20u元 O PCA S PC 30 PCA EER I .0 02 0.05 False Reiection Rate 02 0.2分 (a)Different size of training instances (b)Different size of benchmark sets (c)Different size of PCA components Figure 10.Tradeoff between FAR and FRR ase ReconRate Number of Training insnces mber of2 sining ingance (a)FAR and FRR (b)FRR at FAR=11.1% (a)Training time (b)Classification time Figure 11.Performance evaluation with gait evolution Figure 12.Efficiency of training and classification CPU time (s) Time Std(s)Percentage ing speed.Therefore,the current implementation of WifiU PCA 0.196 0038 713 is only suitable for confined spaces,such as a corridor or Spectrogram 0.072 0.018 26.2% Feature 0.007 0.001 2.5% a narrow entrance.Identifying gaits with no restrictions on Classification 1.7×10-5 4.3×10-6 0.006% the walking path would be an interesting problem for future Total 0.275 0.016 100% research.Second,when there are multiple users walking at Table 2.CPU time to process one second CSI data the same time,the gait patterns captured by WifiU are com- plex mixtures of multiple activities of the users.Although The classification step takes a negligible amount of CPU time static users (such as those that were sitting in the same room because SVM classification with two classes is fast. while we collected data)have no impact to the performance of Our results show that WifiU can train a gait model within 100 WifiU,it is difficult to separate the gait signals from multiple milliseconds when the number of training instances is smal- moving users.In future,we plan to explore the possibility of ler than 800.Figure 12(a)and 12(b)shows that the training using multiple devices to separate gait signals from multiple and classification time of WifiU.increases as the number of users. training instances increases,respectively.The training time CONCLUSIONS increases slowly when the number of training instances in- creases.Curve fitting results show that the training time in- We make the following four key contributions.First,we creases at a speed of O(N4),where N is the number of train- demonstrate the feasibility of using WiFi signals from COTS ing instances.The highly efficient training process enables devices for gait recognition.Second,we propose signal pro- WifiU to continuously retrain the gait model when new meas- cessing techniques to convert raw noisy WiFi signals to high- urements are available.The classification time of WifiU only fidelity spectrograms.Third,we propose methods to extract changes slightly from 0.01 ms to 0.02 ms when the number human gait information and individual specific features from of training instances increases from 100 to 800. spectrograms.Last,we build the WifU system that can re- cognize humans based on the extracted features. LIMITATIONS Acknowledgments WifiU establishes the feasibility of using COTS WiFi devices This work is partially supported by the National Natural Sci- for recognizing humans through their gait patterns.How- ence Foundation of China under Grant Numbers 61373129, ever,our current implementation of WifiU has two limita- 61472184.61321491.and 61472185.the National Science tions.First,the users must walk on a predefined path in Foundation under Grant Numbers CNS-1421407,Collabor- a predefined walking direction,e.g.,walking for 5.5 meters ative Innovation Center of Novel Software Technology and on a straight line as in our experiments.The classification Industrialization,and the Jiangsu High-level Innovation and models trained for a given walking path and direction can- Entrepreneurship(Shuangchuang)Program. not be used for testing samples obtained on different walk- ing paths and directions.This is because Doppler spectro- REFERENCES grams are sensitive to the walking directions.When the user 1.Fadel Adib,Chen-Yu Hsu,Hongzi Mao,Dina Katabi, walks on a different path and/or in a different direction with and Fredo Durand.2015a.Capturing the human figure respect to the WiFi transmitter/receiver,the perceived Dop- through a wall.ACM Transactions on Graphics 34,6 pler frequency changes even if the user retains the same walk- (2015),2190 0.05 0.1 0.15 0.2 0.25 0 0.05 0.1 0.15 0.2 0.25 False Rejection Rate False Acceptance Rate 20 training instances 30 training instances 40 training instances Full training set EER line (a) Different size of training instances 0 0.05 0.1 0.15 0.2 0.25 0 0.05 0.1 0.15 0.2 0.25 False Rejection Rate False Acceptance Rate 3 benchmark 5 benchmark 7 benchmark 9 benchmark 11 benchmark EER line (b) Different size