CentiTrack: Toward Centimeter-Level Passive Gesture Tracking With Commodity WiFi

Gesture awareness plays a crucial role in promoting human-computer interface. Previous works either depend on customized hardware or need a priori learning of wireless signal patterns, facing downsides in terms of the privacy concern, availability, and reliability. In this article, we propose CentiTrack, the first centimeter-level passive gesture-tracking system that works with only three commodity WiFi devices, without any extra hardware modifications or wearable sensors. To this end, we first identify the channel state information (CSI) measurement error sources in the physical-layer process, and then denoise CSI by the complex ratio between adjacent antennas. Principal component analysis (PCA) is further adopted to separate the reflected signals from noises. Benchmark experiments are conducted to verify that the phase changes of denoised CSI are proportional to the length changes of the dynamic path reflected off the hand. In addition, we adopt the multiple signal classification (MUSIC) algorithm to estimate the Angle-of-Arrivals (AoAs) of dynamic paths, and then locate the initial position of hands with triangulation. We also propose a novel static componnets elimination algorithm for tracking correction by eliminating the components unrelated to motion. A prototype of CentiTrack is fully realized and evaluated in various real scenarios. Extensive experiments show that CentiTrack is superior in terms of tracking accuracy, sensing range, and device cost, compared with the state-of-the-arts.