Gait Recognition Using WiFi Signals Wei Wang Alex X.Liu Muhammad Shahzads State Key Laboratory for Novel Software Technology,Nanjing University,China Department of Computer Science and Engineering,Michigan State University,USA s Department of Computer Science,North Carolina State University,USA Email:ww@cs.nju.edu.cn,alexliu @cse.msu.edu,mshahza@ncsu.edu Abstract In this paper,we propose WifiU,which uses commercial WiFi devices to capture fine-grained gait patterns to recognize hu- mans.The intuition is that due to the differences in gaits of different people,the WiFi signal reflected by a walking hu- man generates unique variations in the Channel State Inform- ation(CSD)on the WiFi receiver.To profile human movement using CSI,we use signal processing techniques to generate spectrograms from CSI measurements so that the resulting 7.7m spectrograms are similar to those generated by specifically (a)Application scenario (b)Data collection environment designed Doppler radars.To extract features from spectro- grams that best characterize the walking pattern,we perform Figure 1.WifiU system overview autocorrelation on the torso reflection to remove imperfection in spectrograms.We evaluated WifiU on a dataset with 2,800 be a laptop.Figure 1 shows an overview of our WifiU system. gait instances collected from 50 human subjects walking in a In WifiU,the specific information that the receiver measures room with an area of 50 square meters.Experimental results is the Channel State Information(CSD)of each received WiFi show that WifU achieves top-1,top-2,and top-3 recognition frame.With a human walking around.as a human is mostly accuracies of 79.28%,89.52%,and 93.05%,respectively. made of water,the WiFi signal reflected by the human body generates unique,although small,variations in the CSI meas- ACM Classification Keywords urements on the receiver due to the well-known multi-path H.1.2 User/Machine Systems;I.5.Pattern Recognition effect of wireless signals.These variations in CSI allow us to use signal processing techniques to obtain gait information Author Keywords such as walking speed,gait cycle time,footstep length,and Gait Recognition;Device-free Sensing. movement speeds of legs and torso.As it is well known that humans have quite unique gait [8,15].the gait patterns that INTRODUCTION the WiFi receiver obtains can be used to recognize the walk- ing human subject.Fundamentally,WifiU recognizes humans Motivation and Proposed Approach based on who they are because WifiU extracts unique bio- With the success of WiFi industry,commercially available metrics information from WiFi signals and uses it to perform WiFi devices can not only achieve high speed and low cost, human recognition. but can also measure small changes in WiFi signals and thus sense the changes in their surrounding environments caused WifiU enables many potential applications that require user by moving objects such as humans [30,31].In this paper, identification.For example,a smart building can recognize we propose WifiU,which uses Commercial Off-The-Shelf the user using WifiU,while the user is walking along the cor- (COTS)WiFi devices to capture fine-grained gait patterns so ridor.Based on the user identity,it can automatically open the that we can recognize humans.WifiU consists of two WiFi door when he/she approaches his/her office.Similarly,smart devices,one for continuously sending signals,which can be a home applications can use WifiU to adjust background music router,and one for continuously receiving signals,which can or ambient temperature based on who is at home. Compared with traditional gait recognition systems,which Permission to make digital or hard copies of all or part of this work for personal or use cameras [17,floor sensors [18],or wearable sensors [8 to capture gait information,WifiU is easier to deploy and has tion on the first page.Copyrights for components of this work owned by others than better coverages.From the deployment perspective,WifiU ACM must be honored.Abstracting with credit is permitted.To copy otherwise,or re publish,to post on servers or to redistribute to lists,requires prior specific permission does not require any special hardware(such as floor sensors) and/or a fee.Request permissions from permissions@acm.org. and the human subject does not require to wear any hardware UbiComp '16,September 12-16.2016.Heidelberg.Germany (such as accelerometers).WiFi devices are ubiquitous and ©2016AC3M.ISBN978-1-4503-4461-6/1609.s15.00 most homes/offices are covered by WiFi signals.The hard- D0L:http:/dx.doi.org/10.1145/2971648.2971670Gait