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.
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© 2007 Springer Berlin Heidelberg
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Chen, L., Yang, P., Xu, X., Guo, X., Zhang, X. (2007). Fuzzy Support Vector Machine for EMG Pattern Recognition and Myoelectrical Prosthesis Control. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_152
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DOI: https://doi.org/10.1007/978-3-540-72393-6_152
Publisher Name: Springer, Berlin, Heidelberg
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