Modeling and classification of sEMG based on blind identification theory

Authors:
Yang Li;Yantao Tian;Xiaojing Shang;Wanzhong Chen
Affiliations:
School of Communication Engineering, Jilin University, Changchun;School of Communication Engineering, Jilin University, Changchun and Key Laboratory of Bionic Engineering, Ministry of Education Jilin University;School of Communication Engineering, Jilin University, Changchun;School of Communication Engineering, Jilin University, Changchun
Venue:
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
Year:
2011

Citing 2
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Abstract

Surface electromyography signal is non-stationary, susceptible to external interference. For this situation under this case, cyclostationary input with the inverse nonlinear mapping of the Hammerstein-Wiener model were combined to build surface electromyography model and to realize the blind discrete nonlinear system identification. The parameters of model were used as input of improved BP neural network. The experiments results demonstrated the effectiveness of this approach.