Recognition Using WiFi Signals Wei Wang† Alex X. Liu†‡ Muhammad Shahzad§ †State Key Laboratory for Novel Software Technology, Nanjing University, China ‡Department of Computer Science and Engineering, Michigan State University, USA §Department of Computer Science, North Carolina State University, USA Email: ww@cs.nju.edu.cn, alexliu@cse.msu.edu, mshahza@ncsu.edu Abstract In this paper, we propose WifiU, which uses commercial WiFi devices to capture fine-grained gait patterns to recognize hu￾mans. The intuition is that due to the differences in gaits of different people, the WiFi signal reflected by a walking hu￾man generates unique variations in the Channel State Inform￾ation (CSI) on the WiFi receiver. To profile human movement using CSI, we use signal processing techniques to generate spectrograms from CSI measurements so that the resulting spectrograms are similar to those generated by specifically designed Doppler radars. To extract features from spectro￾grams that best characterize the walking pattern, we perform autocorrelation on the torso reflection to remove imperfection in spectrograms. We evaluated WifiU on a dataset with 2,800 gait instances collected from 50 human subjects walking in a room with an area of 50 square meters. Experimental results show that WifiU achieves top-1, top-2, and top-3 recognition accuracies of 79.28%, 89.52%, and 93.05%, respectively. ACM Classification Keywords H.1.2 User/Machine Systems; I.5. Pattern Recognition Author Keywords Gait Recognition; Device-free Sensing. INTRODUCTION Motivation and Proposed Approach With the success of WiFi industry, commercially available WiFi devices can not only achieve high speed and low cost, but can also measure small changes in WiFi signals and thus sense the changes in their surrounding environments caused by moving objects such as humans [30, 31]. In this paper, we propose WifiU, which uses Commercial Off-The-Shelf (COTS) WiFi devices to capture fine-grained gait patterns so that we can recognize humans. WifiU consists of two WiFi devices, one for continuously sending signals, which can be a router, and one for continuously receiving signals, which can Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full cita￾tion on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or re￾publish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. UbiComp ’16, September 12-16, 2016, Heidelberg, Germany © 2016 ACM. ISBN 978-1-4503-4461-6/16/09. . . $15.00 DOI: http://dx.doi.org/10.1145/2971648.2971670 Signal analysis WiFi router WiFi signal reflection Laptop WiFi signal (a) Application scenario Table Table Walking route (5.5m) 7.7 m 6.5 m 1.6 m sender receiver (b) Data collection environment Figure 1. WifiU system overview be a laptop. Figure 1 shows an overview of our WifiU system. In WifiU, the specific information that the receiver measures is the Channel State Information (CSI) of each received WiFi frame. With a human walking around, as a human is mostly made of water, the WiFi signal reflected by the human body generates unique, although small, variations in the CSI meas￾urements on the receiver due to the well-known multi-path effect of wireless signals. These variations in CSI allow us to use signal processing techniques to obtain gait information such as walking speed, gait cycle time, footstep length, and movement speeds of legs and torso. As it is well known that humans have quite unique gait [8, 15], the gait patterns that the WiFi receiver obtains can be used to recognize the walk￾ing human subject. Fundamentally, WifiU recognizes humans based on who they are because WifiU extracts unique bio￾metrics information from WiFi signals and uses it to perform human recognition. WifiU enables many potential applications that require user identification. For example, a smart building can recognize the user using WifiU, while the user is walking along the cor￾ridor. Based on the user identity, it can automatically open the door when he/she approaches his/her office. Similarly, smart home applications can use WifiU to adjust background music or ambient temperature based on who is at home. Compared with traditional gait recognition systems, which use cameras [17], floor sensors [18], or wearable sensors [8] to capture gait information, WifiU is easier to deploy and has better coverages. From the deployment perspective, WifiU does not require any special hardware (such as floor sensors) and the human subject does not require to wear any hardware (such as accelerometers). WiFi devices are ubiquitous and most homes/offices are covered by WiFi signals. The hard-