Abstract
Compared with traditional methods that employ inertial sensors or wireless sensors, device-free approaches do not require that people carry devices, and they are considered a useful technique for indoor navigation and posture recognition. However, few existing methods can detect the trajectory and movements of humans at the same time. In this study, we propose a scheme called PADAR for addressing these two problems simultaneously by using passive radio frequency identification (RFID) tags but without attaching them to the human body. The idea is based on the principle of radio tomographic imaging, where the variance in a tag’s backscattered radio frequency signal strength is influenced by human movement. We integrated a commodity off-the-shelf RFID reader with a two-dimensional phased array antenna and a matrix of passive tags to evaluate the performance of our scheme. We conducted experiments in a simulated indoor environment. The experimental results showed that PADAR achieved an accuracy of over 70%.
- Federal Standard 1037C. 2006. Definition of Phased Array. Retrieved from http://www.its.bldrdoc.gov/resources/federal-standard-1037c.aspx.Google Scholar
- Fadel Adib, Zachary Kabelac, Dina Katabi, and Robert C. Miller. 2014. 3D tracking via body radio reflections. In Proceedings of the 11th USENIX Conference on Networked Systems Design and Implementation (NSDI’14). USENIX Association, Berkeley, CA, 317--329. Retrieved from http://dl.acm.org/citation.cfm?id=2616448.2616478. Google Scholar
Digital Library
- M. Alghoniemy. 2009. On the equivalence between the MMSE receiver and tikhonov regularization. In Proceedings of the Telecommunications Computer Networks 17th International Conference on Software (SoftCOM’09). 221--224. Google Scholar
Digital Library
- S. Amendola, L. Bianchi, and G. Marrocco. 2015. Movement detection of human body segments: Passive radio-frequency identification and machine-learning technologies. IEEE Anten. Propagat. Mag. 57, 3 (June 2015), 23--37.Google Scholar
- G. Blumrosen, B. Fishman, and Y. Yovel. 2014. Noncontact wideband sonar for human activity detection and classification. IEEE Sensors J. 14, 11 (Nov. 2014), 4043--4054.Google Scholar
Cross Ref
- G. Bradski and A. Kaehler. 2008. Learning OpenCV: Computer Vision with the OpenCV Library (1st. ed.), M. Loukides Editor (Ed.). O'Reilly Media, Sebastopol, CA, 135--140.Google Scholar
- L. Chang, X. Chen, Y. Wang, D. Fang, J. Wang, T. Xing, and Z. Tang. 2016. FitLoc: Fine-grained and low-cost device-free localization for multiple targets over various areas. In Proceedings of the 35th Annual IEEE International Conference on Computer Communications (INFOCOM’16). 1--9.Google Scholar
- H. Ding, J. Han, X. Liu, J. Zhao, et. al. 2015. Human object estimation via backscattered radio frequency signal. In Proceedings of the 34th Annual IEEE International Conference on Computer Communications (INFOCOM’15).Google Scholar
Cross Ref
- R. Eickhoff, F. Ellinger, R. Mosshammer, R. Weigel, A. Ziroff, and M. Huemer. 2008. 3D-Accuracy improvements for TDoA based wireless local positioning systems. In Proceedings of the IEEE Globecom Workshops. 1--6.Google Scholar
- EPCglobal. 2010. Low level reader protocol (llrp). www.llrp.org.Google Scholar
- L. Garcia, L. Lunadei, P. Barreiro, and J. Robla. 2009. A review of wireless sensor technologies and applications in agriculture and food industry: State of the art and current trends. Sensors 9, 6 (June 2009), 4728--4750.Google Scholar
- Avik Ghose, Kingshuk Chakravarty, Amit Kumar Agrawal, and Nasim Ahmed. 2013. Unobtrusive indoor surveillance of patients at home using multiple kinect sensors. In Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems (SenSys’13). ACM, New York, NY. Google Scholar
