Identification Scheme of Surface Electromyography of Upper Limb Movement

In this study four pairs of Ag/AgCl surface electrodes respectively that pasted on the muscle belly of the biceps, triceps in the upper arm, palmaris longus and the brachioradialis muscle in the forearm are used to collect the electromyography (EMG) signals of six patterns of upper limb movements. The feature vectors of the EMG are extracted by wavelet decomposition method, and then these features are classified by the method of the support vector machine. Experimental results show that the wavelet decomposition is an effective feature extraction method. For small samples, nonlinear and high dimensional classification problems, the support vector machine method has better classification effect and generalization performance than the traditional BP neural network. To speed up the computation of the SVM and adapt the increase of the samples, the incremental SVM is also introduced. By comparing with the traditional SVM method, the advantage of the incremental SVM is demonstrated.