for the target person and one for others.In the training pro- cess,we use the gait instances from 7~9 benchmark persons as training data for the negative class.In real deployment,gait instances of benchmark persons can be drawn from a stand- ard database.The reason for using benchmark persons is that these instances are helpful to determine the decision bound- Distance(meters) ary for the target person and improve the identification accur- Figure 8.Detection ratio vs.operational distances acy.Once the gait model is trained,the classifier can calculate collected 50-60 walking instances for each human subject. the fitness probability that an unknown gait instance belongs With the consent of all human subjects,we also recorded their to the target person.We treat gait instances with fitness prob- heights and genders in our anonymized database. ability higher than a given threshold as instances belonging to the target person.The classifier can identify gait instances Evaluation Metrics for persons that are not seen in the training phase,as their gait We evaluated WifiU from four perspectives:operational dis- features have low fitness probability to the gait model of the tance,effectiveness,robustness,and efficiency.For opera- target person tional distance,we evaluated the minimum distance between To recognize a person in a given set of candidates,we need the walking human subject and the WiFi devices.For accur- one gait model for each person in the candidate set.For acy,we evaluated identification accuracy for single user iden- each person,we train a two-class classifier that can separ- tification and top-k accuracy for multiple user recognition. ate him/her from all the other candidates in the candidate set. We evaluated the identification accuracy in terms of False Ac- Thus,for a candidate set with M persons,we build M one- ceptance Rate (FAR)and False Rejection Rate (FRR).The vs-all classifiers.In the prediction phase,we fit the unknown FAR is defined as the rate that a stranger is wrongly clas- walking sample into the M classifiers and get the probability sified as the target subject and the FRR is the rate that the of fitness.WifiU selects the models with the k highest fitness true target is wrongly classified as a stranger.Since we can probability as the top-k candidates for the testing sample. tradeoff between the FAR and FRR by changing the probabil- ity threshold for identification,we define the Equal Error Rate EXPERIMENTAL RESULTS (EER)point as the point that FAR and FRR are equal.Top-k Data Sets accuracy is defined as the percentage of tested instances in We collected gait patterns from 50 human subject with IRB which WifiU is correct in declaring that the walking human approval.The human subjects were 36 male and 14 female subject is among the top-k candidates.Note that WifiU re- graduate students,with similar ages in the range of 22-25 ports a ranked list of candidates in the decreasing order of years.We conducted our experiments in a typical lab with similarity with the walking human subject.For robustness, an area of 50 square meters.The layout of this lab is plot- we evaluated the identification accuracy of WifiU from two ted in Figure 1(b).The WiFi sender and the WiFi receiver perspectives:(1)effect of evolution of human gait with time were placed on a table with a height of 80 cm and they were and (2)effect of difference in apparel and accessories.For separated by a distance of 1.6 meters.In our experiments, efficiency,we focused on evaluating classification efficiency, we used a NetGear JR6100 WiFi router (of less than 100 which is the time that the classifier takes to make the classi- USD)that supports IEEE 802.11n protocol as the sender and fication decision,and classifier construction efficiency,which a Thinkpad X200 laptop with the Intel 5300 wireless card (of is the time for constructing the classifier. about 10 USD)as the receiver to collect CSI measurements using the Linux CSI tool [11].The wireless router was con- Operational Distance figured to work at 5 GHz band and used a channel bandwidth Our results show that WifiU can detect a walking human sub- of 20 MHz.We chose the 5 GHz band.rather than 2.4 GHz ject at a range as long as 14 meters.Using the movement de- band because the wavelength of 5 GHz is shorter,and shorter tection algorithm described in Section 4.2.we measured the wavelengths give better resolutions in movement speeds.The detection range of our system in a large open lobby area.Fig- settings of the WiFi router,including channel bandwidth,car- ure 8 shows the detection probability for a walking human at rier frequency and data rate settings,were similar to those different distances.We observe that our system is able to de- of our campus network where other WiFi devices coexisted tect a walking human with an accuracy of 92%at a distance of in the same channel.We used the omni-directional antennas 14 m and the accuracy quickly reduces to around 50%at the that come with the router and laptop without any modifica- distance of 16 m.However,the range that our system can re- tions.We used the default transmission power settings of the liably extract gait information is smaller.In our experiments WiFi devices,so there are no potential harms to human sub- we found the operational distance that allows us to perform jects,as our devices fully comply with FCC regulations.We gait recognition is about 6 meters,which is the distance used anonymized collected data to protect the privacy of human in our data collection process. subjects Accuracy The detailed data collection process is as follows.Each sub- To evaluate identification accuracy,we first randomly choose ject was requested to walk repeatedly on a straight line with 7 subjects from the 50 subjects in the database to serve as the distance of 5.5 m.They were asked to walk in their natural benchmark set.For each target subject not in the benchmark way without intentional speed up or slow down.The CSI val- set,we train a SVM classifier that treats the target subject ues were recorded on the laptop and processed offline.We as a class and benchmark subjects as the other class.Thefor the target person and one for others. In the training