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Related Experiment Video

Updated: Jan 15, 2026

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
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AdaptiveEdge: Adaptive Model Update for Motor-Intent Decoding With Knowledge Distillation and Efficient EMG Sensor

Mustapha Deji Dere, Giwon Ku, Ji-Hun Jo

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |October 16, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces AdaptiveEdge, a novel strategy for electromyogram (EMG)-based gesture decoding. AdaptiveEdge significantly improves accuracy for active rehabilitation and human-machine interaction by integrating real-time, on-device updates with offline training.

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    Area of Science:

    • Biomedical Engineering
    • Neuroscience
    • Signal Processing

    Background:

    • Electromyogram (EMG)-based gesture decoding is crucial for active rehabilitation and human-machine interaction.
    • Production-grade EMG sensors have limitations, and EMG decoders suffer performance degradation due to fatigue, electrode shifts, and varying conditions.

    Purpose of the Study:

    • To propose a low-cost EMG sensor grid and an advanced decoding strategy, AdaptiveEdge.
    • To address EMG signal disturbances and improve decoder performance through adaptive model updates.

    Main Methods:

    • Developed a low-cost EMG sensor grid.
    • Implemented AdaptiveEdge, an adaptive model update strategy integrating offline training with real-time on-device parameter updates.
    • Conducted comprehensive experiments to evaluate decoding accuracy and resource efficiency.

    Main Results:

    • AdaptiveEdge achieved 88.66% accuracy, a 10.18% improvement over methods without offline training (78.48%).
    • The strategy demonstrated significant accuracy enhancements across diverse EMG disturbance scenarios.
    • Optimized memory usage and energy consumption for on-device applications.

    Conclusions:

    • AdaptiveEdge offers a robust solution for EMG-based gesture decoding, enhancing accuracy and efficiency.
    • The proposed method is suitable for resource-constrained on-device applications like neuroprosthetics.
    • Advancements facilitate more effective and practical EMG-based devices for improved human-machine interaction.