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


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%.


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.


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.


Development of Decision Support System for driving Condition Based on Driving Fatigue Problem

Proc. of Innovative Research and Industrial Dialogue'18

Driving fatigue has been identified as one of the top contributor factor to the road accidents among the Malaysian road user. The aims of this study was to develop a decision support system for improving the driving fatigue problem. The decision support system provide systematic analysis and solutions to minimize the risk and the number of accidents associated with driving fatigue. Six main components involved as the pillar in the development of decision support system. The decision support system is an essential system to analyze the risk factors that would contribute significantly to driving fatigue associated with driving activity.


Development of working-posture monitoring system for ergonomic manufacturing work environment

Advances in Transdisciplinary Engineering

Ergonomic consideration on workers in manufacturing work has been attracted attention by industries. Physical safety and mental stability, which would be offered to workers by a well-designed ergonomic work environment, could not only provide job satisfaction to the workers, but also could enhance productivity in manufacturing work. This study develops a prototype monitoring system of ergonomic working posture in a manufacturing work environment. The methodology of this monitoring system is based on the experimental observation using an experimental tool of X-box cameras controlled by XAMPP software to monitor the posture of workers. As for the ergonomic environment measurement, the system installs the five types of instrumental sensors, which include sound, light, temperature, vibration and indoor air quality. Capturing the body posture of subjects, the system measures the frequency of body bending, the angle of body bending and the time period of the same position kept by the body. Potentially useful several parameters are used in the experiments of this study. These parameters include temperature, light, vibration, body posture, indoor air quality and noise. The parameters are captured in analogue signals, which can be converted to digital signals by a signal converter. The analysis of worker posture on RULA (Right Upper Limb Assessment) was also conducted by using several software tools. Reviewing the experimental results using the monitoring system in a manufacturing industry at welding assembly section, this paper shows the feasibility of the proposed system.


Redesign the Material Handling System by Using Ergonomic Approaches to Reduce Back Pain Risk

International Conference on Kansei Engineering & Emotion Research 2018

This paper is about the redesign material handling device through the study of psychological and biomechanical factors that related the activities push and pull trolley. The project has been conducted in the automotive industry. The data about the problems encountered through a questionnaire among 14 workers at Receiv-ing Area, since it has the highest number of ergonomic risk factors. The result shows the main problem is the existing trolley design seems not suitable and giv-ing musculoskeletal disorder(MSD) of the workers especially back pain. There-fore, the aim of this paper is redesigns the material handling system by using er-gonomic approaches to reduce back pain. The Tekscan software will be used for evaluating hand pressure distribution force on the existing and alternatives trolley. The result obtained is used as a benchmark for the concept of trolley to be rede-signed. The Rapid Upper Limb Assessment (RULA) was applied to analyses the posture of workers on the existing and alternative trolley and the redesign trolley to be proposed by ABC company. Based on the RULA analysis, the redesigned trolley has improved the posture of the workers. Hence, this study concludes that considering ergonomic features for the redesigned trolley contributed to safe body posture.


Development of Decision Support System for Improving Driving Fatigue Problem among Road Users

4th International Forum on Advanced Technologies (IFAT2018)

The aim of this study is to develop an ergonomic vehicle model (EVM) and decision support system (DSSfDF) model for improving the driving fatigue problem among the road users. The ergonomic vehicle model use to capture the user information data, and acts as the database storage to store all the input data and information. While the decision support system provide a systematic analysis and solution to minimize the risk and the number of accidents associated with driving fatigue. There are 6 main components as the pillars for the development of EVM and DSSfDF model; ergonomics evaluation tools, graphical user interface (GUI), ergonomics database, working memory, inference engine, and knowledge base. Both models are essential system and reliable advisory tool for providing analy-sis on risk factors that contribute significantly to driving fatigue, and providing solutions and recommendation to the problem related to driving fatigue. Further analysis and validation is required in future to get the reliable system before being commercialize.


