Towards Smartphone-based 3D Hand Pose Reconstruction Using Acoustic Signals

Accurately reconstructing 3D hand poses is a pivotal element for numerous Human-Computer Interaction applications. In this work, we propose SonicHand, the first Smartphone-based 3D Hand Pose Reconstruction system using purely inaudible acoustic signals. SonicHand incorporates signal processing techniques and a deep learning framework to address a series of challenges. Firstly, it encodes the topological information of the hand skeleton as prior knowledge and utilizes a deep learning model to realistically and smoothly reconstruct the hand poses. Secondly, the system employs adversarial training to enhance the generalization ability of our system to be deployed in a new environment or for a new user. Thirdly, we adopt a hand tracking method based on channel impulse response (CIR) estimation. It enables our system to handle the scenario where the hand performs gestures while moving arbitrarily as a whole. We conduct extensive experiments on a smartphone testbed to demonstrate the effectiveness and robustness of our system from various dimensions. The experiments involve 10 subjects performing up to 12 different hand gestures in 3 distinctive environments. When the phone is held in one of the user’s hand, the proposed system can track joints with an average error of 18.64 mm.