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Related Experiment Video

Updated: Jul 8, 2025

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

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Published on: March 28, 2025

503

Cross-subject EMG hand gesture recognition based on dynamic domain generalization.

Yalan Ye, Yujie He, Tongjie Pan

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    PubMed
    Summary

    This study introduces a new dynamic domain generalization method for electromyography (EMG) signal-based gesture recognition. The approach enables accurate hand gesture recognition for new users without needing calibration data, overcoming a key limitation.

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    Area of Science:

    • Biomedical Engineering
    • Machine Learning
    • Human-Computer Interaction

    Background:

    • Electromyography (EMG) based gesture recognition commonly uses transfer learning.
    • Cross-subject methods require calibration data from new users, which is inconvenient.
    • This calibration requirement hinders the practical, daily application of EMG gesture recognition.

    Purpose of the Study:

    • To propose a novel dynamic domain generalization (DDG) method.
    • To enable accurate EMG-based hand gesture recognition for new subjects without calibration data.
    • To overcome the obstacle of calibration data collection in current EMG recognition systems.

    Main Methods:

    • A meta-adjuster is employed to generate template coefficients for dynamic network parameter adjustment.
    • Two types of templates are designed: diverse features (temporal, spatial, spatio-temporal) and normalization layers.
    • A mix-style data augmentation technique is utilized to enhance the meta-adjuster's training data diversity.

    Main Results:

    • The proposed DDG method achieves accurate recognition of hand gestures from new subjects.
    • The method successfully eliminates the need for calibration data.
    • Experimental results on a public dataset demonstrate superior performance compared to existing methods.

    Conclusions:

    • The novel DDG method effectively addresses the challenge of cross-subject EMG gesture recognition.
    • This approach significantly improves the usability of EMG-based gesture recognition systems by removing calibration requirements.
    • The findings pave the way for more accessible and widespread adoption of EMG technology.