Mathematical Modelling of Biomechanics Factors for Push Activities in Manufacturing Industry

Symposium on Intelligent Manufacturing and Mechatornics 2018

In manufacturing industries, many working tasks require their workers to perform the works in push-pull activities. The workers need to push or pull the tool or material handling in a long distance in to a workplace and performing these activities continuously throughout the working hours, may lead to an early initiation of musculoskeletal disorders (MSDs) symptoms as workers developed muscle fatigue particularly concerning the hand muscles. Grip strength is the force applied by the hand to pull objects and is a part of hand strength. This paper is about the mathematical model of biomechanical factors that contributes to fa-tigue while worker involved on the push activities in manufacturing industry. The experimental was conducted by using Tekscan system to evaluate the muscle fa-tigue and hand grip pressure force while workers performing pushing excessive loads. The input parameters were time exposure, hand side and body mass index (BMI); while the output responses are muscle fatigue (voltage), hand grip pres-sure force (left hand), and hand grip pressure force (right hand). An important parameter that affects the output response is also identified. The finding result from mathematical model for both factors, show that the muscle fatigue was in-fluenced by time exposure, hand side, BMI, and interaction between hand side and BMI; while hand grip pressure force was influenced by time exposure, hand side, BMI, interaction between time exposure and hand side, interaction between time exposure and BMI, and interaction between hand side and BMI.


Development of Ergonomic Vehicle Model and Decision Support for Driving Fatigue

Driving fatigue has been recognized as one of the significant contrib-utor factor to the road accidents and fatalities in Malaysia. The aim of this study was to develop an ergonomic vehicle model (EVM) and decision support sys-tem (DSSfDF) model for improving the driving fatigue problem among the road users. The ergonomic vehicle model use to capture the user information data, and acts as the database storage to store all the input data and information. While the decision support system provide a systematic analysis and solution to minimize the risk and the number of accidents associated with driving fatigue. There are 6 main components as the pillars for the development of EVM and DSSfDF model; ergonomics evaluation tools, graphical user interface (GUI), ergonomics database, working memory, inference engine, and knowledge base. Both models are essential system and reliable advisory tool for providing analy-sis on risk factors that contribute significantly to driving fatigue, and providing solutions and recommendation to the problem related to driving fatigue. Further analysis and validation is required in future to get the reliable system before be-ing commercialize.


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%.


Development of Driving Fatigue Strain Index to Analyze Risk Levels of Driving Activity

Proceedings of 2017 International Conference on Electrical, Electronics and System Engineering (ICEESE 2017)

Driving activity has become more important as this medium being practically, faster and cheaper in connecting human from one to another place. However, driving activity can cause disaster or death to human in daily life as they get fatigued while driving. Driver fatigue is a top contributor to the road crashes. The primary objective of this study was to develop a driving fatigue strain index (DFSI), to quantify the risk levels caused by driving activity, and proposed solution in minimizing the number of road accidents caused by driving fatigue. The development of DFSI is based on risk factors associated with driving activity such as muscle activity, heart rate, hand grip force, seat pressure distribution, whole-body vibration, and driving duration. All risk factors were assigned with multipliers, and the DFSI was the output or result of those multipliers. The development of DFSI is essential to analyze the risk factors that would contribute significantly to discomfort and fatigue associated with driving. Besides, in the future this index has a capability to recommend alternative solutions to minimize fatigue while driving.


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.


A Study of Biomechanical Factor for Driver Fatigue using Regression Model

Proceedings of International Conference on Design and Concurrent Engineering Conference 2017 & Manufacturing Systems Conference 2017


Ergonomics Study Of Working Postures In Demould Process At Aerospace Manufacturing


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.


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.


Estimation of physical burden in daily living activity by wearable sensors

Proceeding of the 2015 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP)

In this paper, we detect daily living activity by using accelerometer, and analyze the relation between the activity and heart rate. The nal purpose is to build a cloud computing system for alerting the physical burden by daily living activity. First, we measure acceleration of daily living activity by using accelerometer and gravity sensor mounted on Android. Next,we apply many lterstovalues measured by using such devices. After that, we detect the activity by using template matching and threshold process. Finally, we calculate a heart rate difference between resting state and moving state, and alert to physical burden.


A Method to Detect Uncomfortable Feeling of Listeners by Biological Information

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.


