Portable and Lightweight Signal Processing Approach for sEMG-Based Human–Machine Interaction in Robotic Hands

Abstract

Surface electromyography (sEMG) presents a viable biosignal for the control of robotic prosthetic hands, as it directly correlates with underlying muscle activity. This study introduces an efficient, computationally lightweight signal processing methodology designed for real-time embedded systems. The proposed methodology comprises a preprocessing pipeline, incorporating bandpass and notch filtering, followed by segmentation via overlapping sliding windows. Time-domain features, specifically Mean Absolute Value (MAV), Zero Crossing (ZC), Waveform Length (WL), Slope Sign Change (SSC), and Variance (VAR), are extracted to characterize relevant muscular activation patterns. By prioritizing computational efficiency and embedded system feasibility, this method establishes a practical framework for user intent recognition and real-time control of wearable robotic hands, particularly within assistive and rehabilitative applications. The experimental findings clearly indicate that the extracted features effectively differentiate between various hand gestures, allowing for accurate, real-time control of the wearable robotic hand. The system's high responsiveness, low latency, and resilience to noise underscore its suitability for assistive and rehabilitative applications. With its focus on computational simplicity and feasibility for embedded implementation, the proposed method provides a practical basis for recognizing user intent in human-machine interaction systems.