sEMG-based gesture recognition with spiking neural networks on low-power FPGA

Scrugli, Matteo Antonio

;Leone, Gianluca;Busia, Paola;Meloni, Paolo

2024-01-01

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Abstract

Classification of surface electromyographic (sEMG) signals for the precise identification of hand gestures is a crucial area in the advancement of complex prosthetic devices and human-machine interfaces. This study presents a real-time sEMG classification system, exploiting a Spiking Neural Network (SNN) to distinguish among twelve distinct hand gestures. The system is implemented on a Lattice iCE40-UltraPlus FPGA, explicitly designed for low-power applications. Evaluation on the NinaPro DB5 dataset confirms an accuracy of 85.6%, demonstrating the model’s effectiveness. The power consumption for this architecture is approximately 1.7 mW, leveraging the inherent energy efficiency of SNNs for low-power classification.