Human Action Recognition using Normalized Cone Histogram Features

Proceedings of the IEEE Symposium Series on Computational Intelligence (CIMSIVP 2014)

In this paper, we propose a normalized cone histogram features method to recognize human actions in video clips. The cone features are extracted based not on the center of gravity as is common, but on the head position of the extracted human region. Initially, the head, hands and legs positions are determined. Thereafter, the distances and orientations between the head and the hands and legs are the extracted and employed as the features. The histogram's x-axis represents the orientations and the y-axis the distances. To make the method invariant to human region sizes, the features are normalized using the L2 normalization technique. The classification method used was the perceptron neural network. We conducted experiments using the ucf-sports-actions database to verify the effective ness of our approach. We achieved an accuracy of about 75% on a selected test set.


Development of a Healthcare Monitoring System Based on Pulse Wave Analysis

Proceedings of 2014 IEEE 3rd Global Conference on Consumer Electronics (GCCE 2014)


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.


Driver Body Information Analysis with Near-miss Events

Proceedings of AMBIENT 2014: The Fourth International Conference on Ambient Computing, Applications, Services and Technologies

This study specifically examines safety verification behaviors and near-miss events at non-regulated intersection with poor visibility. Assessing the drivers physical information (i.e., eye-gaze movements and face orientation) and the sudden appearance of bicycles that the driver would encounter while approaching a non-regulated intersection, we attempt to analyze the causal relation of workload sensitivity and driving style and the distinctive motion of the safety verification behaviors before and after near-miss events.


Vehicle Extraction from Aerial Images Captured using an UAV

Proceedings of the 2014 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing(NCSP'14)


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.


Hand Written Character Recognition using Star-Layered Histogram Features

Proceedings of SICE Annual Conference 2013

In this paper, we present a character recognition method using features extracted from a star layered histogram and trained using neural networks. After several image preprocessing steps, the character region is extracted. Its contour is then used to determine the center of gravity (COG). This CoG point is used as the origin to create a histogram using equally spaced lines extending from the CoG to the contour. The first point the line touches the character represents the first layer of the histogram. If the line extension has not reached the region boundary, the next hit represents the second layer of the histogram. This process is repeated until the line touches the boundary of the character's region. After normalization, these features are used to train a neural network. This method achieves an accuracy of about 93% using the MNIST database of handwritten digits.


Classification of Driver's Head Posture by using Unsupervised Neural Networks

Proceedings of The Third International Conference on Ambient Computing, Applications, Services and Technologies

We analyze drivers head posture during safety verification at an unsignalized intersection with poor visibility and propose a method for classifying head posture using two types of unsupervised neural networks: Self-Organizing Maps (SOMs) and fuzzy Adaptive Resonance Theory (ART). The proposed method can generate the optimal number of cluster-generated labels for the target problem. We experimentally assess the effectiveness of the proposed method by adjusting the fuzzy ART network vigilance parameters.


Development of an Android Application for Imaging of Superficial Veins


Analysis of Safety Verification Behavior and Classification of Drivers Head Posture

Proceedings of 2013 IEEE International Conference on Mechatronics and Automation

In this paper, we analyze drivers head posture of safety verification at the unsignalized blind intersection, and propose a classification method of head posture using two kinds of unsupervised neural networks: SOMs and Fuzzy ART to quantize drivers head motion for construct a driving assist system which is able to detect the continual deviation signals. The proposed method is able to categorize head posture roughly.


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.


A Basic Study for Quantification of Driving Behaviors and Estimation of Driving Psychology

In this paper, we propose a quantification method of driving behavior using two kinds of unsupervised neural networks: Self-Organizing Maps and Fuzzy Adaptive Resonance Theory, and discuss the relation between safety verification motion and driving psychology. Finally, we show that driving behavior expresses the driving psychology.


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.


Epibiosis Vein Imaging through Near-Infrared Ray during Temperature Stimuli


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.


