REACT: A Conditioning Framework for User-Adaptive sEMG Hand Pose Estimation
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866916061710909440 |
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| author | Xie, Eric Cheung, Hei Shing |
| author_facet | Xie, Eric Cheung, Hei Shing |
| contents | Surface electromyography (sEMG) enables continuous hand pose estimation on wearable devices, but models trained on multi-user corpora degrade on unseen individuals due to inter-user variability in anatomy and electrode placement. We propose REACT, a lightweight conditioning framework that personalizes a frozen pretrained EMG-to-pose backbone at inference time using only a handful of calibration recordings. REACT learns a compact user embedding from calibration data and applies Feature-wise Linear Modulation (FiLM) to adapt the shared encoder's feature space, requiring no gradient updates at deployment. On the large-scale EMG2POSE benchmark, REACT improves over the state-of-the-art baseline across all three generalization splits in both regression and tracking modes, reducing angular error by up to 3.9% with minimal parameter overhead and under 45 seconds of per-user calibration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_30127 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | REACT: A Conditioning Framework for User-Adaptive sEMG Hand Pose Estimation Xie, Eric Cheung, Hei Shing Human-Computer Interaction Signal Processing Surface electromyography (sEMG) enables continuous hand pose estimation on wearable devices, but models trained on multi-user corpora degrade on unseen individuals due to inter-user variability in anatomy and electrode placement. We propose REACT, a lightweight conditioning framework that personalizes a frozen pretrained EMG-to-pose backbone at inference time using only a handful of calibration recordings. REACT learns a compact user embedding from calibration data and applies Feature-wise Linear Modulation (FiLM) to adapt the shared encoder's feature space, requiring no gradient updates at deployment. On the large-scale EMG2POSE benchmark, REACT improves over the state-of-the-art baseline across all three generalization splits in both regression and tracking modes, reducing angular error by up to 3.9% with minimal parameter overhead and under 45 seconds of per-user calibration. |
| title | REACT: A Conditioning Framework for User-Adaptive sEMG Hand Pose Estimation |
| topic | Human-Computer Interaction Signal Processing |
| url | https://arxiv.org/abs/2605.30127 |