Hand gesture classification using high-quality structured data such as videos, im-
ages, and hand skeletons is a well-explored problem in computer vision. Alterna-
tively, leveraging low-power, cost-effective bio-signals, e.g., surface electromyo-
graphy (sEMG), allows for continuous gesture prediction on wearable devices.
In this work, we aim to enhance 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. Specif-
ically, we propose EMBridge, a… Read More
EMBridge: Enhancing Gesture Generalization from EMG Signals through Cross-Modal Representation Learning
Hand gesture classification using high-quality structured data such as videos, im-
ages, and hand skeletons is a well-explored problem in computer vision. Alterna-
tively, leveraging low-power, cost-effective bio-signals, e.g., surface electromyo-
graphy (sEMG), allows for continuous gesture prediction on wearable devices.
In this work, we aim to enhance 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. Specif-
ically, we propose EMBridge, a…
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