首页|An extended variational autoencoder for cross-subject electromyograph gesture recognition

An extended variational autoencoder for cross-subject electromyograph gesture recognition

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  • NETL
  • NSTL
  • Elsevier
© 2024 Elsevier LtdSurface electromyographic hand gesture recognition has gained significant attention in recent years, especially within the field of human–computer interfaces. However, cross-subject tasks remain challenging due to inherent individual differences. To address this, a novel approach for hand gesture recognition is proposed that leverages a subject-generalized variational autoencoder. This approach involves an extended variational autoencoder designed to disentangle input data into three distinct feature-specific representations. The primary classifier within the variational autoencoder focuses on gesture recognition, while two auxiliary classifiers work together to extract subject-specific and gesture-specific features. The gesture-specific features capture generalized characteristics applicable across all subjects, enabling direct application to new subjects. To enhance accuracy and stability, a competitive voting strategy is implemented. The effectiveness of the proposed method was evaluated using a dataset comprising six representative gestures performed by eight subjects. Comparative analysis with baseline models shows that our approach outperforms others, demonstrating superior generalization with an average accuracy of 90.52% in cross-subject validation.

Competitive votingCross-subjectFeature disentanglementGesture recognitionSurface electromyographicVariational autoencoder

Zhang Z.、Ming Y.、Shen Q.、Wang Y.、Zhang Y.

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School of Mechatronic Engineering and Automation Shanghai University

Department of Spine Surgery Renji Hospital Shanghai Jiaotong University School of Medicine

2025

10.1016/j.bspc.2024.106828

Biomedical signal processing and control

Biomedical signal processing and control

SCI
ISSN：1746-8094
年,卷(期)：2025.99(Jan.)
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