AR and WNN hybrid method for pattern recognition of SME signals

Wavelet neural networks (WNN) combine the functions of time-frequency localization from the wavelet transform and of self-studying from the neural network, which make them particularly suitable for the classification of complex patterns. Based on auto-regressive (AR) model and WNN, pattern recognition of prosthesis movements was studied in this paper. Firstly, we present an Auto-regressive (AR) Model analysis of surface myoelectric (SME) signals recorded on the ulnar flexor carpi and extensor carpi region of the right hand in resting position. Then we chose BP neural network classifier, RBF neural network classifier and Wavelet Neural Network (WNN) classifier to research the correlation between SME signals and wristwork. Four types of prosthesis movements are recognized by extracting four-order AR coefficient and construct them as eigenvector into different Neural Network. The experiment results show that the proposed WNN method achieved a mean classification accuracy of 93.75% of four movements (hand open (HO), hand close (HC), forearm in torsion (FI) and forearm extortion (FE)), which outperformed the method using BP Neural Network (with mean classification accuracy 86.25%) and the RBF Neural Network method (87.5%). It indicated that the AI and WNN hybrid method was feasible for pattern recognition of SME signals.