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Increasingly popular home assistants are widely utilized as the centralcontroller for smart home devices. However, current designs heavily rely onvoice interfaces with accessibility and usability issues; some latest ones areequipped with additional cameras and displays, which are costly and raiseprivacy concerns. These concerns jointly motivate Beyond-Voice, a noveldeep-learning-driven acoustic sensing system that allows commodity homeassistant devices to track and reconstruct hand poses continuously. Ittransforms the home assistant into an active sonar system using its existingonboard microphones and speakers. We feed a high-resolution range profile tothe deep learning model that can analyze the motions of multiple body parts andpredict the 3D positions of 21 finger joints, bringing the granularity foracoustic hand tracking to the next level. It operates across differentenvironments and users without the need for personalized training data. A userstudy with 11 participants in 3 different environments shows that Beyond-Voicecan track joints with an average mean absolute error of 16.47mm without anytraining data provided by the testing subject.

Beyond-Voice: Towards Continuous 3D Hand Pose Tracking on Commercial Home Assistant Devices