Toward Improving the Reliability of Discrete Movement Recognition of sEMG Signals

Abstract

Currently, the classification accuracy of surface electromyography (sEMG) signals is high in literature, but the conventional recognition system may classify untrained movements or the trained movements of low reliability to one of its target classes by mistake. If such a system is used for prosthetic control, sometimes it may cause a disaster. A two-layer classifier that fuses the Gaussian mixture model (GMM) and k-nearest neighbor (kNN) in a sequential structure is proposed in this study. The proposed algorithm can reject the trained movements with low reliability and is efficient in rejecting the untrained movements, thus enhancing the reliability of the myoelectric control system. The results show that the proposed algorithm can produce 95.7% active accuracy in recognizing 12 trained movements and a 30.3% error rate for rejecting 12 untrained movements. When the movement number is six, the active accuracy for trained movements can reach 99.2%, and the error rate of untrained movement is only 17.4%, which is much better than previous studies. Therefore, the proposed classifier can accurately recognize the trained movements and reject untrained movement patterns effectively.