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Envision a ubiquitously device-free motion sensing, this work focuses on the analysis of Wi-Fi-based hand gesture trajectory tracking by utilizing the Doppler frequency obtained from channel state information (CSI). Because human limb movement generates variant micro-Doppler signatures produced by the contribution of different parts of the hand and arm surfaces to the spectrum, an estimation technique is proposed to extract the temporal profile of the hand-only Doppler signature. With a set of Doppler profiles from different pairs of Wi-Fi antennas, hand trajectory can be traced by exploiting the multi-static Doppler radar model. The Kalman filter (KF) was applied to mitigate the accumulated noise from the recursive process of trajectory estimation. To validate the proposed method, a human limb model was developed to simulate the deterministic movement of a hand gesture by exploiting the non-rigid motion of the robotic arm. The electromagnetic wave scattered from the human limb model was computed using a physical optics (PO) approximation to simulate time-variant CSI. In the experiment, the hand Doppler signature could be successfully extracted from the spectrum with an error of less than 4 Hz at the 90th percentile of the CDF. With the extracted profiles, the trajectories of a square and M-shaped gesture were successfully traced, albeit with moderate trajectory offset of 10-20&#x00B0;. Measurement conducted in a meeting room with commodity Wi-Fi devices installed on laptops also confirmed the tracking capability of the proposed framework.

Analysis of Non-Intrusive Hand Trajectory Tracking by Utilizing Micro-Doppler Signature Obtained From Wi-Fi Channel State Information