Abstract: In the process of rehabilitation training,patients need the assistance of external equipment to complete the exercise.During this process,the patient’s muscle function gradually recovers,and the auxiliary force provided by the auxiloiary equipments gradually becomes smaller.This requires rehabilition training equipments to be able to accurately predict a wide range of muscle strength.Aiming at this problem,a stratified algorithm based on surface electromyography (sEMG) for accurately predicting muscle strength was proposed.In the first stratified algorithm,the Particle swarm optimization (PSO) algorithm is used to improve the Support Vector Machines (SVM) algorithm,to solve the problems of noise in sEMG and nonlinear separability of the signal itself.The improved SVM is used to build a three classifier and the muscle force is prehminaril divded into three categovies:high,medium and low.The second stratified algorithm uses three corresponding to different muscle strength BP neural networks to accurately predict muscle strength.Experiment results show that 20 repeated calculations gave an average absolute error of 0.58 and a variance of 0.18.It is concluded that the combined model scheme using PSO_SVM+BP can achieve the accuracy of muscle strength prediction.

Key words: BP neural network, Particle swarm optimization algorithm, Stratified algorithm, Support vector machines, Surface electromyography