ware that we experimented with in this work,namely Net- the scenario where a burglar attempts to know who is at home Gear JR6100 WiFi router and ThinkPad X200 laptop (with by eavesdropping the WiFi signal emitted by the WiFi router Intel 5300 WiFi NIC),are COTS devices and we do not re- in the victim's house.As WiFi signals can penetrate through quire any hardware modification to these devices.Further- obstacles such as furniture,wooden doors,and walls [3].the more,unlike cameras,WifiU does not require lighting and burglar only needs to passively measure the CSI of the signal works in dark just as well as in light outside the house without decoding the content of the WiFi packets.Therefore,it is hard for the victim to prevent privacy Technical Challenges and Solutions breaches.While protecting privacy breach is not the focus of The first challenge is to profile gait patterns using CSI dy- this paper,we hope that this work will bring this privacy issue namics.Extracting gait information from CSI signals is dif- to the research community and inspire future work. ficult because the signal reflections of different body parts are mixed together in the CSI waveform.As different hu- RELATED WORK man body parts move at different speeds while walking,the Gait Based Human Recognition radio signal reflections from different body parts have differ- Prior gait based human recognition schemes mostly use in- ent frequencies.To separate the radio signal reflections from formation collected from the following three types of sensors: different body parts,we convert the CSI waveforms (of two cameras.floor sensors.and wearable sensors [81.Camera dimensions:time and amplitude)to spectrograms in the time- based schemes first generate a sequence of human silhouettes frequency domain (of three dimensions:time,frequency,and from the video frames by separating the moving objects from amplitude).We apply spectrogram enhancement techniques the background,and then use the sequence of human silhou- to reduce signal noises.The resulting spectrograms give us ettes to perform recognition [17].An example camera based detailed human gait information similar to those generated scheme achieves an accuracy of 91%on the USF HumanID by Doppler radars [22.271. Database with 122 subjects [291.Floor sensor based schemes The second challenge is to extract features from spectrograms use force sensors under the floor to detect human footsteps that best characterize the walking pattern of a human.There [18.An example floor sensor based scheme achieves an ac- are various metrics (such as walking speed and gait cycle curacy of 90%~97.5%for identification a single target per- time)that can be used to characterize human walking pat- son [281.Wearable sensor based schemes use the acceler- terns.However,obtaining these measurements from a spec- ometers equipped in smart phones and wearable devices to trogram is challenging because we need to precisely measure collect gait information and extract features for human re- the time of gait cycles.For example,to measure gait cycle cognition [6,19.An example wearable sensor based scheme time,i.e.,the time between two consecutive instances for the achieves an accuracy of 90%over 6 human subjects 24. right heel hits the ground [33],we need to determine the time that the right heel hitting the ground,which is non-trivial.To Activity and Gait Recognition using Radar Signals As a human body can reflect wireless signals,human move- accurately measure gait cycle time,we perform autocorrela- ment can be analyzed using the Doppler shift of the re- tion on the contours of the torso reflection to remove small imperfection in spectrograms.We also generate a spectro- flected signal or the time-of-flight measurements obtained gram signature to distinguish persons with similar walking through Frequency-Modulated Continuous-Wave (FMCW) radars.The micro-Doppler signature,which describes the ve- speeds and footstep lengths. locity of different body parts,can be used for human activity Summary of Experimental Results recognition [16,21,22].Using the Boulic walking model,it is We implemented WifiU on COTS laptop and WiFi router. possible to estimate parameters from the micro-Doppler sig- We conducted experiments on our gait database that contains nature and use these parameters to rebuild the animation of more than 2,800 gait instances collected from 50 human sub- the walking process [27].Tahmoush et al.built a radar based jects walking in a typical laboratory with an area of 50 square gait recognition system that uses the stride rate as features meters as shown in Figure 1(b).We anonymized all collected and achieve recognition accuracy of 80%over 8 human sub- data to protect privacy.Over the 50 subjects,WifiU achieves jects [25].Adib et al.used FMCW Radar to capture a human recognition accuracies of 79.28%,89.52%,and 93.05%for figure behind the wall and to identify the user with an accur- top-1,top-2,and top-3 candidates,respectively acy of 88%over 15 human subjects [1].Compared to radar signals,WiFi signals are much easier and cheaper to obtain, Limitations and Privacy Implications but have much narrower bandwidth(20 MHz compared to The current implementation of WifiU has two limitations. 1.79 GHz of the FMCW radar used in WiTrack [21)and thus First,the recognition system is suitable only for confined have much lower time-resolutions.This makes human gait spaces,such as a corridor or a narrow entrance,because the recognition using WiFi signals much harder than using radar users must walk on a predefined path in a predefined direc- signals.Consequently,the techniques in this paper are funda- tion.Second,WifiU can extract the gait pattern only when mentally different from those used in FMCW radars there is a single user walking on the predefined path.Our future work is to explore the possibility of using multiple Activity Recognition using WiFi Signals devices to separate gait signals from multiple users. CSI measurements have been used for applications such as human activity recognition [31,32],fall detection [12],and From the perspective of privacy,hackers can potentially use even object localization [23].E-eyes utilized CSI histogram WifiU to identify"victims"without being detected.Consider to achieve in-place and walking activity recognition with anware that we experimented with in this