Abstract：In order to improve the accuracy of gait recognition, the gait recognition of human lower limb was studied based on the fusion of surface electromyography (sEMG), knee joint angle and plantar pressure. Firstly, the sEMG signals were decomposed by wavelet packet to extract the features of multi-scale energy and multi scale fuzzy entropy. Then, the principal component analysis (PCA) method was employed to reduce the dimension of the feature value of sEMG, and the feature vectors were constituted by the features of sEMG, plantar pressure and the knee energy. Finally, the feature vectors were inputted into the least squares support vector machine (PSO-LSSVM) optimized by the particle swarm to recognize gait of lower limb. The experimental results show that this method has higher recognition accuracy and validity than other methods.
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2018, Vol. 39 
