Abstract：To improve the accuracy of human surface electromyography (sEMG) signals for the recognition of lower limb movements, an RBF neural network classification model based on genetic algorithm (GA) optimization was proposed in this work. The sEMG of eight kinds of daily lower limb movements was collected, and the ‘sym6' wavelet function was selected for filtering preprocessing of sEMG. The principal component analysis (PCA) was used to reduce the dimension of time-frequency domain features, and the feature vectors were input into the RBF neural network optimized by GA for training and recognition. Experimental results showed that the average recognition rate of this method for the eight lower limb movements of the same subject was 94.00%±0.45 %, and the recognition rate for the lower limb movements of 15 different subjects reached 89.3 %, which was 11.8 % higher and 6 s shorter than that of the traditional BP neural network. The proposed method displayed a high recognition accuracy in the application of using sEMG signals to recognize human lower limb movements, providing a reference for the study of intention recognition of lower limb intelligent rehabilitation robot and of assistance in the rehabilitation of patients with lower limb disabilities.
张鹏, 张峻霞, 刘瑞恒, Ahmed Mohamed Moneeb Elsabbagh. 基于GA-RBF神经网络和sEMG的下肢动作识别方法研究[J]. 中国生物医学工程学报, 2022, 41(1): 41-47.
Zhang Peng, Zhang Junxia, Liu Ruiheng, Ahmed Mohamed Moneeb Elsabbagh. The Study of Lower Limb Motion Recognition Method Based on GA-RBF Neural Network andsEMG Signals. Chinese Journal of Biomedical Engineering, 2022, 41(1): 41-47.
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