work, namely Net￾Gear JR6100 WiFi router and ThinkPad X200 laptop (with Intel 5300 WiFi NIC), are COTS devices and we do not re￾quire any hardware modification to these devices. Further￾more, unlike cameras, WifiU does not require lighting and works in dark just as well as in light. Technical Challenges and Solutions The first challenge is to profile gait patterns using CSI dy￾namics. Extracting gait information from CSI signals is dif- ficult because the signal reflections of different body parts are mixed together in the CSI waveform. As different hu￾man body parts move at different speeds while walking, the radio signal reflections from different body parts have differ￾ent frequencies. To separate the radio signal reflections from different body parts, we convert the CSI waveforms (of two dimensions: time and amplitude) to spectrograms in the time￾frequency domain (of three dimensions: time, frequency, and amplitude). We apply spectrogram enhancement techniques to reduce signal noises. The resulting spectrograms give us detailed human gait information similar to those generated by Doppler radars [22, 27]. The second challenge is to extract features from spectrograms that best characterize the walking pattern of a human. There are various metrics (such as walking speed and gait cycle time) that can be used to characterize human walking pat￾terns. However, obtaining these measurements from a spec￾trogram is challenging because we need to precisely measure the time of gait cycles. For example, to measure gait cycle time, i.e., the time between two consecutive instances for the right heel hits the ground [33], we need to determine the time that the right heel hitting the ground, which is non-trivial. To accurately measure gait cycle time, we perform autocorrela￾tion on the contours of the torso reflection to remove small imperfection in spectrograms. We also generate a spectro￾gram signature to distinguish persons with similar walking speeds and footstep lengths. Summary of Experimental Results We implemented WifiU on COTS laptop and WiFi router. We conducted experiments on our gait database that contains more than 2,800 gait instances collected from 50 human sub￾jects walking in a typical laboratory with an area of 50 square meters as shown in Figure 1(b). We anonymized all collected data to protect privacy. Over the 50 subjects, WifiU achieves recognition accuracies of 79.28%, 89.52%, and 93.05% for top-1, top-2, and top-3 candidates, respectively. Limitations and Privacy Implications The current implementation of WifiU has two limitations. First, the recognition system is suitable only for confined spaces, such as a corridor or a narrow entrance, because the users must walk on a predefined path in a predefined direc￾tion. Second, WifiU can extract the gait pattern only when there is a single user walking on the predefined path. Our future work is to explore the possibility of using multiple devices to separate gait signals from multiple users. From the perspective of privacy, hackers can potentially use WifiU to identify “victims” without being detected. Consider the scenario where a burglar attempts to know who is at home by eavesdropping the WiFi signal emitted by the WiFi router in the victim’s house. As WiFi signals can penetrate through obstacles such as furniture, wooden doors, and walls [3], the burglar only needs to passively measure the CSI of the signal outside the house without decoding the content of the WiFi packets. Therefore, it is hard for the victim to prevent privacy breaches. While protecting privacy breach is not the focus of this paper, we hope that this work will bring this privacy issue to the research community and inspire future work. RELATED WORK Gait Based Human Recognition Prior gait based human recognition schemes mostly use in￾formation collected from the following three types of sensors: cameras, floor sensors, and wearable sensors [8]. Camera based schemes first generate a sequence of human silhouettes from the video frames by separating the moving objects from the background, and then use the sequence of human silhou￾ettes to perform recognition [17]. An example camera based scheme achieves an accuracy of 91% on the USF HumanID Database with 122 subjects [29]. Floor sensor based schemes use force sensors under the floor to detect human footsteps [18]. An example floor sensor based scheme achieves an ac￾curacy of 90%∼97.5% for identification a single target per￾son [28]. Wearable sensor based schemes use the acceler￾ometers equipped in smart phones and wearable devices to collect gait information and extract features for human re￾cognition [6, 19]. An example wearable sensor based scheme achieves an accuracy of 90% over 6 human subjects [24]. Activity and Gait Recognition using Radar Signals As a human body can reflect wireless signals, human move￾ment can be analyzed using the Doppler shift of the re- flected signal or the time-of-flight measurements obtained through Frequency-Modulated Continuous-Wave (FMCW) radars. The micro-Doppler signature, which describes the ve￾locity of different body parts, can be used for human activity recognition [16,21,22]. Using the Boulic walking model, it is possible to estimate parameters from the micro-Doppler sig￾nature and use these parameters to rebuild the animation of the walking process [27]. Tahmoush et al. built a radar based gait recognition system that uses the stride rate as features and achieve recognition accuracy of 80% over 8 human sub￾jects [25]. Adib et al. used FMCW Radar to capture a human figure behind the wall and to identify the user with an accur￾acy of 88% over 15 human subjects [1]. Compared to radar signals, WiFi signals are much easier and cheaper to obtain, but have much narrower bandwidth (20 MHz compared to 1.79 GHz of the FMCW radar used in WiTrack [2]) and thus have much lower time-resolutions. This makes human gait recognition using WiFi signals much harder than using radar signals. Consequently, the techniques in this paper are funda￾mentally different from those used in FMCW radars. Activity Recognition using WiFi Signals CSI measurements have been used for applications such as human activity recognition [31, 32], fall detection [12], and even object localization [23]. E-eyes utilized CSI histogram to achieve in-place and walking activity recognition with an