Feature Extraction Method of sEMG Based on Auto Permutation Entropy
XIE Ping, WEI Xiu-Li, DU Yi-Hao, CHEN Xiao-Ling
Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, College of Electrical Engineering, Yanshan University, Qinhuangdao 066004
Abstract：Due to the nonstationarity and the nonlinearity of the surface electromyogram (sEMG), a method based on the permutation entropy and auto mutual information is proposed to quantitatively describe the internal dynamic features of the sEMG and realize the description of nonlinear characteristics under different motion states. Experiments are carried out to acquire the sEMG data of elbow joint at different bending angles. The auto permutation entropies of the signals are calculated and used as the inputs of support vector machines to identify different motion states. The validity of the proposed method is verified by the comparative analysis of auto permutation entropy and other indexes describing the sEMG features.
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2014, Vol. 27 