pro￾cess, we use the gait instances from 7∼9 benchmark persons as training data for the negative class. In real deployment, gait instances of benchmark persons can be drawn from a stand￾ard database. The reason for using benchmark persons is that these instances are helpful to determine the decision bound￾ary for the target person and improve the identification accur￾acy. Once the gait model is trained, the classifier can calculate the fitness probability that an unknown gait instance belongs to the target person. We treat gait instances with fitness prob￾ability higher than a given threshold as instances belonging to the target person. The classifier can identify gait instances for persons that are not seen in the training phase, as their gait features have low fitness probability to the gait model of the target person. To recognize a person in a given set of candidates, we need one gait model for each person in the candidate set. For each person, we train a two-class classifier that can separ￾ate him/her from all the other candidates in the candidate set. Thus, for a candidate set with M persons, we build M one￾vs-all classifiers. In the prediction phase, we fit the unknown walking sample into the M classifiers and get the probability of fitness. WifiU selects the models with the k highest fitness probability as the top-k candidates for the testing sample. EXPERIMENTAL RESULTS Data Sets We collected gait patterns from 50 human subject with IRB approval. The human subjects were 36 male and 14 female graduate students, with similar ages in the range of 22-25 years. We conducted our experiments in a typical lab with an area of 50 square meters. The layout of this lab is plot￾ted in Figure 1(b). The WiFi sender and the WiFi receiver were placed on a table with a height of 80 cm and they were separated by a distance of 1.6 meters. In our experiments, we used a NetGear JR6100 WiFi router (of less than 100 USD) that supports IEEE 802.11n protocol as the sender and a Thinkpad X200 laptop with the Intel 5300 wireless card (of about 10 USD) as the receiver to collect CSI measurements using the Linux CSI tool [11]. The wireless router was con- figured to work at 5 GHz band and used a channel bandwidth of 20 MHz. We chose the 5 GHz band, rather than 2.4 GHz band because the wavelength of 5 GHz is shorter, and shorter wavelengths give better resolutions in movement speeds. The settings of the WiFi router, including channel bandwidth, car￾rier frequency and data rate settings, were similar to those of our campus network where other WiFi devices coexisted in the same channel. We used the omni-directional antennas that come with the router and laptop without any modifica￾tions. We used the default transmission power settings of the WiFi devices, so there are no potential harms to human sub￾jects, as our devices fully comply with FCC regulations. We anonymized collected data to protect the privacy of human subjects. The detailed data collection process is as follows. Each sub￾ject was requested to walk repeatedly on a straight line with distance of 5.5 m. They were asked to walk in their natural way without intentional speed up or slow down. The CSI val￾ues were recorded on the laptop and processed offline. We 2 4 6 8 10 12 14 16 0 0.2 0.4 0.6 0.8 1 Distance (meters) Detection ratio Figure 8. Detection ratio vs. operational distances collected 50–60 walking instances for each human subject. With the consent of all human subjects, we also recorded their heights and genders in our anonymized database. Evaluation Metrics We evaluated WifiU from four perspectives: operational dis￾tance, effectiveness, robustness, and efficiency. For opera￾tional distance, we evaluated the minimum distance between the walking human subject and the WiFi devices. For accur￾acy, we evaluated identification accuracy for single user iden￾tification and top-k accuracy for multiple user recognition. We evaluated the identification accuracy in terms of False Ac￾ceptance Rate (FAR) and False Rejection Rate (FRR). The FAR is defined as the rate that a stranger is wrongly clas￾sified as the target subject and the FRR is the rate that the true target is wrongly classified as a stranger. Since we can tradeoff between the FAR and FRR by changing the probabil￾ity threshold for identification, we define the Equal Error Rate (EER) point as the point that FAR and FRR are equal. Top-k accuracy is defined as the percentage of tested instances in which WifiU is correct in declaring that the walking human subject is among the top-k candidates. Note that WifiU re￾ports a ranked list of candidates in the decreasing order of similarity with the walking human subject. For robustness, we evaluated the identification accuracy of WifiU from two perspectives: (1) effect of evolution of human gait with time and (2) effect of difference in apparel and accessories. For efficiency, we focused on evaluating classification efficiency, which is the time that the classifier takes to make the classi- fication decision, and classifier construction efficiency, which is the time for constructing the classifier. Operational Distance Our results show that WifiU can detect a walking human sub￾ject at a range as long as 14 meters. Using the movement de￾tection algorithm described in Section 4.2, we measured the detection range of our system in a large open lobby area. Fig￾ure 8 shows the detection probability for a walking human at different distances. We observe that our system is able to de￾tect a walking human with an accuracy of 92% at a distance of 14 m and the accuracy quickly reduces to around 50% at the distance of 16 m. However, the range that our system can re￾liably extract gait information is smaller. In our experiments we found the operational distance that allows us to perform gait recognition is about 6 meters, which is the distance used in our data collection process. Accuracy To evaluate identification accuracy, we first randomly choose 7 subjects from the 50 subjects in the database to serve as the benchmark set. For each target subject not in the benchmark set, we train a SVM classifier that treats the target subject as a class and benchmark subjects as the other class. The