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SRM: A Source-Reprojection Module for Cross-Day sEMG Gesture Recognition.

Dian Li¹, Peiji Chen¹, Shunta Togo^1,2

¹Department of Mechanical and Intelligent Systems Engineering, The University of Electro-Communications, Tokyo 182-8585, Japan.

Sensors (Basel, Switzerland)

|June 26, 2026

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Surface electromyography (sEMG) gesture recognition improves with the Source-Reprojection Module (SRM) for unsupervised cross-day adaptation. This method enhances myoelectric interface calibration by adapting to new data without retraining the main classifier.

Area of Science:

Biomedical Engineering
Machine Learning
Human-Computer Interaction

Background:

Surface electromyography (sEMG) based gesture recognition accuracy decreases over time due to domain shift.
Recalibration is burdensome, especially in assistive devices requiring locked classifier versions for regulatory compliance.

Purpose of the Study:

To develop an unsupervised cross-day adaptation method for sEMG gesture recognition.
To enable adaptation without retraining the main classifier or using target-session labels.

Main Methods:

Proposed the Source-Reprojection Module (SRM), a plug-in front end for adaptation.
SRM uses conditional adversarial feature learning and a residual signal-space projector.
The method is guided by the frozen classifier's gradients, identity regularization, and latent-space distribution matching.

Keywords:

cross-day adaptation domain shift gesture recognition source-free adaptation surface electromyography unsupervised domain adaptation

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Main Results:

Mean holdout accuracy increased from 70.9% (frozen classifier) to 72.8% with SRM.
SRM outperformed the frozen baseline in 10 out of 12 subject-seed runs.
The adaptation showed a modest accuracy gain, supporting proof-of-mechanism.

Conclusions:

The Source-Reprojection Module (SRM) offers a viable approach for unsupervised cross-day adaptation in sEMG gesture recognition.
SRM allows for adaptation while keeping the main task classifier frozen, addressing practical constraints in assistive technology.
Further research is needed to validate these findings in larger populations and diverse clinical settings.