Abstract
Many hand motion recognition methods using Electromyogram (EMG) signals have been developed. Because most studies used only surface EMG signals from a forearm, available motion-related information was limited. In this paper, we report a SVM (Support Vector Machine) based hand motion recognition method using hybrid sensors. The hybrid sensor consists of an EMG sensor and an optical distance sensor, and can measure myoelectric activities and distance between the sensor and the skin surface at the same time. It is expected that distance changes caused by muscle elevation compensate the limited information derived from myoelectric activities. To examine the effectiveness of our method, we performed hand motion recognition experiments with four subjects. Experimental results showed that our method using hybrid sensors can improve motion recognition accuracy compared to when using only EMG signals.
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