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    Summary
    This summary is machine-generated.

    Machine learning models simplify magnet localization for myokinetic control interfaces, enhancing prosthetic hand control. This data-driven approach offers improved accuracy and efficiency for wearable devices.

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    Area of Science:

    • Biomedical Engineering
    • Machine Learning
    • Prosthetics

    Background:

    • Myokinetic control interfaces use implanted magnets for prosthetic control.
    • Previous magnet localization relied on complex optimization procedures.
    • Need for faster, more efficient localization methods for wearable devices.

    Purpose of the Study:

    • To develop and implement machine learning models for simplified magnet localization.
    • To improve the speed and efficiency of prosthetic control systems.
    • To optimize hardware and energy consumption for wearable applications.

    Main Methods:

    • Employed linear and radial basis functions artificial neural networks (ANNs).
    • Developed ANNs offline and implemented them on field-programmable gate arrays (FPGAs).
    • Optimized computational precision, execution time, hardware, and energy consumption.

    Main Results:

    • Achieved a tracking accuracy of 720 micrometers 95% of the time for a single magnet.
    • Demonstrated a latency of 12.07 microseconds.
    • Proposed system architecture is expected to be more power-efficient than previous solutions.

    Conclusions:

    • Machine learning offers a simplified and accelerated approach to magnet localization for myokinetic control.
    • The developed system shows promise for efficient and accurate prosthetic control.
    • Further research is encouraged for simultaneous multi-magnet tracking.