, it wasfound that ride comfort decreases as tire pressure increases.


Japanese Sign Language Classification Using Gathered Images and Convolutional Neural Networks

Proceedings of 2020 IEEE 2nd Global Conference on Life Sciences and Technologies (LifeTech)

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.


Personal Authentication by Walking Motion using Kinect

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.


Study on Discrimination of Finger Motions based on EMG signals

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.


A Nail Image Analysis Method to Evaluate Accumulated Stress Using Fuzzy Reasoning

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.


Detection of Dangerous Objects By Pan-tilt Camera

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.


Verication of Regression Analysis of Muscle Fatigue Using Wrist EMG

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.


Electroencephalogram Data for Classifying Answers to Questions with Neural Networks and Support Vector MachineNetworks

Proceedings of International Conference on Electronics and Signal Processing, ICESP2019

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.


Proposal of Japanese Sign Language Motion Recognition Method using Leap Motion

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.


Driver State Estimation Based on Visual and Heart Rate Statistical Features

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.


Construction of Japanese Vowel Identification System Using Lips EMG

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.


An authentication system for aerial input numerals using Leap motion and CNN

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.


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

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.


Detection of Dangerous Objects By Pan-tilt Camera

Proceedings of The Fifth International Conference on Electronics and Software Science ICESS2019, Japan, 2019

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.


Multiclass Classification and Regression Analysis of Muscle Fatigue Using Wrist EMG

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.


A Method to Evaluate Accumulated Stress Using Nail Image

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.


A Method to Extract Change of Lunula of the Nail

The Institute of Electrical Engineers of Japan

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.


A Method to Detect Presence or Absence of Learning Understanding Using Center Cumulative Frequency Comparison Method and Multistage ICA

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.


A Method to Estimate Pressing Positions of Guitar String for Guitar Solo Skill Acquisition

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.


An Electroencephalogram Analysis Method to Detect Answers of Questions Using Machine Learning Techniques

2019 International Conference for Leading and Young Computer Scientists

Asia Pasific Society for Computing and Information Technology

This paper introduces any methods to detect answers of questions based on an electroencephalogram (EEG) analysis techniques. The proposed methods consist of three phases; EEG recordings, EEG feature extraction, answers detection. In the EEG recordings, the simple electroencephalograph is used to record the EEG signals. This device has lots of sensors that are dry type. It is easy to wear the device. In the EEG feature extraction, the frequency analysis technique and convolutional neural networks (CNNs) are employed to calculate the features of the EEG signals. Fast Fourier transform is used to calculate the power spectra of the EEG signals. The CNNs are used to extract the EEG features related to the answers of the questions. In the answers detection, the multi-layer perceptron (MLP), support vector machine (SVM) are employed to classify the answers of questions (yes or no). In order to show the effectiveness of the proposed method, we conduct the experiments using real EEG data sets. This paper has two experiments. In experiment 1, the proposed method classifies the answers of questions on daily conversation. The EEG feature is extracted using the FFT and the CNNs. The answers are classified using the SVM. In the experimental results, we confirm that the recognition accuracy is 90% or more. In experiment 2, the proposed method classified the answers of questions on understanding the contents of the learning material. The EEG feature is extracted using the FFT by selecting the EEG recording positions. The answers are classified using the MLP. In the experimental results, we confirm that the recognition accuracy is 70% or more. Then, best of the recognition accuracy is 90% or more. These results suggest that it is possible to detect the answers of questions using the proposed method.


Recognition of Aerial Input Numerals by Leap Motion and CNN

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.


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

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%.


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

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.


Personal Authentication by Lips EMG Using Dry Electrode andCNN

Proc. of 2018 International Conference on Internet of Things and Intelligence System (IOTAIS)

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.


Authentication of Aerial Input Numerals by Leap Motion and CNN

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.


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%.


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

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.


A Method to Check whether Human Understands Contents of Learning Using Electroencephalogram

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.


An Electroencephalogram Analysis Method to Classify Answers of Questions Using Deep Neural Networks

International Conference on Information Technology and Computer Science

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.