Nonlinear Eigenspace Models Based on Fast Statistical Learning Algorithm

Proceedings of IASTED International conference on Software Engineering and Applications

In the field of pattern recognition, feature extraction plays an important role prior to classification in order to filter out the background noise, reduce the dimensionality for input and so on. Fisher Linear Discriminant Analysis (FLDA) is well-known as one of the most famous feature extraction methods. In recent years, FLDA has been improved in various ways because an eigenspace is learned faster and/or the classification performance is improved. Simple-FLDA (SFLDA) has been proposed to speed up the learning by improving FLDA algorithm. However, the above methods are calculated in input space. Thus, it might not be efficient in cases where data distribution is complex. Then, Simple Kernel Discriminant Analysis (SKDA), which is an improved version of Kernel Discriminant Analysis (KDA), has been proposed to acquire a better performance for classification by applying kernel trick. Whereas a better performance is acquired by SKDA than that by SFLDA, its learning speed has increased instead. In this paper, an additional improvement is applied to SKDA algorithm and the improved version of SKDA (SIKDA) is introduced. The performance of SIKDA is as same as that of SKDA. In addition, learning speed has become faster than that by SKDA. These are shown in the experiment, especially, the influence of proposed method has seen in a specified dataset.


Optimization of Categorizing Driver's Head Motionfor Driving Assistance Systems

Proceedings of SICE Annual Conference 2012 Final Program and Papers

Our system needs quantization of drivers 3D head motions in safety verification only phase variation on 2D image taken by monocular in-vehicle camera, and modeling of head motion information. In this paper, we optimize categorization of drivers head motion using two kinds of unsupervised neural networks: Self-Organizing Maps (SOMs) and Fuzzy Adaptive Resonance Theory (ART).


Decision of Two Alternatives by EEG using Genetic Algorithm

Proceedings of International Technical Conference on Circuits/Systems, Computers and Communications (CD-R), D-T3-05

The electroencephalograms (EEGs) are a biological signal that represents the electrical activity of the brain. There is a tendency to quantify the human mind and its changes based on preference, feeling, and impression by analyzing electroencephalograms (EEGs) taken on the human scalp. Recently, it is expected that EEG will promote special man-machine interface. In this study we try to estimate human mind and emotion from EEG which becomes popular by development of measurement techniques and analysis of brain activity. In this paper, detection of frequencies, which expressly shows two tendencies to be distinguished while a human subject is in low and high concentration states, is carried out. The purpose of this study is to construct a tool for alternative decision with the frequency difference in two states. Two concentration states can be classified using specified frequencies measured when a human subject is in low and high concentration states. Severe disorders such as Amyotrophic Lateral Sclerosis (ALS) make it hard for motion-impaired people to communicate with others by talking and writing. Therefore, support systems that can help them to establish communication can be constructed with less mental load by utilizing the analyzed data of this study.


Classification of Head Motions for Estimation of Driver's Internal States

Proceedings of International Technical Conference on Circuits/Systems, Computers and Communications (CD-R), D-T3-01


Kernel Discriminant Analysis Based on Iterative Calculations

Proceedings of International Technical Conference on Circuits/Systems, Computers and Communications (CD-R), E-T1-04

Fisher Linear Discriminant Analysis (FLDA) is well-known as one of the most famous feature extraction methods in the research field of pattern recognition. In recent years, FLDA has been developed with various techniques to improve its effectiveness for classification, learning speed and generality for any problems. Simple-FLDA (SFLDA), which is a faster version of FLDA, has been proposed to extract features from input datasets in an efficient way. However, it is not always efficient in cases where the datasets are complex, because eigenvectors spanning an eigenspace are acquired in just the input space. In this paper, we proposed a new feature extraction algorithm derived by expanding SFLDA to a non-linear space for effective classification. The algorithm is constructed with just simple calculations, but it is capable of obtaining effective features. In some experiments, better classification accuracies have been acquired with a few specified datasets.


Proceedings of International Technical Conference on Circuits/Systems, Computers and Communications (CD-R), E-T1-02

It is difficult to search for lost property in extensive grounds. Therefore, we often have to give up finding it out. To solve this problem, search methods using a security camera have been proposed. It is necessary to not increase the administrative burden when you search an object by using security cameras. In this study, we develop a system for searching an object from camera images automatically. When searching the object, we need to input appropriate information of the object to the system. In this paper, a ``hand-drawn rough sketch'' image is used for this purpose. The present system can identify the object by analyzing the object shape in the image.


