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Related Experiment Video

Updated: May 2, 2026

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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DiffSpkSync: A Muscle Synergy-Guided Spiking Diffusion Model for EMG Signal Generation to Improve Gesture Recognition

Kejia Su, Bo Wan, Jiayang Huang

    IEEE Journal of Biomedical and Health Informatics
    |April 30, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces DiffSpkSync, a new method to generate more training data for high-density surface electromyography (HD-sEMG) hand gesture recognition (HGR). This approach enhances HGR model accuracy, crucial for medical and rehabilitation applications.

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    Area of Science:

    • Biomedical Engineering
    • Machine Learning
    • Human-Computer Interaction

    Background:

    • High-density surface electromyography (HD-sEMG) is vital for intuitive human-machine interaction.
    • Scarcity of training data limits the performance of HD-sEMG based hand gesture recognition (HGR) models, particularly in medical and rehabilitation contexts.

    Purpose of the Study:

    • To address data scarcity in HD-sEMG based HGR.
    • To propose DiffSpkSync, a novel generative framework to improve HGR model training data.

    Main Methods:

    • DiffSpkSync integrates muscle synergy-guided diffusion modeling for signal reconstruction.
    • It employs spiking neuron-based sparsification for reduced energy consumption.
    • A time-series mixup strategy is used to preserve local dynamics during data augmentation.

    Main Results:

    • Training gesture classifiers with DiffSpkSync augmented data consistently improved classification accuracy in both intrasession and intersession scenarios.
    • DiffSpkSync outperformed representative generative baselines like VAE, DCGAN, DANN-CRC, and PatchEMG.
    • Real-time validation showed an average end-to-end latency of 130.22 ms and 95.87% accuracy.

    Conclusions:

    • DiffSpkSync effectively enhances HGR model performance by addressing data scarcity.
    • The proposed framework demonstrates superior results compared to existing generative methods.
    • The method's efficiency and accuracy support its application in real-world HGR systems.