Verification of blinking LED block installed at crosswalk entrance for low-vision people

Proceedings of the AHFE 2018 International Conference on Human Factors in Transportion

There are individual differences in the degree of disorder and appearance of low vision people, but in many cases discrimination of light and darkness is possible. Therefore, it is considered effective to use flashing light as support for low vision people. As support for such low vision, research is being conducted in this laboratory to introduce a light emitting blinking block at the entrance to the pedestrian crossing. In this study, as an introduction study of the development of luminescent block for support to amblyopia, we asked subjects to evaluate the intensity of blinking stimulus when presenting blinking light to subjects and the goodness of visibility by sensory evaluation, Consider the blinking method best suited for support.


An Electroencephalogram Analysis Method to Detect Preference Using Gray Association Degree

The proceedings of International Conference on Electronics, Information, and Communication 2018

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%.


Identification of Visually Impaired Person with Deep Learning

Proceedings of the 1st International Conference on Intelligent Human Systems Integration (iHSI 2018)

The purpose of this study is to identify visually impaired persons by analyzing still pictures of walking of a visually impaired person and that of a healthy person using deep learning. Still images of walking are taking still pictures from video images. Shoot from sideways and diagonally with two video cameras. The number of images (with 1000 or 2000) and the dropout (three, two, or one time) was changed and analyzed. Because the study focused on only visually impaired persons (totally blind persons) and the healthy person's study machines of two patterns in the experiment, a correct answer rate of 99.9% for every 2000 images and 2 times of the dropout number was obtained.


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

the Institute of Industrial Applications Engineers (IIAE)

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.


Biometrics Authentication of Aerial Handwritten Signature Using a Convolutional Neural Network

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%.


Personal Authentication Based on Wrist EMG Analysis by a Convolutional Neural Network

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%.


Japanese Vowels Recognition Using Linear Discriminant Analysis and Surface Electromyogram Measured with Bipolar Dry Type Sensors

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.


Research on Blinking-Luminescence Travel Support for Visually Impaired Persons

Proceedings of the AHFE 2017 International Conference on Human Factors in Transportion

Of the five human senses, vision is said to account for 80% of the in-formation acquired. The presentation of light is an effective way to convey visual information. A blinking presentation especially attracts attention. A low-vision person acquires peripheral information through residual vision. In this case, light effectively supports the travel of low-vision persons. A blinking light stimulates more than a continuous light. However, a blinking light is not sufficiently compatible with the surrounding environment. This research investigates an environmentally friendly blinking pattern to arouse attention.


Verification of Installed Position of LED Block Equipped with Projections to Indicate Travel Direction

A crosswalk entrance is on the boundary of a roadway and is a dangerous area for visually impaired persons. Tactile walking surface indicators (TWSIs) are installed on the road to support independent travel for blind and low-vision individuals. We developed an LED block equipped with projections to strengthen support at the crosswalk entrance. Blind individuals can sense this block through the soles of their shoes. Results indicated that the LED block equipped with projections to locate travel direction effectively supports crossing. The effectiveness of this block is determined using measurement and questionnaire results.


Proceeding of International Conference on Advanced Technology & Sciences (ICAT'Rome)

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.


Biometric Authentication of Aerial Handwritten Signature Using a Convolutional Neural Network

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.


Japanese Janken Recognition Based on Wrist EMG Analysis by CNN and SVM

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.


Proof Experiment of LED Block Equipped with Projections to Locate Travel Direction for Blind and Vision Impaired Persons

Proceedings of the AHFE2016 International Conference on Human Factors in Transportation

