Liuyi Ling1,2,3, Yiwen Wang1,*, Fan Ding4, Li Jin1, Bin Feng3, Weixiao Li3, Chengjun Wang1, Xianhua Li1
CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2771-2790, 2023, DOI:10.32604/cmc.2023.043383
Abstract Surface electromyography (sEMG) is widely used for analyzing and controlling lower limb assisted exoskeleton robots. Behavior intention recognition based on sEMG is of great significance for achieving intelligent prosthetic and exoskeleton control. Achieving highly efficient recognition while improving performance has always been a significant challenge. To address this, we propose an sEMG-based method called Enhanced Residual Gate Network (ERGN) for lower-limb behavioral intention recognition. The proposed network combines an attention mechanism and a hard threshold function, while combining the advantages of residual structure, which maps sEMG of multiple acquisition channels to the lower limb motion states. Firstly, continuous wavelet transform… More >
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