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    This study introduces a wearable system for recognizing gestures using electromyography (EMG) and hyperdimensional computing. It achieves high accuracy with on-chip learning, offering an energy-efficient, fully embedded solution for biopotential recognition.

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    Area of Science:

    • Biomedical Engineering
    • Computer Science
    • Wearable Technology

    Background:

    • Gesture recognition systems often require offline training and significant power.
    • Wearable biopotential monitoring demands energy-efficient processing for practical applications.

    Purpose of the Study:

    • To develop a wearable electromyographic (EMG) gesture recognition system using hyperdimensional computing (HDC).
    • To enable efficient on-chip, online learning for a fully embedded system.
    • To evaluate the system's accuracy, energy efficiency, and scalability.

    Main Methods:

    • Implemented a gesture recognition system on a programmable parallel ultra-low-power (PULP) platform.
    • Utilized hyperdimensional computing for efficient on-chip training and classification.
    • Tested the system on 10 subjects recognizing 11 distinct gestures.

    Main Results:

    • Achieved 85% average accuracy for 11 gestures, comparable to state-of-the-art systems.
    • Demonstrated online learning capability, a unique feature for embedded systems.
    • Reported low energy consumption: 10.04 mJ for learning and 83.2 μJ per classification.
    • Achieved 10.4 mW average power consumption, providing ~29 hours of autonomy with a 100 mAh battery.

    Conclusions:

    • The proposed HDC-based wearable system offers a highly accurate and energy-efficient solution for EMG gesture recognition.
    • The system's on-chip learning capability and scalability (up to 256 electrodes) make it suitable as a universal biopotential recognition framework.