The eyesight of a person with low visual capacity ("LV") might decrease remarkably under low-light conditions such as the morning, evening, and nighttime, decreasing their opportunities to go out. Crosswalk entrances are on the boundary with the roadway and are among the dangerous areas for visually impaired persons. An LV person cannot easily find the entrance of a crosswalk at nighttime. LV persons can detect the developed LED blocks with their residual vision. Moreover, for the blind person to cross the crosswalk safely, it is very important to acquire the direction at the soles of the shoes. The authors have verified the effectiveness of the developed block for visually impaired person by laboratory experiments. In this experiment, both visually impaired persons and blind persons found the developed block to effectively support crossing. Moreover, the time required for locating with the LED block was short . Orientation using the LED block required less time. The LED block was observed to have an orientation accuracy equivalent to that of TWSIs. Moreover, subjects expressed opinions such as "It was easy to locate" and "There is a sense of security when going straight" in interviews after the experiment. Installing LED blocks at crosswalk entrances was found to be useful for guiding visually impaired persons. Moreover, the shape of the projection indicating the direction is easily verified through the soles of the blind person's shoes. A LED block experiment was conducted to determine its actual efficiency. In this research, the visibility of the LED block was verified for different luminescence and illuminance environments. This result clarified how to provide illumination for a visually impaired person. Based on this research, the authors developed a LED block with projections to locate travel direction for a visually impaired person. The developed block can be detected with the LVs residual vision. Blind persons can also detect this block through the soles of their shoes. This block was constructed at the entrance to a crosswalk of an actual intersection, and a proof experiment was conducted. Twenty persons with weak sight and 20 totally blind people verified the effectiveness of this block. Especially, the effectiveness of the experiment on the LED block by the LVs can be verified by doing after the sunset. This experiment measured the visibility of the LED block at the pavement side and the opposite side. In the experiments with blind persons, the time to acquire the direction of the crossing from the direction location block and the veering distance at the crossing was measured to verify its effectiveness. Moreover, low-vision persons and totally blind persons answered a questionnaire concerning the effectiveness of the block. The effectiveness of this block is described from the measurement data and the questionnaire results.


Quantitative Evaluation of Orientation Performance of Tactile Walking Surface Indicators for the Blind

Tactile walking surface indicators (TWSIs) were invented in Japan in 1965. They are now used around the world to help visually impaired persons. There are two types of TWSIs, attention patterns and guiding patterns. TWSIs are perceived through use of a long white cane or through the soles of the shoes for the blind. The TWSI design of the attention pattern only calls attention to a hazard or to hazards and decision points. This attention pattern is usually constructed at the crosswalk entrance. The direction of the crossing can be acquired by the row of the projection of the attention pattern through the soles of the shoes. TWSIs must have high detection sensitivity and a high ability to distinguish objects as well as excellent direction detection. An international standard for TWSIs was enacted in 2012. However, the international standard organization (ISO) allows a wide-ranging size. The background that allowed the size with a wide-ranging ISO standard was in the adoption of various sizes by each country. For instance, the top diameter of attention patterns was specified as 12 to 25mm. The spacing was specified as 42 to 70mm. In addition, truncated domes or cones of the attention pattern were arranged in a square grid, parallel or diagonal at 45°to the principal direction of travel. This diagonal will not indicate the direction of the crossing. In this research, the performance of indicating the direction was compared at the same intervals for five diameters specified by the international standard. To secure omnidirectional performance, truncated domes were arranged on the disc. The subject enters the disc from a random direction and determines the direction of the row through the soles of their shoes. Five discs with different diameters were selected at random. The spacing of the truncated domes was assumed to be 60mm as adopted by the national standard of Japan (JIS). The experiment was performed by 20 people. For the five kinds of TWSLs, the direction was determined as the direction of the row at the rate from 43 to 54%. Other angles were all less than 7% at a resolution of 5°. The row of 45° forms the row at the spacing of the square root of two times. It was a result similar as for this row. It has been understood to have the orientation performance all five kinds of attention pattern with a different diameter. The attention pattern of JIS was the highest value. Though this experiment does not verify the orientation performance of the difference in spacing, the attention pattern of the diagonal means the performance is not used. The arrangement of JIS is square grid only. In this research, the orientation performance is quantitatively compared by the difference of the diameter. The purpose of this research is to acquire data as a scientific basis for improving the ISO standard.


A Method to Detect and Track Mosquitoes Using Orientation Code Matching and Particle Filter

Proceeding of the 2016 International Conference on Electrical Engineering

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.


