Machine Learning

CPEP: Contrastive Pose-EMG Pre-training Enhances Gesture Generalization on EMG Signals

This paper was accepted at the Foundation Models for the Brain and Body Workshop at NeurIPS 2025.
Hand gesture classification using high-quality structured data such as videos, images, and hand skeletons is a well-explored problem in computer vision. Leveraging low-power, cost-effective biosignals, e.g. surface electromyography (sEMG), allows for continuous gesture prediction on wearables. In this paper, we demonstrate that learning representations from weak-modality data that are aligned with those from structured, high-quality data can improve representation quality and enables zero-shot…

​This paper was accepted at the Foundation Models for the Brain and Body Workshop at NeurIPS 2025.
Hand gesture classification using high-quality structured data such as videos, images, and hand skeletons is a well-explored problem in computer vision. Leveraging low-power, cost-effective biosignals, e.g. surface electromyography (sEMG), allows for continuous gesture prediction on wearables. In this paper, we demonstrate that learning representations from weak-modality data that are aligned with those from structured, high-quality data can improve representation quality and enables zero-shot… ​​ Read More

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