Hand gesture recognition attracts great attention for interaction since it is intuitive and natural to perform. In this paper, we explore a novel method for interaction by using bone-conducted sound generated by finger movements while performing gestures. We design a set of gestures that generate unique sound features, and capture the resulting sound from the wrist using a commodity microphone. Next, we design a sound event detector and a recognition model to classify the gestures. Our system achieves an overall accuracy of 90.13% in quiet environments and 85.79% under noisy conditions. This promising technology can be deployed on existing smartwatches as a low power service at no additional cost, and can be used for interaction in augmented and virtual reality applications.
@article{arxiv.2112.05986,
title = {Acoustic Sensing-based Hand Gesture Detection for Wearable Device Interaction},
author = {Bing Zhou and Matias Aiskovich and Sinem Guven},
journal= {arXiv preprint arXiv:2112.05986},
year = {2021}
}