Abstract
Surface electromyography (sEMG) signal is widely used in lower limb exoskeleton system because it can reflect human motion intention. Aiming at the problem of low decoding accuracy of joint motion angle of lower limb exoskeleton robot, a knee motion angle prediction method based on sEMG signal is proposed. Firstly, in order to solve the problem of high time efficiency and insufficient stability of single time domain feature extraction method for sEMG signal, the eigenvalues are extracted from time domain, frequency domain and time-frequency domain respectively, and the prediction error of different channel sEMG signal feature parameter combination model is analyzed. Then, an innovative particle swarm optimization LSTM deep learning algorithm (PSO-LSTM) is proposed to establish the map** model between sEMG signal and knee joint angle, and determine the PSO-LSTM model corresponding to the optimal feature parameter combination to realize the accurate prediction of knee joint angle. Finally, the experimental results show that the predicted results of the model are highly consistent with the real angle values, which is helpful to improve the human-computer cooperation of the lower limb exoskeleton system.
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Chen, J., Tao, Y., Huang, Y., Zhao, Z., Zhou, S. (2022). Joint Angle Prediction of Lower Extremity Exoskeleton Based on sEMG Signal. In: Jia, Y., Zhang, W., Fu, Y., Yu, Z., Zheng, S. (eds) Proceedings of 2021 Chinese Intelligent Systems Conference. Lecture Notes in Electrical Engineering, vol 804. Springer, Singapore. https://doi.org/10.1007/978-981-16-6324-6_68
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DOI: https://doi.org/10.1007/978-981-16-6324-6_68
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