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    Summary
    This summary is machine-generated.

    This study presents a cost-effective, embedded wireless platform for real-time hand gesture recognition using TinyML and high-density surface electromyography (HD-sEMG). The system enhances prosthetic device AI integration with on-device fine-tuning capabilities.

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    Area of Science:

    • Biomedical Engineering
    • Artificial Intelligence
    • Wearable Technology

    Background:

    • Myoelectric prosthetics often rely on complex, external hardware for control.
    • Real-time processing of high-density surface electromyography (HD-sEMG) signals is crucial for intuitive prosthetic control.
    • Integrating artificial intelligence (AI) directly into prosthetic devices can enhance functionality and reduce costs.

    Purpose of the Study:

    • To develop a fully embedded wireless platform for real-time hand gesture recognition using TinyML and HD-sEMG.
    • To evaluate the performance of the Coral Tensor Processing Unit (TPU) accelerator for on-device inference.
    • To explore the effectiveness of on-device versus cloud-based model fine-tuning and data quantization techniques.

    Main Methods:

    • Developed an embedded wireless platform using off-the-shelf components and the Coral TPU accelerator.
    • Implemented TinyML for real-time hand gesture recognition utilizing 64-channel HD-sEMG data.
    • Investigated on-device and cloud-based model fine-tuning strategies.
    • Explored 8-bit data quantization techniques to optimize hardware compatibility and performance.

    Main Results:

    • Achieved a general inference time of 2.96 ms, demonstrating the TPU's suitability for real-time tasks.
    • On-device fine-tuning improved gesture recognition accuracy by up to 36.15% in intersession tests, comparable to cloud-based methods.
    • 8-bit quantization maintained or slightly improved performance, with a best-case improvement of 0.96% over unquantized data.
    • The platform offers a cost-effective, self-sufficient solution for AI integration in prosthetics.

    Conclusions:

    • The developed embedded platform provides a robust foundation for on-device HD-sEMG based hand gesture recognition.
    • This approach offers a more accessible and practical solution for myoelectric prosthetic control.
    • The findings support the integration of TinyML and efficient hardware accelerators for advanced wearable AI applications.