Self-organized clustering approach for motion discrimination using EMG signal.

In order to control a myoelectric hand, it is necessary to discriminate among motions using electromyography (EMG) signals. One of the biggest problems in doing so is that EMG feature patterns of different motions overlap, and a classifier cannot discriminate clearly between them. Therefore, we propose a motion discrimination method to solve this problem. In this method, representative feature patterns are extracted from the EMG signals by using a self-organized clustering method, and user's intended motions are assigned as class labels to these feature patterns on the basis of the joint angles of the hand and fingers. The classifier learns using training data that consists of feature patterns and class labels, and then discriminates motions. In an experiment, we compared the discrimination rates of the proposed and conventional methods. The results indicate that the discrimination rate obtained with the former is 5-30% higher than that obtained with the latter; this result verifies the effectiveness of our method.