Hardware-Based Spiking Neural Network for Real-Time Hand Gesture Recognition Using MYO Armband EMG Sensor

Hand gesture recognition is a key technology for improving prosthetic control and enhancing human-robot interaction. Traditional machine learning approaches struggle with real-time, low-power constraints in embedded systems. This paper proposes a hardware-based Spiking Neural Network (SNN) for real-time hand gesture recognition from electromyography (EMG) signals using the MYO Armband. The SNN leverages energy-efficient, event-driven computation to classify dynamic gestures in real-time, demonstrating applicability in myoelectric prosthetics and robotics. Unlike typical implementations that rely on offline training, this system executes on-chip online training and inference. Implemented on a Cyclone V FPGA, it achieves 76.0% accuracy with 1.2 ms latency, validating its suitability for responsive myoelectric control.