Digital Library
- Yi Guo, Lei Yang, Bowen Li, Tianci Liu, and Yunhao Liu. 2014. RollCaller: User-friendly indoor navigation system using human-item spatial relation. In Proceedings of the 33rd Annual IEEE International Conference on Computer Communications (INFOCOM’14).Google Scholar
Cross Ref
- Sidhant Gupta, Daniel Morris, Shwetak Patel, and Desney Tan. 2012. SoundWave: Using the doppler effect to sense gestures. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’12). ACM, New York, NY, 1911--1914. Google Scholar
Digital Library
- J. Han, C. Qian, et. al. 2014. Twins: Device-free object tracking using passive tags. Proceedings of the 33rd Annual IEEE International Conference on Computer Communications (INFOCOM’14).Google Scholar
Cross Ref
- C. Hekimian-Williams, B. Grant, Xiuwen Liu, Zhenghao Zhang, and P. Kumar. 2010. Accurate localization of RFID tags using phase difference. In Proceedings of the IEEE International Conference on Radio Frequency Identification (RFID’10). 89--96.Google Scholar
- Y. Gong, T. Huang, H. Ning, and W. Xu. 2008. Discriminative learning of visual words for 3D human pose estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’08).Google Scholar
- D. Huang, N. Rajalakshmi, and G. Shyamnath. 2014. Feasibility and limits of Wi-fi imaging. In Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems (SenSys’14). ACM, New York, NY, 266--279. Google Scholar
Digital Library
- K. Zhao, C. Qian, et al. 2015. EMoD: Efficient motion detection of device-free objects using passive RFID tags. In Proceedings of the IEEE International Conference on Network Protocols (ICNP’15).Google Scholar
Cross Ref
- Q. Lanxin, N. Wistrom, T. Voigt, and H. Zhuang Qin. 2016. 3DinSAR: Object 3D localization for indoor RFID applications. In Proceedings of the IEEE Conference on Radio Frequency Identification (RFID’16).Google Scholar
- Tianxing Li, Chuankai An, Zhao Tian, Andrew T. Campbell, and Xia Zhou. 2015. Human sensing using visible light communication. In Proceedings of the 21st Annual International Conference on Mobile Computing and Networking (MobiCom’15). ACM, New York, NY, 331--344. Google Scholar
Digital Library
- N. Lionel M., L. Yunhao, L. Yiu Cho, and P. Abhishek P. 2004. LANDMARC: Indoor location sensing using active RFID. Wireless Netw. 10 (2004), 701--710. Google Scholar
Digital Library
- Y. Liu, L. Chen, J. Pei, Q. Chen, and Y. Zhao. 2007. Mining frequent trajectory patterns for activity monitoring using radio frequency tag arrays. In Proceedings of the 5th Annual IEEE International Conference on Pervasive Computing and Communications (PerCom’07). 37--46. Google Scholar
Digital Library
- PlayStation. 2017. Retrieved from https://asia.playstation.com/.Google Scholar
- Qifan Pu, Sidhant Gupta, Shyamnath Gollakota, and Shwetak Patel. 2013. Whole-home gesture recognition using wireless signals. In Proceedings of the 19th Annual International Conference on Mobile Computing and Networking (MobiCom’13). ACM, New York, NY, 27--38. Google Scholar
Digital Library
- W. Ruan, Q. Z. Sheng, L. Yao, T. Gu, M. Ruta, and L. Shangguan. 2016. Device-free indoor localization and tracking through human-object interactions. In Proceedings of the IEEE 17th International Symposium on A World of Wireless Mobile and Multimedia Networks (WoWMoM’16). 1--9.Google Scholar
- W. Ruan, L. Yao, Q. Sheng, N. Falkner, and X. Li. 2014. TagTrack: Device-free localization and tracking using passive RFID tags. In Proceedings of the International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MOBIQUITOUS’14). Google Scholar
Digital Library
