Abstract
In order to recognize the pattern of the powered type lower limb prosthesis movement accurately, a genetic algorithm and neural network algorithm was proposed in this paper. Gyroscope sensor which was installed in the prosthetic socket and pressure sensors which were installed in the Prosthetic foot were used for data acquisition in the experiment. The mean of the angle was calculated in different motion mode corresponding support phase and swing phase. They were input variables of the BP neural network model. Genetic algorithm was used to optimize the weights and bias of BP neural network. Optimized BP neural network was applied to establish recognition model. The results showed that the proposed method offered the advantages of high precision and fast convergence in contrast with BP neural network.
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Acknowledgments
This work is supported by Natural Science Foundation of China (61174009) and Natural Science Foundation of Hebei Province, China (F2011202155).
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© 2013 Springer-Verlag Berlin Heidelberg
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Liu, L., Yang, P., Liu, Z., Chen, L., Geng, Y. (2013). Pattern Recognition of the Thigh Amputee Motion Based on Genetic Algorithm and BP. In: Sun, Z., Deng, Z. (eds) Proceedings of 2013 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 254. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38524-7_31
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DOI: https://doi.org/10.1007/978-3-642-38524-7_31
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