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Real-Time sEMG Processing With Spiking Neural Networks on a Low-Power 5K-LUT FPGA.

Matteo Antonio Scrugli, Gianluca Leone, Paola Busia

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    PubMed
    Summary
    This summary is machine-generated.

    This study introduces low-power Spiking Neural Networks (SNNs) on an FPGA for real-time surface electromyographic (sEMG) signal processing. The system achieves 83.17% accuracy in gesture recognition and 0.875 correlation in force modeling for advanced prosthetics.

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    Area of Science:

    • Biomedical Engineering
    • Neuroscience
    • Computer Science

    Background:

    • Surface electromyographic (sEMG) signals are crucial for developing advanced prosthetic devices and human-machine interfaces.
    • Accurate modeling of hand movement from sEMG enables continuous tracking beyond discrete gesture recognition.
    • Low-power, efficient processing is essential for wearable and implantable assistive technologies.

    Purpose of the Study:

    • To present real-time sEMG processing solutions using lightweight Spiking Neural Networks (SNNs) implemented on an FPGA.
    • To evaluate the performance of SNNs for both discrete finger gesture recognition and continuous finger force modeling.
    • To demonstrate the suitability of these systems for low-power applications.

    Main Methods:

    • Implementation of two real-time sEMG processing solutions based on Spiking Neural Networks (SNNs).
    • Efficient hardware implementation on a Lattice iCE40-UltraPlus FPGA for low-power operation.
    • Performance assessment using the NinaPro DB5 dataset for gesture recognition and the Hyser dataset for force modeling.

    Main Results:

    • Achieved 83.17% accuracy in classifying twelve different finger gestures.
    • Demonstrated a correlation of up to 0.875 for continuous finger force modeling.
    • Reported low power consumption: 11.31 mW active power, 44.6 µJ per gesture classification, and 1.19 µJ per force inference.

    Conclusions:

    • Lightweight SNNs efficiently processed sEMG signals for both gesture recognition and force modeling on an FPGA.
    • The developed systems are highly suitable for low-power prosthetic and human-machine interface applications.
    • Dynamic power management further reduced average power consumption significantly.