Zhengyuan Xu1,2,#, Junxiao Yu1,#, Wentao Xiang1, Songsheng Zhu1, Mubashir Hussain3, Bin Liu1,*, Jianqing Li1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 157-177, 2023, DOI: 10.32604/cmes.2022.020035
Abstract In this article, to reduce the complexity and improve the generalization ability of current gesture recognition
systems, we propose a novel SE-CNN attention architecture for sEMG-based hand gesture recognition. The
proposed algorithm introduces a temporal squeeze-and-excite block into a simple CNN architecture and then
utilizes it to recalibrate the weights of the feature outputs from the convolutional layer. By enhancing important
features while suppressing useless ones, the model realizes gesture recognition efficiently. The last procedure of
the proposed algorithm is utilizing a simple attention mechanism to enhance the learned representations of sEMG
signals to perform multi-channel sEMG-based gesture recognition tasks.… More >
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