Conservative Subject Invariant EMG-based Gesture Recognition
Cross-subject generalization remains a fundamental challenge in surface electromyography (sEMG)-based gesture recognition. Although deep learning methods have improved within-subject performance, they often rely on subject-specific data and struggle to balance invariance and discriminability. In this work, we propose a conservative multi-objective learning framework for subject-invariant sEMG gesture recognition. The proposed model adopts a multi-head architecture that jointly optimizes gesture classification, adversarial subject confusion through gradient reversal, and triplet-based metric learning to encourage discriminative and subject-invariant representations. To improve optimization...
| Authors | Hamed Rafiei, Ali Mousavi |
|---|---|
| Primary category | cs.LG |
| Published | 4 July 2026 |
| Last updated at source | 4 July 2026 |
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