Fuzzy Support Vector Machine for EMG Pattern Recognition and Myoelectrical Prosthesis Control

Authors:
Lingling Chen;Peng Yang;Xiaoyun Xu;Xin Guo;Xueping Zhang
Affiliations:
School of Electrical Engineering and Automation, Hebei University of Technology, 300130 Tianjin, China;School of Electrical Engineering and Automation, Hebei University of Technology, 300130 Tianjin, China;School of Electrical Engineering and Automation, Hebei University of Technology, 300130 Tianjin, China;School of Electrical Engineering and Automation, Hebei University of Technology, 300130 Tianjin, China;School of Electrical Engineering and Automation, Hebei University of Technology, 300130 Tianjin, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

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Abstract

For the optional control to the trans-femoral prosthesis and natural gait, an ongoing investigation of lower limb prosthesis model with myoelectrical control was presented. In this research, the surface electromyographic signals of lower limb were extracted to be switch signal, and translate into movement information. Considering every muscle's different physiologic tendency, fuzzy support vector regression method was applied to establish an intelligent black box that can interpret the physiological signals to accurate information of knee joint angle. It achieves a comparable or better performance than other methods, and provides a more native gait to the prosthesis user.