Abstract: In this work, for the first time, we explore the direction of using WiFi signals to profile human gait patterns. We propose WifiU, which uses commercial WiFi devices to capture fine-grained gait patterns so that we can recognize humans. The intuition is that with a human walking around, the WiFi signal reflected by the human body generates certain unique variations in the Channel State Information (CSI) measurements on the WiFi receiver. These variations in CSI allow us to use signal processing techniques to obtain gait information that can be used to non-intrusively recognize the walking individual as humans have unique gait patterns. Our novelty lies in both the goal of using WiFi signals for gait recognition and the way of using WiFi signals to obtain gait patterns. Although some prior work has used WiFi signals for human activity recognition, the information captured from human activities is not fine-grained enough to recognize gait patterns. To profile human movement using CSI dynamics, we use signal processing techniques to generate spectrograms from CSI measurements so that the resulting spectrograms are similar to those generated by expensive Doppler radars. To extract features from spectrograms that best characterize the walking pattern of a human, we perform autocorrelation on the contours of the torso reflection to remove small imperfection in spectrograms. Experimental results show that WifiU achieves top-1, top-2, and top-3 recognition accuracies of 92.31%, 97.58%, and 98.86%, respectively.
Bio: Alex X. Liu is a Professor at Michigan State University. He obtained his Ph.D. degree in Computer Science from The University of Texas at Austin in 2006. He received the IEEE & IFIP William C. Carter Award in 2004, the National Science Foundation CAREER Award in 2009, and the Michigan State University Withrow Distinguished Scholar Award in 2011. His special research interests are in networking, security, and privacy. His general research interests include computer systems, distributed computing, and dependable systems.