Development of a new measurement system to detect selectively volatile organic compounds derived from the human body.

A new concept expired gas measurement system used double cold-trap method was developed. The system could detect selectively volatile organic compound (VOC) derived from the human body. The gas chromatography (GC) profiles of healthy volunteer's expired gas collected by our system were analyzed. As a result, 60 VOCs were detected from the healthy volunteer's expired gas. We examined 14 VOCs among them further, which could be converted to the concentration from the GC profiles. The concentration of almost VOCs decreased when the subjects inspired purified air compared with the atmosphere. On the other hand, isoprene was almost the same. It was strongly suggested that these VOCs were derived from the human body because the concentration of these VOCs in the atmosphere were nearly zero. Expired gas of two sleep apnea syndrome (SAS) patients were analyzed as preliminary study. As a result of the study, the concentration of some VOCs contained in the expired gas of the SAS patients showed higher value than a healthy controls.

Development of the assist system to operate a computer for the disabled using multichannel surface EMG.

The purpose of this study is development of the assist system to easily operate a computer for the disabled such as amputees. In order to operate a computer, we usually use a mouse, and our system assists a mouse operation. Our system requires electromyography (EMG) signals of the arm instead of the mouse operation by the hand, and those EMG signals are used to become a control source for the mouse operation. To acquire the EMG signal, using a multichannel electrode that has 96 channels is a key of this assist system. As for the old assist system using the EMG, most of them require the definition of the target muscle. In many cases, these systems used 2 or more electrodes. We have developed an assist system using 96 channels matrix-type surface multi channel electrode. The EMG signal is recognized by canonical discriminant analysis. From measured EMG data, we can discriminate 10 movements of the hand. So, our system will be a powerful one to support activities of the disabled.