Hand gesture classification using high-quality structured data such as videos, images, and hand skeletons is a well-explored problem in computer vision. Alternatively, leveraging low-power, cost-effective bio-signals, e.g., surface electromyography (sEMG), allows for continuous gesture prediction on wearable devices. In this work, the focus is on enhancing EMG representation quality by aligning it with embeddings obtained from structured, high-quality modalities that provide richer semantic guidance, ultimately enabling zero-shot gesture generalization. The proposed EMBridge method aims to bridge the gap between EMG signals and high-quality modalities to improve gesture recognition and generalization capabilities.

<Apple 뉴스 본문 전체읽기>