基于双流卷积神经网络的表面肌电信号上肢动作识别

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中图分类号：TP911.7；TP242.6DOI：10.3969/j.issn.1004-132X.2026.03.019 开放科学（资源服务）标识码（OSID）：

Abstract： In order to enhance the accuracy of upper limb motion recognition based on sEMG signals and to validate the applications of the intent recognition model in real rehabilitation robots，a upper limb mo tion recognition method was proposed using a two-stream convolutional neural network for sEMG signals. The approach began by applying wavelet threshold denoising，bandpass filtering，full-wave rectification， and envelope smoothing，folowed by sample construction using a sliding window. The original EMG signals were then processed with variational mode decomposition and discrete wavelet packet transform. Key intrinsic mode functions and wavelet packet transform coeficients were extracted as inputs for the two branches of the model to enable high-level feature learning. A temporal convolutional network was employed to capture temporal dynamics and global dependencies within the features.The feature fusion modulethen integrated the high-level feature information.The proposed method achieves average recognition accuracies of 93.43% ， 92.37% ，and 97.54% on the public Ninapro DB4/DB5 datasets respectively and self-collected data for 6 upper limb movements. The average recognition accuracy reaches 87% for the 6 upper limb movements of 5 participants.

Key words： upper extremity motion recognition; two-stream convolutional neural network； surface electromyographic （sEMG） signal； variational modal decomposition； discrete wavelet packet transform； upper extremity motion recognition experiment

0 引言

脑卒中俗称“中风”，具有高发病率、高致残率和高死亡率等显著特点[1]。（剩余15234字）