Multi-class surface EMG classification using support vector machines and wavelet transform

In this paper, surface electromyographic signal is analyzed by wavelet transform. The feature vectors are built by extracting the singular value of the wavelet coefficients. The multi-class support vector machine classifier is designed by using four kinds of multi-class classification approaches, and completed the eight class surface EMG pattern classification. The SVM classifier is applied to the classification of eight movements with recording of the surface EMG. Experimental results show that the average recognition rate is over 90%. The classification accuracy of SVM classifier is significantly better than RBF neural network classifier.