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SEmgFormer: Muscle Synergy Channel Attention Enhanced Vision Transformer Network For sEMG Motion Recognition Using STFT Spectrogram

Zhuangzhuang Li,Yuxuan Tian,Chenyi Guo,Ji Wu

Published 2025 in IEEE International Joint Conference on Neural Network

ABSTRACT

The recognition of motions based on surface electromyography (sEMG) has been extensively studied, yielding promising results from initial machine learning approaches to contemporary deep learning methods. However, most previous research has concentrated on the classification of movements from individual body parts, such as the widely used gesture dataset, NinaPro. Furthermore, much of work has been restricted to convolutional neural networks (CNNs) and their variants, without a thorough exploration of the synergistic effects of muscles from different body parts, often assigning equal weights to all muscles. This study presents the collection of electromyographic data from sixteen major muscles across the entire body, acquiring the MultiMotion-sEMG Dataset, which includes forty-three full-body movements from thirteen participants. According to the current knowledge, this is the first dataset designed to synchronize the capture of full-body surface electromyography (sEMG) signals. Based on this dataset, a novel sEMG recognition network, SEmgFormer, is proposed, which is augmented by a vision transformer (ViT). The short-time Fourier transform (STFT) is utilized to transform conventional time-domain sEMG signal recognition tasks into visual understanding tasks of time-frequency spectrograms, utilizing the Cutmix method for data augmentation. In addition, a novel Muscle Synergy Channel Attention (MS-CA) mechanism is introduced, improving the channel attention mechanism (CA). The results indicate that the proposed model surpasses other methods in performance, including CNN-based networks, achieving optimal accuracy. This validates the efficacy of the proposed ViT classifier using time-frequency spectrograms as input, enhancing the accuracy of sEMG recognition based on full-body signals, and paving new avenues for research in this field.

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