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

Abstract

Envision a ubiquitously device-free motion sensing, this work focuses on the analysis of Wi-Fi-based hand gesture trajectory tracking by utilizing Doppler frequency obtained from channel state information (CSI). Since a movement of human limb generates variant Micro-Doppler signatures caused by different parts on hand and arm surfaces to the spectrum, the 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 antenna, hand trajectory can be traced by exploiting the multi-static Doppler radar model. Kalman filter (KF) was applied to mitigate the accumulated noise from the recursive process of trajectory estimation. For the validation of the proposed method, the human limb model was developed to simulate a deterministic movement of hand gesture by exploiting the non-rigid motion of the robotic arm. The electromagnetic wave scattered from the human limb model was computed using physical optics (PO) approximation to simulate time-variant CSI. Through the experiment, hand Doppler signature could be successfully extracted from the spectrum with the error less than 4-Hz at 90 th percentile of CDF. With the extracted profiles, the trajectory of a square and M-shape gesture were successfully traced albeit a moderate trajectory offset of 10-20 degrees. The measurement conducted in the meeting room with commodity Wi-Fi devices installed on laptops also confirmed the tracking capability of the proposal. INDEX TERMS channel state information, micro-Doppler, motion sensing, non-rigid motion, trajectory tracking, Wi-Fi.

Eliminating the Barriers: Demystifying Wi-Fi Baseband Design and Introducing the PicoScenes Wi-Fi Sensing Platform

“…Model-based Wi-Fi sensing [18] has made remarkable advancement after 10 years of development, and has led to many new research areas of fine-grained sensing, including indoor localization [19], [20], [21], [22], trajectory tracking [23], [24], [25], material identification [26], hand gesture recognition [27], inertial measurements [28], etc. mDTrack [22] proposes a joint approach to estimate angle of arrival (AoA), time of flight (ToF) and Doppler effect simultaneously, achieving the decimeter-level resolution for indoor localization.…”

“…mDTrack [22] proposes a joint approach to estimate angle of arrival (AoA), time of flight (ToF) and Doppler effect simultaneously, achieving the decimeter-level resolution for indoor localization. Nopphon et al [23] extract Doppler frequency from CSI, and use it to track the hand trajectory with centimeter-level accuracy. WiMi [26] proposes a sophisticated system that can identify the type of materials regardless of its motion state.…”

Passive Indoor Tracking Fusion Algorithm Using Commodity Wi-Fi

“…The essence of the Wi-Fi-based trajectory tracking system is to collect and analyse the CSI (Channel State Information) to obtain the attributes of the wireless link channel. Then, the motion trajectory and position information of the human body is analysed [1]. At present, there are two mainstream ideas for CSI positioning [2,3]: (1) Establishing a location fingerprint database for matching positioning.…”