A new optimization method of the geometric distance in an automatic recognition system for bird vocalisations

Proceedings of the Acoustics 2012 Nantes Conference

We have been developing an automatic recognition system for bird vocalisations. Many biologists have been using the early 32 bit version of our system, and we have been working on a 64 bit version. The software segments a waveform of the bird vocalisation from a three-hour continuous recording and extracts the sound spectrum pattern from the waveform using the LPC spectrum analysis. Next, the software compares the sound spectrum pattern (the input pattern) with the standard pattern (that was extracted in advance) using a similarity scale. We use a new similarity scale called the ``Geometric Distance''. The Geometric Distance is more accurate than the conventional similarities in the noisy environment. In the 64 bit version, the software matches an input pattern with the 40,000 elements of the standard patterns per second and per processor, and it is 2.8 times faster than the conventional cosine similarity. In this paper, we introduce an automatic segmentation method of bird vocalisations and a new optimization method of the Geometric Distance. The new optimization method offers improvements of an order of magnitude over the conventional Geometric Distance.


Detection of Abandoned Objects in Public Facilities

Proceeding of 2012 International Workshop on Nonlinear Circuits, Communication and Signal Processing NCSP'12

This paper presents a new method to detect abandoned objects. Our approach consists of the following 5 steps; Dynamic background modeling by using the Gaussian mixture model based on features obtained by space-time patches, human region detection based on Support Vector Machine, movement region detection based on matching of regions histogram features, construction of region database from region features, and abandoned objects detection from the region database. The proposed method adds color information on the features obtained using dynamic background modeling to enhance the effectiveness of detection for an object that is assimilated into background. Experimental results prove the efficiency of our algorithm on PETS2006 and AVSS2007 benchmark data.


Fundamental Study on EEG Analysis for Safety Driving Support System

In this paper, as a fundamental study of relationship between drivers internal state and the safety verification motion, we analyze four drivers EEG (electroencephalogram) at unsignalized intersections, and discuss effects of drivers internal state changes on safety verification.


Recognition of FingerMotion by Wrist EMG

In this paper finger motions were recognized by EMG measured using dry-type electrodes attached to wrist. Target behaviors to be recognized are four motions that the Janken ``rock'', ``scissors'', ``paper'' and when not doing anything ``neutral''. We tried to reduce an execution time by the simple-PCA in training and recognition, with a view to implementation of interface which can be utilized in daily life.


Visual Illusion of Depth Percception during Car Driving


Classification of Motions by EMG of Ankle


Speech Recognition of Whisper Voice Based on EMG Signals

Speech recognition using surface electromyographic (EMG) is regarded as one of substitute or support for situations that cannot obtain a clear sound. This research aims at Japanese speech recognition to a whisper voice using EMG signals. In this paper, as the first step, we recognized Japanese 5 vowels of the prolonged sound. In order to recognize them, FFT was carried out for the EMG signals obtained from 2 muscular parts. After that, the features were extracted by Simple-PCA, and 1-NN was used to recognize them. The average recognition accuracy of 5 vowels was about 79%.


Feature Extraction For Human Recognition Based On Bifurcation Points Using Genetic Algorithms

本論文では，遺伝的アルゴリズムを用いた分岐点に基づく人物認識のための特徴抽出手法を提案した．画像処理において，人物を認識する手法は様々開発されてきたが，人物はそれぞれ体形が異なり，さらに姿勢も変化するため，精度をあげることが難しい．そこで本手法では，交叉や突然変異などを繰り返しながら最適解を求める遺伝的アルゴリズムを用いることで人物認識のための特徴量を抽出する．


Acoustic Model Selection Method for Speaker Dependent Speech Recognition

In this paper, we investigate an intra-speaker's variability using a specific speaker's speech data witch are collected over long time period. Especially, we investigate a relationship between a speech recognition performance and a distance calculated by dynamic programming matching algorithm (we called ''DP distance'' hereafter), and a time dif- ference of a speech recognition day and an acoustic model training day. From the investigation results, we can see that the intra-speaker's speech variability varies the recognition performance. Hence, for restricting the variation of speech recognition performances caused by intra-speaker's speech variability, we proposed an acoustic model selection method in this paper. The proposed method decides candidates of optimal acoustic model by using DP distance, and selects an optimal acoustic model by using likelihood. For evaluating the proposed method, we conduct speech recognition experiment using a male speaker's speech data collected over long time period. As a result, speech recognition accuracy of selected model by the proposed method is slightly degraded than that of the conventional method. However, the proposed method can select an optimal acoustic model with smaller calculation costs than the conventional method.


