Application of improved BP algorithm to surface EMG signal classification / 医疗卫生装备

ABSTRACT

The application of improved BP neural network together with the wavelet transform to the classification of surface EMG signal is described. The data reduction and preprocessing of the signal are performed by wavelet transform. The network can identify such four kinds of forearm movements with a high accuracy as hand extension, clench fist, forearm pronation and forearm supination. This paper compares the results by standard BP algorithm with that of Bayesian regularization together with LM algorithm. Experimental result shows that the improved BP neural network has a great potential when applied to electromechanical prosthesis control because of its enhanced training speed and identification accuracy.