Understanding of Task-specific and Subject-specific Components in Surface EMG

2026-07-16

AI Summary

This work addresses the limited generalization of surface electromyography (sEMG)-based gesture recognition and user authentication across individuals, which stems from inter-subject neuromuscular variability. The authors propose, for the first time, a deep disentanglement model within a multi-task learning framework that explicitly separates task-relevant (gesture) representations from subject-specific (identity) features, thereby uncovering their fundamental differences in cross-day stability and underlying physiological mechanisms. Experimental results demonstrate that the proposed approach significantly improves gesture recognition accuracy in both cross-subject and cross-day scenarios. Furthermore, while the extracted identity-related components can support user authentication, their relatively low cross-day stability corroborates the efficacy and physiological interpretability of the disentanglement strategy.

Abstract

Surface electromyogram (sEMG) signals are widely used in human-machine interfaces for gesture recognition and user identification, but existing models often struggle to generalize across individuals due to subject-specific neuromuscular characteristics. This study introduces a disentanglement model that separates task-specific and subject-specific components from sEMG signals, thereby improving the generalization and interpretability of gesture recognition and user identification systems. Experimental results demonstrate that the disentangled components significantly improve the accuracy of both gesture classification and user identification across subjects and days, outperforming conventional methods under the same experimental conditions. Further analysis reveals that the task-specific components capture consistent activation patterns associated with the same gestures across individuals. In contrast, the subject-specific components reflect unique neuromuscular characteristics that can be used for user identification. Notably, the subject-specific components show lower similarity across days than the task-specific components, contributing to a greater decrease in user identification accuracy than in gesture recognition accuracy. These findings suggest that the disentanglement approach not only improves classification performance but also provides deeper insights into the physiological mechanisms underlying sEMG signals. The model's ability to isolate and interpret different neuromuscular components holds promise for enhancing the robustness of sEMG-based applications in real-world settings, including rehabilitation and user authentication. Our code is available at https://github.com/Open-EXG/HandDisentanglement.

Problem

• surface EMG
• gesture recognition
• user identification
• subject-specific variability
• generalization

Innovation

• disentanglement
• surface EMG
• task-specific component
• subject-specific component
• cross-subject generalization