WiTrace: Centimeter-Level Passive Gesture Tracking Using WiFi Signals

Gesture tracking is a basic Human-Computer Interaction mechanism to control devices such as electronic Internet of Things and VR/AR devices. However, prior WiFi signal based systems focus on gesture recognition and provide results with insufficient accuracy, and thus cannot be applied for highprecision gesture tracking. In this paper, we propose a CSI based device-free gesture tracking system, called WiTrace, which leverages the CSI values extracted from WiFi signals to enable accurate gesture tracking. For 1D tracking, WiTrace derives the phase of the signals reflected by the hand from the composite signals, and measures the phase changes to obtain the movement distance. For 2D tracking, WiTrace proposes the first CSI based scheme to accurately estimate the initial position, and adopts the Kalman filter based on Continuous Wiener Process Acceleration model to further filter out tracking noise. Our results show that WiTrace achieves the estimated accuracy of 3.91 cm for initial position on average, and achieves cm-level accuracy, with mean tracking errors of 1.46 cm and 2.09 cm for 1D tracking and 2D tracking, respectively.