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  4. Vol 8, Issue 6, 2024

A dual-branch network architecture for sEMG-based gesture recognition

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DOI: 10.23977/acss.2024.080613 | Downloads: 18 | Views: 839

Author(s)

Chenyu Shi 1, Yuchun Wang 1

Affiliation(s)

1 School of Information and Electronic Technology, Jiamusi University, Jiamusi, China

Corresponding Author

Yuchun Wang

ABSTRACT

The surface electromyography (sEMG) signal, as a type of bioelectrical signal, has been widely applied in modern human-computer interaction, especially for gesture recognition. The rapid advancement of deep learning has significantly promoted the development of sEMG-based gesture recognition technology. However, existing studies often face challenges such as insufficient feature extraction from sEMG signals and low differentiation between similar gestures. To address these issues, this study proposes a novel dual-branch model architecture specifically designed for sparse-channel sEMG gesture recognition. The model leverages the strengths of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory-Transformer (BiT) networks to process both the time-frequency representations and raw signals of sEMG data, thoroughly extracting spatiotemporal features. Additionally, the proposed Hybrid Attention Block (HAB) further enhances the feature representation capability of the CNN branch. To verify the model's effectiveness, multiple experiments were conducted on the NinaPro-DB1 dataset. The results demonstrate that the proposed model achieved a classification accuracy of 89.23%, outperforming most mainstream models.

KEYWORDS

sEMG, Gesture Recognition, Deep Learning, CNN, BiLSTM, Transformer

CITE THIS PAPER

Chenyu Shi, Yuchun Wang, A dual-branch network architecture for sEMG-based gesture recognition. Advances in Computer, Signals and Systems (2024) Vol. 8: 86-93. DOI: http://dx.doi.org/10.23977/acss.2024.080613.

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