Multi-Channel sEMG Based Human Lower Limb Motion Intention Recognition Method

Abstract

The paper presents a multi-channel sEMG based human lower limb motion intention recognition method, aiming at solving the problem of human lower limb motion intention recognition when using an exoskeletal robot. The cepstrum distance is used to automatically detect the endpoints of the sEMG signal for each motion. There are extracted the time domain and frequency domain characteristic parameters of the multi-channel sEMG signal, which are used to merge and constructe a joint feature matrix. The joint feature matrix is reduced by the principal component analysis (PCA) method, and a low-dimensional matrix of each motion is obtained. Traditional back propagation (BP) neural network model is optimized by the use of particle swarm optimization (PSO) algorithm. The low-dimensional matrix of each motion of the human lower limb is identified by the optimized BP neural network model. The average motion recognition rate of the improved method from 86.3%±8.24% to 93.6%±2.6% compared with the classical BP neural network algorithm in the recognition experiment. Multi-channel sEMG based human lower limb motion intention recognition method is reliable and effective.