EMG-GPT: Predictive Pretraining on Residual-Quantized EMG Tokens for Hand Pose Estimation

Abstract:Surface electromyography (sEMG) is a low-power, cost-effective biosignal for hand-pose estimation and gesture classification. In this work, we examine whether self-supervised pretraining on sEMG can yield transferable representations for continuous hand-pose estimation. We introduce EMG-GPT, a causal transformer-based model that operates on discrete sEMG representations from a frozen residual vector quantization (RVQ) tokenizer and learns temporal dynamics through depth-autoregressive future-code prediction. The model combines within-frame integration with causal temporal modeling while preserving the geometry of the pretrained codebook. EMG-GPT shows competitive results in both Regression and Tracking tasks, supporting EMG-only pretraining as a viable approach for learning transferable sEMG representations.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2610.05235 [cs.LG]

(or arXiv:2610.05235v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.05235 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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