A Basic Study for Driver State Estimation Based on Time Series Data Analysis


Vowel sound recognition using electromyogram with dry sensors

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.


Improvement in Detection of Abandoned Object by Pan-tilt Camera

Proceeding of the 2016 8th International COnference on Knowlodge and Smart Technology

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.


Japanese Janken Recognition by Support Vector Machine Based on Electromyogram of Wrist

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.


Method to Evaluate Similarity of Music by Music Features

42st Annual Conference of the IEEE Industrial Electronics Society (IECON 2015)

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.


Hand Motion Discrimination by Support Vector Machine Based on Electromyography of Wrist

Proceeding of the 2015 International Conference on Engineering and Applied Science

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.


On-line Recognition of Finger Motions Using Wrist EMG and Simple-PCA

Proc. of Asian Control Conference 2015 (ASCC'2015),

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.


Method to Detect Impression Evaluation Patterns on Music Listened to Using EEG Analysis Technique

The 10th Asian Control Conference (ASCC 2015)

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.


Feeling Evaluation Detection for Auto-skip Music using EEG Analysis Technique

International workshop on Sensing, Actuation, Motion Control, and Optimization (SAMCON2015)

The Institute of Electrical Engineers of Japan

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.


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)

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.


Detection of Abandoned Object by Pan-Tilt Camera

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.


Fundamental study for driving scene classification using Bag of Keypoints

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.


Analysis of Driver's Eye-gaze Movements at Near-miss Events

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.


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)

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.


Lost Property Detection by Genetic Algorithm with Local Search

Proceeding of The 2nd International Conference on Intelligent Systems and Image Processing 2014

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.


Development of EOG Mouse Using Learning Vector Quantization

Proceeding of the 2nd International Conference on Intelligent Systems and Image Processing 2014

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.


Research on identifying LED blocks mounted on crosswalk boundaries for persons with visually impairment

5th International Conference on Applied Human Factors and Ergonomics 2014


Research on continuity among assistive devices for personal mobility of visually impaired persons around crossings

Proc. of 5th Inernational Symposium on Advanced Control of Industrial Processes (ADCONIP 2014)


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

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.


Personal authentication system by using Kinect

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.


Comparison of Poolong Methods in a Deep Neural Network

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.


Creation of a Panoramic Image by Genetic Algorithm

Proceeding of 2013 International Conference on System, Process, and Control

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.


Implementation of EOG Mouse Using Learning Vector Quantization and EOG-feature Based Methods

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.


Abandoned Object Detection by Genetic Algorithm with Local Search

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.


Proceeding of 2013 International Workshop on Nonlinear Circuits, Communication and Signal Processing NCSP'13

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.


A Method for Detecting Signs of Train Sickness on Tilting Train

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.


Detection of Abandoned Objects Based on Spatiotemporal Features from Public Stationary Camera

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.


Recognition of Eye Motions Using EOG and Statistic Learning

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.


Database Optimization Technique for Ethical Pharmaceutical Searching System

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.


Trigger Pattern Detection Method for Assisting in Ambulation Rehabilitation Based on EEG Analysis

Proc. of 21st IEEE International Symposium on Robot and Human Interactive Communication, Ro-Man2012

This paper proposes a method to detect the trigger pattern of walking motion from an electroencephalogram (EEG). Our final goal is to develop a rehabilitation assistance system that can assist in ambulation exercise and be used on a daily basis by watching for the patient's degree of functional recovery. Due to assist in ambulation rehabilitation, it needs to detect the feature signal related to the voluntary walking. As a first step, we detected an EEG feature pattern that has the potential to become the starting point of walking motion. The proposed method involves EEG recording, noise reduction, and EEG pattern classification. The EEG device had dry-type sensors and several electrodes with a headphone. Morphological filters were used to reduce EEG noise. The EEG patterns related to walking motion were classified using a support vector machine. To demonstrate the effectiveness of the proposed method, we conducted experiments using real EEG data.