- Ricky J. Sethi and Amit K. Roy-Chowdhury. 2010. The human action image and its application to motion recognition. In Proceedings of the 7th Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP’10). ACM, New York, NY, 1--8. Google Scholar
Digital Library
- L. Shangguan and K. Jamieson. 2016. The design and implementation of a mobile RFID tag sorting robot. In Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys’16). ACM, New York, NY, 31--42. Google Scholar
Digital Library
- Jamie Shotton, Toby Sharp, Alex Kipman, Andrew Fitzgibbon, Mark Finocchio, Andrew Blake, Mat Cook, and Richard Moore. 2013. Real-time human pose recognition in parts from single depth images. Commun. ACM 56, 1 (Jan. 2013), 116--124. Google Scholar
Digital Library
- VTT. Guidesense. Retrieved from http://www.guidesense.com/.Google Scholar
- B. Wagner, N. Patwari, and D. Timmermann. 2012. Passive RFID tomographic imaging for device-free user localization. In Proceedings of the Workshop on Positioning, Navigation and Communication (WPNC’12).Google Scholar
- B. Wagner, B. Striebing, and D. Timmermann. 2013. A system for live localization in smart environments. In Proceedings of the 10th IEEE International Conference On Networking Sensing and Control (ICNSC’13). 684--689.Google Scholar
- G. Wang, C. Qian, J. Han, et al. 2016. Verfiable smart packaging with passive RFID. In Proceedings of the UBICOMP. Google Scholar
Digital Library
- Jue Wang and Dina Katabi. 2013. Dude, where’s my card?: RFID positioning that works with multipath and non-line of sight. In ACM SIGCOMM Comput. Commun. Rev., Vol. 43. ACM, 51--62. Google Scholar
Digital Library
- Jue Wang, Deepak Vasisht, and Dina Katabi. 2014. RF-IDraw: Virtual touch screen in the air using RF signals. SIGCOMM Comput. Commun. Rev. 44, 4 (Aug. 2014), 235--246. Google Scholar
Digital Library
- W. Wang, A. X. Liu, M. Shahzad, K. Ling, and S. Lu. 2017. Device-free human activity recognition using commercial WiFi devices. IEEE J. Select. Areas Commun. 35, 5 (May 2017), 1118--1131.Google Scholar
Digital Library
- Y. Wei and M. Ng. 2004. Weighted Tikhonov filter matrices for ill-posed problems. Appl. Math. Comput. 149, 2 (Feb. 2004), 411--422. Google Scholar
Digital Library
- J. Wilson and N. Patwari. 2010. Radio tomographic imaging with wireless networks. IEEE Trans. Mobile Comput. 9, 5 (May 2010), 621--632. Google Scholar
Digital Library
- Xbox. 2017. Retrieved from http://www.xbox.com.Google Scholar
- Lei Yang, Yekui Chen, Xiang-Yang Li, Chaowei Xiao, Mo Li, and Yunhao Liu. 2014. Tagoram: Real-time tracking of mobile RFID tags to high precision using COTS devices. In Proceedings of the 20th Annual International Conference on Mobile Computing and Networking. ACM, 237--248. Google Scholar
Digital Library
- Lei Yang, Qiongzheng Lin, Xiangyang Li, Tianci Liu, and Yunhao Liu. 2015. See through walls with COTS RFID system! In Proceedings of the 21st Annual International Conference on Mobile Computing and Networking (MobiCom’15). ACM, New York, NY, 487--499. Google Scholar
Digital Library
- D. Zhang, J. Zhou, M. Guo, J. Cao, and T. Li. 2011. TASA: Tag-free activity sensing using RFID tag arrays. IEEE Trans. Parallel Distrib. Syst. 22, 4 (April 2011), 558--570. Google Scholar
Digital Library
- Yang Zhao, Neal Patwari, Jeff M. Phillips, and Suresh Venkatasubramanian. 2013. Radio tomographic imaging and tracking of stationary and moving people via kernel distance. In Proceedings of the 12th International Conference on Information Processing in Sensor Networks (IPSN’13). ACM, New York, NY, 229--240. Google Scholar
Digital Library
Index Terms
Device-Free Motion & Trajectory Detection via RFID
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