Acoustic Sensing-based Hand Gesture Detection for Wearable Device Interaction
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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 quiet environments and 85.79 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.
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