Learning Algorithm for Self-Organizing Map Classification of Electroencephalogram Patterns with Individual Differences

Proc. of WCCI 2012 IEEE World Congress Computation Intelligence

This paper introduces a method to classify electroencephalogram (EEG) patterns using a self-organizing map (SOM) with new learning algorithm considering an individual difference in EEG. To classify the EEG patterns with the individual difference, we propose an algorithm to specify the learning area of SOM based on sub-attribute information (SOMSA). The EEG data has two attributes that are the feature indicating the EEG pattern and sub-attribute information meaning the individual difference. The individual difference is quantified by analyzing human character, because this paper defines that one's character affects the individual difference. In the preprocessing, we extract the EEG feature vector by calculating the time average on each frequency band which are theta, alpha and beta, respectively. One's character is analyzed though the ego analysis using psychological testing. The device for recording the EEG uses the band-type device with a few numbers of electrodes. In order to prove the effectiveness of the proposed method, we conduct experiments using real EEG data. The experimental results show that the accuracy rate of the EEG pattern classification using SOMSA improves compared with the standard SOM.


UD of Road Surface for the Low Vision --- How Road Surface Condition Affects Visibility of Tactile Walking Surface Indicators(TWSIs)for the Low Vision

WHO have announced that about 80% of visually impaired persons are the low vision (LVs) in the world. Many LVs can walk safely using the road and the feature things as a key. In Japan, many LVs can walk safety using a Tactile walking surface indicators (TWSIs) because its color is high conspicuous blight yellow and exists most walking place. However, LVs cannot identify the position of TWSIs, because of exists different color of TWSIs or low luminance contrasts between TWSIs and surrounding road. In order to improve these situations, we think ``lateral bands'', which installs neutral lines in both side of TWSIs, as a new method. This method improved the visibility of the TWSIs for LVs, but some LVs miss-recognize such lateral bands as a hole or ditches and they cannot walk safety. The purpose of our research is to find causes miss-recognition when LVs use. In this research, we designed two experiments, cause of we think luminance contrast of lateral bands or relationship of lateral bands and road are very important matter of miss-recognition for LVs. These experiments are to find the view of the band of LVs, and to find relationship between width, luminance contrasts and illumination. Fifty-nine LVs were participated as subjects as these experiments. The most important result was the luminance contrast of a line affects miss-recognition for LVs. I think that these research findings become one indicator of TWSIs with the lateral band of outdoor area or public building which was adapted for the universal design. Moreover, this result makes a big guideline of floor designing not only TWISs but also without. And, this result also makes good effort not only visual impaired persons but also an elderly person with eyesight gets worse.


Walking Motion Feature Detection System Based on EEG Analysis

Proceedings of 2012 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP2012)

The Research Institute of Singnal Processing Japan(RISP)


Effect of Road Surface Condition for the Low Vision


Variability in EEG with Single Point Sensing as Inter-Individual Difference Measure Using Self-Organizing Map

Proc. of 2011 International Symposium on Nolinear Theory and its Applications, NOLTA2011

In this paper, we introduce an EEG analysis technique to confirm an inter-individual difference in prefrontal cortex EEG with a single point sensing. The device for recording the EEG uses the dry-type sensor and a few numbers of electrodes. The EEG analysis adapts the feature mining on EEG pattern using a self-organizing map (SOM). The EEG patterns are determined based on the preference evaluation on sound listened to. In the preprocessing, we extract the EEG feature vector by calculating the time average on each frequency band which are theta, low-alpha, high-alpha, low-beta, high-beta, respectively. To confirm the inter-individual difference, we do experiments using real EEG data. These results show that the learning results by SOM on each human are clearly different when using same initial weight values for the SOM.


Visualization on Pareto Solutions in Multi-objective Optimization

Proc. of 4th IASTED International Conference on Artificial Intelligent and Soft Computing, ASC2011

This paper proposes a method for assisting in the specification of a single optimal solution. The proposed method visualizes the relationship between the gene and fitness values of all individuals in a Pareto solution. The gene and fitness values are expressed as colored and gray scales, respectively. The information visualization technique in this paper has the advantage of indicating the Pareto solution and its details in one frame. To show the effectiveness of the proposed method, we apply the proposed method to benchmark problems. The proposed method visualizes the relationship between the gene and fitness values of all individuals in a Pareto solution. The gene and fitness values are expressed as colored and gray scales, respectively. It is possible to comfirm the Pareto solution and its details easily.


