Lightweight Spiking Neural Networks for Low-Power EMG-Based Hand Gesture Classification on Embedded Systems

Résumé fourni par la source

This paper presents a lightweight spiking neural network (SNN) application for EMG-based hand gesture classification, demonstrating greater suitability over artificial neural networks (ANNs) for low-power, real-time neuromorphic processing applications. The compact SNN architecture is ideal for embedded machine-learning applications, such as portable medical devices. In this paper, we demonstrate two SNN networks, A and B. Each network was deployed and physically tested on an embedded system powered by the ARM Cortex-M4. Network A is trained and evaluated on the Roshambo dataset, while Network B is trained and evaluated on the CapgMyo dataset. Network A achieved an accuracy of 78.89%, and Network B achieved an accuracy of 70.83% on hardware, highlighting the potential for real-time, low-power EMG analysis in wearable devices.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.