CPEP: Contrastive Pose-EMG Pre-training Enhances Gesture Generalization on EMG Signals Apple Machine Learning Research

​[[{“value”:”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…”}]] [[{“value”:”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