Experimental Validation of an Online Adaptive and Learning Obstacle Avoiding Support System for the Electric Wheelchairs

Proceedings of 2010 IEEE International Conference on Systems, Man and Cybernetics (SMC 2010)

身体に障害を持つ高齢者が，空間が制限された室内において電動車いすの操縦することは，相当のスキルが必要である．この論文では，強化学習を用いて電動車いすの障害物回避の支援システムを述べる．障害物回避は，Minimum Vector Field Histogram(MVFH)法によって半自動で支援する．このMVHFをユーザー操作と障害物回避支援に適用する．この提案手法では，修正割合が環境とユーザーの状態に従って，強化学習によって調整される．この新しい手法をシュミレーション例で数値的に評価した．さらに，提案手法を実際の電動車いすに応用し，実際の操縦環境で提案手法の有効性を検証した．


A Method of Obtaining Sense of Touch by Using EEG

Proc. of 19th IEEE International Symposium on Robot and Human Interactive Communication (Ro-Man'10)


Association between Ego Scores and Individual Characteristics in EEG Analysis --- Basic Study on Individual Brain Activity

This paper introduces a method for discussing an association between ego scores and individual characteristics in EEG analysis. The ego scores express one of the personality and/or nature on a human. The EEG analysis calculates the power spectra of the frequency weighted of the EEG signal, and evaluates through detecting the EEG patterns while listening to the music. A k-nearest neighbor classifier is used to detect the EEG patterns. The specified weight value to weight frequency of EEG and the results of EEG pattern detection express the individual characteristics in EEG analysis. Finally, we do experiment using a real EEG data for discussing the association the ego scores and the individual characteristics in EEG analysis. An interesting tendency was that the subjects who were the combined ego-type tended to have a powerful response to negative stimuli more than positive stimuli. They had the stable filter weighted frequency.


Identification of Tactile Walking Surface Indicators by White Cane

Proceedings of 2010 IEEE International Conference on Virtual Environmnets Human-Computer Interfaces and Measurement Systems (VECIMS 2010)


Study on Association between User's Personality and Individual Characteristic of Left Prefrontal Pole EEG Activity

Proc. of 2010 Sixth International Conference on Natural Computation (ICNC'10)

This paper introduces a method for discussing the association between human personality based on egogram scores and the results of classifying the electroencephalogram (EEG) patterns while listening to the music. The egogram based on psychological testing is used for analyzing his/her personality. The frequencies of the EEG analyzed are the components that contain significant and immaterial information and have different importance. We express the different importance through the weight value on frequencies using real-coded genetic algorithm. Then, the EEG patterns, which are determined based on the evaluation of the impression on the music, are classified using the k-nearest neighbor method. Finally, we discuss the association between his/her personality and the individual characteristic in the EEG analysis. An interesting tendency was that the false-detection accuracy of the EEG pattern, meaning the response on negative stimuli, did not become low when the score and level on ego states of Adult was extreme.


Study on Relationship between Personality and Individual Characteristic of EEG for Personalized BCI

It is an important issue that the relationship between personality and individual characteristic of an electroencephalogram (EEG) for using a personalized brain computer interface on daily basis. In this paper, we introduce a method for discussing the relationship between personality based on egogram scores and the results of the EEG pattern detection. The egogram based on psychological testing is used for analyzing human personality. The frequencies of the EEG analyzed are the components that contain significant and immaterial information and have different importance. We express the different importance through the weight value on frequencies using real-coded genetic algorithm. Then, the EEG patterns, which are determined based on the evaluation of the impression on the music, are classified/detected using the k-nearest neighbor method. Finally, we discuss the relationship between human personality and the individual characteristic in the EEG analysis. An interesting tendency was that the false-detection ratio of the EEG pattern, meaning the response on negative stimuli, did not become low when the score and level on ego states of Adult was extreme.