Embedded online recognition of hand gesture EMG modes

Controlling a multi-DOF prosthetic hand by EMG signals demands for effective pattern recognition methods that can be easily embedded in the controller of the hand. In this paper, methods of K-nearest neighbor and support vector machine (SVM) were used to identify different modes of myoelectric signals, which were obtained in several on-line experiments. Both methods were performed on different training sample sets, called threshold set and steady-state set, and in the case of abundance and relative insufficiency of samples. Experimental results show that the SVM method is superior to K-nearest neighbor, and the real-time recognition results are better when using threshold dataset as training samples than using steady-state dataset. The proposed method, which is based on SVM and embedded in DSP, can discriminate 10 hand gesture EMG modes with a prediction accuracy of above 95% and a decision frequency of about 30 Hz.