Shusaku Nomura

Evaluation of Student's Physiological Response Towards E-Learning Courses Material by Using GSR Sensor

This study aims to evaluate student physiological response towards the e-learning materials. The experiments were conducted by introducing two contracting e-learning materials, i.e., the one is characterized as interactive material and the other is non-interactive one. During the experiment physiological sensor, i.e., galvanic skin response (GSR) sensor was attached to the participant. Furthermore, GSR data were extracted by feature generator, LDA. The purpose of feature extraction is to find preferably small number of features that are particularly distinguishing or informative for the classification process and that are invariant to irrelevant transformations of the data. Finally, several classifiers were performed discriminating student attitude towards e-learning course materials response using GSR sensor data. The results showed that discriminant analysis (DA) and support vector machine (SVM) give high accuracy rate, while the k-nearest neighbor (KNN) give moderate accuracy rate.

Determination of Age and Gender Based on Features of Human Motion Using AdaBoost Algorithms

Automated human identification by their walking behavior is a challenge attracting much interest among machine vision researchers. However, practical systems for such identification remain to be developed. In this study, a machine learning approach to understand human behavior based on motion imagery was proposed as the basis for developing pedestrian safety information systems. At the front end, image and video processing was performed to separate foreground from background images. Shape-width was then analyzed using 2D discrete wavelet transformation and 2D fast Fourier transformation to extract human motion features. Finally, an adaptive boosting (AdaBoost) algorithm was performed to classify human gender and age into its class based on spatiotemporal information. The results demonstrated the capability of the proposed systems to classify gender and age highly accurately.

Rough set analysis on the relation between human psychological mood state and the immune function

Recent behavioral medicine studies have revealed that the human secretory substances change according to his/her mental states. Especially, those substances transiently get increase (or decrease) against the short-term experimental stressors. However, the relation with rather longer stressor, or daily stressor, has not yet well understood or reported discrepant results. One possible reason for this discrepancy might be brought from uncertainty of the psychological evaluation, which is the score of subjective questionnaire. We then introduced the rough set theory to analyze the relation between the psychological state and a major immune substance. As a result, several items in the questionnaire were extracted as dominant items, while there was still no correlation between them.

Identification of human pulse waveform by silicon microphone chip

An Electret Condenser Microphone (ECM) fabricated by Micro Electro Mechanical System (MEMS) technology, or a silicon microphone, was employed as a novel apparatus for human pulse wave measurement. Since ECM frequency response characteristic, i.e. sensitivity, theoretically constant in the level at lower than resonance frequency as stiffness control, the slightest pressure difference at around 1.0Hz generated by human pulse waveform is expected to measure by this silicon microphone. However, with regard to MEMS-ECM as a single device, the difficulty arises in a possible sensitivity reduction due to a high input-impedance amplifier embedded in MEMS-ECM. We then determined the frequency response at around 1.0Hz, and based on this result we defined the compensation circuit by which the human pulse waveform was successfully obtained.

Gender and Age Classification Based on Pattern of Human Motion Using Choquet Integral Agent Networks

Automated human identification from their walking behavior is a challenge attracting much interest among machine vision researchers. However, the systems which are able to detect pedestrian attributes based on their walking behavior remain to be developed. Here, a soft computing approach to determine walking behavior based on motion imagery is studied as the basis for developing pedestrian safety information systems. Gender and age are classified based on motion pattern derived in experiments. At the front end, image and video processing was performed to separate foreground from background images. The widths of silhouette were analyzed using two-dimensional (2D) Fourier transformation to extract human motion features. Feature sub-sets were then selected to find salient, effective classification features. Finally, Choquet integral agent networks (CHIAN) with a competitive learning algorithm were employed to classify gender and age into its classes. The experimental results demonstrated capability of the proposed system to classify gender and age in highly accurately.

Driver's drowsiness inhibition by subcutaneous stimulation based on SNS activity

The overall aim of the study is to develop the ambient drowsiness control programs based on driver’s physiological states. this study is developed and verified of a system that controls a driver’s drowsiness a stimulus that is too small to be noticed by a driver. Most studies on driver drowsiness have focused on the detection or evaluation of psychological states in some way. Our system assumes that a small change in temperature affects peripheral thermoreceptors and that afferent fibers transmit this stimulus to the cerebral center via the spinal nerves. To evaluate the system, we constructed a virtual reality system for automobile driving using an experimental method described in our previous studies. In this study, drowsiness was controlled by our system, and the effectiveness of the system was tested. The results suggest that this is an efficient method for controlling driver drowsiness.

Data mining the relationship between psychological and physiological stress measurement using rough set analysis

Recent behavioral medicine studies have revealed that some hormones secreted in human body sensitively changes according to his/her mental stress. Thus it is expected as an objective measurement of mental stress. However this newly developed interdisciplinary studies frequently showed inconsistent results. Some technical reasons were indicated for this discrepancy. Above all, we focused on the fact that in all these studies the only method introduced to estimate the relationship between the level of hormones and the subjective stress scale was the correlation analysis. In this study we employed Rough set analysis in place of conventional linear correlation analysis for mining the relationship between a subjective stress scale, “Profile of Mood Scale” (POMS), and a well known stress biomarker, salivary cortisol. As a developing result, numbers of items (inquiries in POMS) which relatively associated with cortisol level were found, whereas no significant linear correlation was obtained between them.

An Application of Rough Set Analysis toa Psycho-Physiological Study - Assessing the Relation Between Psychological Scale and Immunological Biomarker.

Proceedings of Joint 4th International Conference on Soft Computing and Intelligent Systems and 9th International Symposium on advanced Intelligent Systems in Nagoya on September 17-21, 2008 (SCIS & ISIS 2008)

Enhancing cardiac parasympathetic nervous activity using a posture respiration ambient biofeedback system: a pilot study

A respiration–posture feedback system was developed to control breathing involuntarily. A small air chamber placed under a subject’s back deflates and inflates to make a subject’s upper body move vertically while lying on a bed. By regulating the deflation/inflation of the air chamber in synchronization with actual respiration, the subject’s respiration was successfully lengthened and deepened. The modulation of the respiration acted as a physiological sedative for the subject as the heart rate variability index suggested that the subject’s parasympathetic nervous system activity was enhanced.

Development of a bionanodevice for detecting stress levels

Recent advances in molecular analysis techniques have enabled scientists to assess the tiny amounts of biochemical substances secreted in our bodies. This has revealed that the levels of various secretory hormones and immune substances vary sensitively with the mental state of a person. Such hormones and immune substances exhibit transient increases with various psychological stressors. They thus have the potential to be used as a novel biometric for monitoring stress. Biomarkers that occur in saliva can be monitored non-invasively and are thus potentially useful as practical indicators of mental stress. Stress biomarkers are considered to be released into the blood stream or other secretory fluids by physiological stress reactions. Stress biomarkers are expected to be detectable in sweat and other humoral fluids that are exuded from the skin surface. Based on this, we have developed a bionanodevice for detecting stress by capturing stress biomarkers on the skin surface in a non-invasive manner. A prototype bionanodevice is described in which a motor protein is introduced for molecular handling.