Hand Gesture Classification Using sEMG Signals: Nearest-Centroid-Based Methodology With DBA

Abstract

Surface electromyography (sEMG), the electrical signals generated by muscle activity, plays an important role in prosthetics, orthotics, and human-computer interaction because of its non-invasive signal acquisition, real-time responsiveness, and ability to provide rich information for decoding complex movements. However, the complexity and nonlinear characteristics of sEMG signals present challenges in implementing sEMG-based tasks, such as hand gesture classification. Motivated by effectively addressing the challenges and extending the applicability of sEMG signals, we propose a nearest-centroid-based hand gesture classification framework using sEMG signals. We utilize the Dynamic Time Warping Barycenter Averaging (DBA) algorithm to generate centroids of sEMG signals and leverage Dynamic Time Warping (DTW) to measure signal discrepancies. Our pipeline features an effective data preprocessing approach and a heuristic, repeatable, optimal parameter search process. Additionally, our framework supports interpretability analysis using Shapley values. Through our extensive experiments, we demonstrate that our framework has achieved an average accuracy of 90.0% and a peak accuracy of 91.25% on a public dataset Mendeley data-sEMG, which outperforms the surveyed existing methods and establishes our framework as state-of-the-art on such dataset.