A Cloth Simulation System to Select the Right SIze


A Novel Nonlinear Discriminant Analysis by Iterative Operations

In the past, Principal Component Analysis (PCA), Fisher Linear Discriminant Analysis (FLDA) and other feature analyses have been widely used as statistical techniques in pattern recognition. Furthermore, these techniques have been improved to extract better features for both classification and learning. For instance, Simple-PCA (SPCA) and Simple-FLDA (SFLDA) have been proposed to learn an eigenspace not only easier but also faster by iterative operations. However, the eigenspace by these feature extraction algorithms might not be adequate to every dataset, because these are linear feature extraction methods. In this paper, we propose a new Simple-FLDA algorithm applied Kernel method, which leads nonlinear feature extraction. Therefore, the proposed algorithm is simply constructed. In addition, kernel base vectors, which are projected from high-dimensional space to inner product space, are used for feature extraction. The same criterion for discriminant analysis is used by using these kernel base vectors. In the experiment, 10% better classification accuracy is acquired when one dataset has high dimensionality.


Abandoned Luggage Detection using a Dynamic Background Model and Earth Movers Distance

Proceedings of 18th Korea-Japan Joint Workshop on Frontiers of Computer Vision


Estimation of venous shapes acquired from CMOS camera images.

Proceedings of the Eighteenth Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV2012)


Brain Tissues Segmentation for Diagnosis of Alzheimer-Type Dementia

Proceedings on IEEE Nuclear Science Symposium, Medical Imaging Conference

In this paper, we specifically discuss segmentation of brain tissues which are used for calculation of atrophy rate. We proposed a brain tissue segmentation method using two kinds of unsupervised neural networks: Self-Organizing Maps (SOMs) and Fuzzy Adaptive Resonance Theory (ART). The performance of proposed method was tested in two brain MR images used in daily diagnosis. Proposed method could segment CSF accurately with continuity of brain tissues.


Basic Studies for Estimation of Driver's Internal States Using Head Positions

Proceedings on 4th International Symposium on Applied Sciences In Biomedical and Communication Technologies (ISABEL)

In this paper, we analyze driving movies taken by monocular in-vehicle camera, and examine drivers head position category in safety verification at intersections for quantification of head motion information. Moreover, we propose a quantifiable categorizing algorithm of head motion using two kinds of unsupervised neural networks. Through an experiment on actual driving data, the results provide a possibility of quantification of individual head position in safety verifications.


Development of automatic filtering system for individually unpleasant data detected by pupil-size change.

Proceedings of 2011 IEEE International Conference on Systems, Man, and Cybernetics


An analytical method for face detection based on image patterns of EEG signals in the time-frequency domain.

Workshop on Brain-Machine Interfaces, Proceedings of 2011 IEEE International Conference on Systems, Man, and Cybernetics


Analysis of Relationship between Head Motion Information and Driving Scene for Dangerous Driving Forecast

In this study, we are aiming to detect deviation signal which has a possibility to cause car accidents, using drivers head motion information. The head motion information is extracted from only 2D camera images. Our method is able to extract the information without alignment of facial positions. In this paper, SOMs categorization results of our method are discussed and evaluated.


Supervised Iterative Learning Algorithm for Eigenspace Models

In pattern recognition, the principal component analysis (PCA) is one of the most famous feature extraction methods for dimensionality reduction of high-dimensional datasets. Furthermore, Simple-PCA (SPCA) which is a faster version of the PCA, has been carried out effectively by iterative operated learning. However, in SPCA, when input data are distributed in a complex way, SPCA might not be efficient because it is learned without class information of the dataset. Thus, SPCA cannot be said that it is optimal for classification. In this paper, we propose a new learning algorithm, which is learned with the class information of the dataset. Eigenvectors spanning eigenspace of the dataset are obtained by calculation of data variations belonging to each class. We will show the derivation of the proposed algorithm and demonstrate some experiments to compare the SPCA with the proposed algorithm by using UCI datasets.


BCI Based on Single EEG Used Simple Electroencephalograph