On Optimizing Electrode Configuration for Wrist-Worn sEMG-Based Thumb Gesture Recognition

TL;DR

This study systematically evaluates electrode configurations for wrist-worn sEMG systems to optimize thumb gesture recognition, revealing key factors that influence performance and providing practical design guidelines.

Contribution

It offers the first comprehensive analysis of electrode placement, referencing, and density effects on wrist-based sEMG gesture recognition performance.

Findings

Extensor-side electrodes outperform flexor-side electrodes.

Monopolar recordings outperform bipolar configurations.

Increasing channel count improves performance with diminishing returns.

Abstract

Thumb gestures provide an effective and unobtrusive input modality for wearable and always-available human-machine interaction. Wrist-worn surface electromyography (sEMG) has emerged as a promising approach for compact and wearable human-machine interfaces. However, compared to forearm sEMG, the impact of electrode configuration on wrist-based decoding performance remains understudied. We systematically investigated electrode configuration strategies for wrist-based thumb-movement recognition using high-density (HD) and low-density (LD) sEMG measurement systems. We considered factors such as muscle region, reference scheme, channel count, and spatial density of the electrode. Experimental results show that 1) extensor-side electrodes outperform flexor-side electrodes (HD: 0.871 vs. 0.821; LD: 0.769 vs. 0.705); 2) monopolar recordings consistently outperform bipolar configurations (15 channel with HD monopolar vs. LD bipolar: 0.885 vs. 0.823); and 3) increasing channel count enhances performance, but exhibits diminishing returns. We further show that electrode spatial distribution introduces a trade-off between spatial coverage and compactness. The findings suggest that the effectiveness of wrist-worn sEMG systems depends less on the deployment of a large number of electrodes in a broad sensing area and more on the optimization of electrode placement and the referencing scheme. This work provides practical guidelines for developing efficient wrist-worn sEMG-based gesture recognition systems.