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Related Experiment Video

Updated: Mar 28, 2026

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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Adversarially regularized transformer with channel-wise noise for robust hand gesture recognition using surface

Doheun Cha1, Dong-Gyu Lee2, Sangtae Ahn1

  • 1School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, 41566 South Korea.

Biomedical Engineering Letters
|March 27, 2026
PubMed
Summary

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This study introduces a new network for surface electromyography (sEMG) hand gesture recognition, enhancing accuracy and robustness against noise and attacks. The method shows state-of-the-art performance in prosthetic control and human-computer interaction applications.

Area of Science:

  • Biomedical Engineering
  • Machine Learning
  • Signal Processing

Background:

  • Surface electromyography (sEMG) enables hand gesture recognition via skin surface electrical signals.
  • sEMG signal non-stationarity and noise hinder reliable classification.
  • Existing methods struggle with robustness and generalization.

Purpose of the Study:

  • To develop a novel network for robust sEMG-based hand gesture recognition.
  • To improve classification accuracy and generalization despite signal challenges.
  • To enhance model resilience against adversarial attacks.

Main Methods:

  • Proposed a novel network integrating adversarial training with noise perturbation.
  • Utilized benchmark datasets NinaPro DB1, DB2, and DB4 for evaluation.
Keywords:
Adversarial trainingHand gesture recognitionNoise perturbationSurface electromyography

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  • Tested performance in aggregated and subject-wise evaluations.

    • Main Results:

    • Achieved state-of-the-art results on NinaPro DB1, DB2, and DB4 datasets.
    • Demonstrated significant performance improvements compared to EMGHandNet.
    • Showcased enhanced robustness against Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) attacks.

    Conclusions:

    • The proposed network significantly enhances sEMG hand gesture recognition accuracy and robustness.
    • Adversarial training proves effective in improving generalization and resilience.
    • The approach addresses critical limitations for real-world applications like prosthetic control.