Classification of sEMG signals using integrated neural network with small sized training data

摘要

The sEMG (Surface electromyography) signals detected from activated muscles can be used as a control source for prosthesis. So an efficient and accurate method for the classification of sEMG signal patterns has become a hot research in recent years. Artificial neural network is a popular used method in this field, however, most neural networks require large numbers of samples in the training stage to obtain the potential relationships between input feature vectors and the outputs. In this paper, Integrated back propagation neural network (IBPNN) is used to classify sEMG signals acquired during five different hand motions. The correct classification rates of IBPNN for the five hand movements are significantly higher than that of BPNN and Elman neural network. This reveals that IBPNN achieves the best performance with a small sized training data and can be used in control systems on prosthetic hands and other robotic devices based on electromyography pattern recognition.