ABSTRACT
In this paper, we propose WifiU, which uses commercial WiFi devices to capture fine-grained gait patterns to recognize humans. The intuition is that due to the differences in gaits of different people, the WiFi signal reflected by a walking human generates unique variations in the Channel State Information (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 spectrograms 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.
- 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. Google ScholarDigital Library
- Fadel Adib, Zachary Kabelac, and Dina Katabi. 2015b. Multi-Person Localization via RF Body Reflections. In Proc. USENIX NSDI. Google ScholarDigital Library
- Fadel Adib and Dina Katabi. 2013. See through walls with WiFi!. In Proc. ACM SIGCOMM. Google ScholarDigital Library
- Arijit Banerjee, Dustin Maas, Maurizio Bocca, Neal Patwari, and Sneha Kasera. 2014. Violating privacy through walls by passive monitoring of radio windows. In Proc. ACM WiSec. Google ScholarDigital Library
- Chih-Chung Chang and Chih-Jen Lin. 2011. LIBSVM: A library for support vector machines. ACM Trans. Intelligent Systems and Technology (2011). Google ScholarDigital Library
- Mohammad Omar Derawi. 2010. Accelerometer-based gait analysis, a survey. In Norwegian Information Security Conference.Google Scholar
- Yariv Ephraim and David Malah. 1984. Speech enhancement using a minimum-mean square error short-time spectral amplitude estimator. IEEE Trans. Acoustics, Speech and Signal Processing 32, 6 (1984), 1109--1121.Google ScholarCross Ref
- Davrondzhon Gafurov. 2007. A survey of biometric gait recognition: Approaches, security and challenges. In Annual Norwegian Computer Science Conference.Google Scholar
- Davrondzhon Gafurov, Kirsi Helkala, and Torkjel Søndrol. 2006. Gait recognition using acceleration from MEMS. In IEEE ARES. Google ScholarDigital Library
- Jon Gjengset, Jie Xiong, Graeme McPhillips, and Kyle Jamieson. 2014. Phaser: Enabling Phased Array Signal Processing on Commodity WiFi Access Points. In Proc. ACM MobiCom. Google ScholarDigital Library
- Daniel Halperin, Wenjun Hu, Anmol Sheth, and David Wetherall. 2011. Tool Release: Gathering 802.11n Traces with Channel State Information. ACM SIGCOMM CCR 41, 1 (2011), 53. Google ScholarDigital Library
- Chunmei Han, Kaishun Wu, Yuxi Wang, and Lionel M Ni. 2014. WiFall: Device-free fall detection by wireless networks. In Proc. IEEE INFOCOM.Google ScholarCross Ref
- Donny Huang, Rajalakshmi Nandakumar, and Shyamnath Gollakota. 2014. Feasibility and limits of Wi-Fi imaging. In Proc. ACM Sensys. Google ScholarDigital Library
- IEEE. 2009. Enhancements for higher throughput. IEEE Standard 802.11n. (2009).Google Scholar
- Anil K Jain, Patrick Flynn, and Arun A Ross. 2007. Handbook of biometrics. Springer. Google ScholarDigital Library
- Youngwook Kim and Hao Ling. 2009. Human activity classification based on micro-Doppler signatures using a support vector machine. IEEE Trans. Geoscience and Remote Sensing 47, 5 (2009), 1328--1337.Google ScholarCross Ref
- Mark S Nixon and John N Carter. 2006. Automatic recognition by gait. Proc. the IEEE 94, 11 (2006), 2013--2024.Google ScholarCross Ref
- Robert J Orr and Gregory D Abowd. 2000. The smart floor: a mechanism for natural user identification and tracking. In Proc. ACM CHI. Google ScholarDigital Library
- Abena Primo, Vir V Phoha, Rajesh Kumar, and Abdul Serwadda. 2014. Context-Aware Active Authentication Using Smartphone Accelerometer Measurements. In Proc. IEEE CVPRW. 98--105. Google ScholarDigital Library
- Qifan Pu, Sidhant Gupta, Shyamnath Gollakota, and Shwetak Patel. 2013. Whole-home gesture recognition using wireless signals. In Proc. ACM MobiCom. 27--38. Google ScholarDigital Library
- RG Raj, VC Chen, and R Lipps. 2010. Analysis of radar human gait signatures. IET Signal Processing 4, 3 (2010), 234--244.Google ScholarCross Ref
- Shobha Sundar Ram, Craig Christianson, Youngwook Kim, and Hao Ling. 2010. Simulation and analysis of human micro-dopplers in through-wall environments. IEEE Trans. Geoscience and Remote Sensing 48, 4 (2010), 2015--2023.Google ScholarCross Ref
- Souvik Sen, Božidar Radunovic, Romit Roy Choudhury, and Tom Minka. 2012. You are facing the mona lisa: spot localization using phy layer information. In Proc. ACM MobiSys. Google ScholarDigital Library
- Sebastijan Sprager and Damjan Zazula. 2009. A cumulant-based method for gait identification using accelerometer data with principal component analysis and support vector machine. WSEAS Trans. Signal Processing 5, 11 (2009), 369--378. Google ScholarDigital Library
- Dave Tahmoush and Jerry Silvious. 2009. Radar micro-doppler for long range front-view gait recognition. In Proc. IEEE BTAS. Google ScholarDigital Library
- David Tse and Pramod Viswanath. 2005. Fundamentals of wireless communication. Cambridge university press. Google Scholar
- P Van Dorp and FCA Groen. 2008. Feature-based human motion parameter estimation with radar. IET Radar, Sonar & Navigation 2, 2 (2008), 135--145.Google ScholarCross Ref
- Ruben Vera-Rodriguez, John SD Mason, Julian Fierrez, and Javier Ortega-Garcia. 2013. Comparative analysis and fusion of spatiotemporal information for footstep recognition. IEEE Trans. Pattern Analysis and Machine Intelligence 35, 4 (2013), 823--834. Google ScholarDigital Library
- Chen Wang, Junping Zhang, Liang Wang, Jian Pu, and Xiaoru Yuan. 2012. Human identification using temporal information preserving gait template. IEEE Trans. Pattern Analysis and Machine Intelligence 34, 11 (2012), 2164--2176. Google ScholarDigital Library
- Guanhua Wang, Yongpan Zou, Zimu Zhou, Kaishun Wu, and Lionel M. Ni. 2014. We Can Hear You with Wi-Fi!. In Proc. ACM MobiCom. Google ScholarDigital Library
- Wei Wang, Alex X. Liu, Muhammad Shahzad, Kang Ling, and Sanglu Lu. 2015. Understanding and Modeling of WiFi Signal Based Human Activity Recognition. In Proc. ACM MobiCom. Google ScholarDigital Library
- Yan Wang, Jian Liu, Yingying Chen, Marco Gruteser, Jie Yang, and Hongbo Liu. 2014. E-eyes: In-home Device-free Activity Identification Using Fine-grained WiFi Signatures. In Proc. ACM MobiCom. Google ScholarDigital Library
- Michael W Whittle. 2007. Gait analysis: an introduction. Butterworth-Heinemann.Google Scholar
- Yunze Zeng, Parth H Pathak, and Prasant Mohapatra. 2016. WiWho: WiFi-based Person Identification in Smart Spaces. In Proc. IEEE/ACM IPSN. Google ScholarDigital Library
Index Terms
- Gait recognition using wifi signals
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