In order to improve the recognition rate of lower limb motion pattern

(a novel lower limb motion recognition method was designed by fusion of surface electromyography (sEMG) signal and acceleration signal.Firstly

the sEMG signal was decomposed into a set of product functions(PFs)by Local mean decomposition(LMD)

and the multiscale permutation entropy(MPE) of PFs was calculated.Then

one scale permutation entropy was selected as the feature of sEMG by the Laplacian score.The feature vector is composed by this sEMG feature and the permutation entropy of acceleration signal.Finally

based on the combination of inter-class Euclidean distance and intra-class sample distribution

an improved support vector machine based binary tree(ISVM-BT) was designed.The feature vector was inputted into this SVM to recognize the lower limb motion.The experimental results indicate that the proposed method achieved 98.62% at the average recognition rate for seven daily activities

and has higher accuracy than other methods.