Modeling and Classifying of sEMG Based on FFT Blind Identiflcation
Yang Li, Tian Yantao, Wanzhong Chen · 2012
1 Abstract In this paper, the FFT-based blind identiflcation method is used to establish surface electromyographic signal (sEMG) in order to overcome the disadvantage of sEMG, which is susceptible to muscle fatigue and external factors. With no assumption on the precise knowledge of channel order, the FFT (fast Fourier transform)-based method is able to estimate the channel parameters as well as determine channel order. It ex- tends the cross-relation principle to the frequency domain via the discrete Fourier transform, and performs better in small sample signal modeling, which is suitable for sEMG. The parameters of sEMG model are used as the input of the improved BP neu- ral network to classify difierent movement patterns and a better recognition result is achieved compared with other blind identi- flcation methods.