sEMG-Based Gesture Recognition with Spiking Neural Networks on Low-Power FPGA

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.