of benchmark sets 0 0.05 0.1 0.15 0.2 0.25 0 0.05 0.1 0.15 0.2 0.25 False Rejection Rate False Acceptance Rate 5 PCA components 10 PCA components 15 PCA components 20 PCA components 25 PCA components 30 PCA components EER line (c) Different size of PCA components Figure 10. Tradeoff between FAR and FRR 0 0.05 0.1 0.15 0.2 0.25 0 0.05 0.1 0.15 0.2 0.25 False Rejection Rate False Acceptance Rate EER Line (a) FAR and FRR Spring Winter Suits Briefcase 0 0.1 0.2 0.3 0.4 False Rejection Rate (b) FRR at FAR=11.1% Figure 11. Performance evaluation with gait evolution CPU time (s) Time Std (s) Percentage PCA 0.196 0.038 71.3% Spectrogram 0.072 0.018 26.2% Feature 0.007 0.001 2.5% Classification 1.7×10−5 4.3×10−6 0.006% Total 0.275 0.016 100% Table 2. CPU time to process one second CSI data The classification step takes a negligible amount of CPU time because SVM classification with two classes is fast. Our results show that WifiU can train a gait model within 100 milliseconds when the number of training instances is smaller than 800. Figure 12(a) and 12(b) shows that the training and classification time of WifiU, increases as the number of training instances increases, respectively. The training time increases slowly when the number of training instances increases. Curve fitting results show that the training time increases at a speed of O(N 1.4 ), where N is the number of training instances. The highly efficient training process enables WifiU to continuously retrain the gait model when new measurements are available. The classification time of WifiU only changes slightly from 0.01 ms to 0.02 ms when the number of training instances increases from 100 to 800. LIMITATIONS WifiU establishes the feasibility of using COTS WiFi devices for recognizing humans through their gait patterns. However, our current implementation of WifiU has two limitations. First, the users must walk on a predefined path in a predefined walking direction, e.g., walking for 5.5 meters on a straight line as in our experiments. The classification models trained for a given walking path and direction cannot be used for testing samples obtained on different walking paths and directions. This is because Doppler spectrograms are sensitive to the walking directions. When the user walks on a different path and/or in a different direction with respect to the WiFi transmitter/receiver, the perceived Doppler frequency changes even if the user retains the same walk- 200 400 600 800 0 20 40 60 80 100 Number of Training instances Training time (ms) (a) Training time 200 400 600 800 0 0.005 0.01 0.015 0.02 Number of Training instances Classification time (ms) (b) Classification time Figure 12. Efficiency of training and classification ing speed. Therefore, the current implementation of WifiU is only suitable for confined spaces, such as a corridor or a narrow entrance. Identifying gaits with no restrictions on the walking path would be an interesting problem for future research. Second, when there are multiple users walking at the same time, the gait patterns captured by WifiU are complex mixtures of multiple activities of the users. Although static users (such as those that were sitting in the same room while we collected data) have no impact to the performance of WifiU, it is difficult to separate the gait signals from multiple moving users. In future, we plan to explore the possibility of using multiple devices to separate gait signals from multiple users. CONCLUSIONS We make the following four key contributions. First, we demonstrate the feasibility of using WiFi signals from COTS devices for gait recognition. Second, we propose signal processing techniques to convert raw noisy WiFi signals to high- fidelity spectrograms. Third, we propose methods to extract human gait information and individual specific features from spectrograms. Last, we build the WifiU system that can recognize humans based on the extracted features. Acknowledgments This work is partially supported by the National Natural Science Foundation of China under Grant Numbers 61373129, 61472184, 61321491, and 61472185, the National Science Foundation under Grant Numbers CNS-1421407, Collaborative Innovation Center of Novel Software Technology and Industrialization, and the Jiangsu High-level Innovation and Entrepreneurship (Shuangchuang) Program. REFERENCES 1. Fadel Adib, Chen-Yu Hsu, Hongzi Mao, Dina Katabi, and Frédo Durand. 2015a. Capturing the human figure through a wall. ACM Transactions on Graphics 34, 6 (2015), 219