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FreeSense: human-behavior understanding using Wi-Fi signals

  • Tong Xin
  • Bin Guo
  • Zhu Wang
  • Pei Wang
  • Zhiwen Yu
Original Research

Abstract

Device-free passive human behavior understanding plays an important role in human–computer interaction and public safety management. Especially, human detection and human identification are two key enablers for a wide range of indoor location-based services such as asset security, emergency response and personalized service. In this paper, we proposed a new method for human detection with high robustness and a novel approach for indoor human identification based on Wi-Fi Channel State Information (CSI) signals. The former utilizes the phenomenon that when a person moves, phase differences will appear between the waveforms of different receiving antennas. It can be used to deal with the effect of multipath and noises. The latter is based on the observation that each person has specific influence patterns to the surrounding Wi-Fi signals while moving, regarding their body shape characteristics and motion patterns. We use a combination of Principal Component Analysis (PCA), Discrete Wavelet Transform (DWT) and Dynamic Time Warping (DTW) techniques to capture this specific influence patterns. We implemented our human detection method in two typical indoor environments (i.e., a meeting room and a bedroom) and the results demonstrate an average false positive (FP) of 0.58% and an average false negative (FN) of 1.20%. We also implemented our human identification system in a home environment and recruited 9 users for data collection and evaluation. Experimental results indicate that the identification accuracy is about 88.9–94.5% as the size of the candidate user set changes from 6 to 2, showing that the proposed method is effective in domestic environments.

Keywords

Wi-Fi sensing Channel state information Human detection Human identification 

Notes

Acknowledgements

This work was partially supported by the National Key R&D Program of China (2017YFB1001800), the National Natural Science Foundation of China (No. 61772428).

References

  1. Adib F, Katabi D (2013) See through walls with WiFi!. In: Proceedings of ACM SIGCOMM 2013 conference on SIGCOMM. ACM, pp 75–86Google Scholar
  2. Ali K, Liu AX, Wang W, Shahzad M (2015) Keystroke recognition using WiFi signals. In: Proceedings of the international conference on mobile computing and networking. ACM, pp 90–102Google Scholar
  3. Chellappa R, Wilson CL, Sirohey S (1995) Human and machine recognition of faces: a survey. Proc IEEE 83(5):705–741CrossRefGoogle Scholar
  4. Chen L, Hoey J, Nugent CD, Cook DJ, Yu Z (2012) Sensor-based activity recognition. IEEE Trans Syst Man Cybern Part C 42(6):790–808CrossRefGoogle Scholar
  5. Chen C, Zhang D, Ma X, Guo B, Wang L, Wang Y et al (2017) CrowdDeliver: planning city-wide package delivery paths leveraging the crowd of taxis. IEEE Trans Intell Transp Syst 18(6):1478–1496Google Scholar
  6. Chen C, Jiao S, Zhang S, Liu W, Feng L, Wang Y (2018) TripImputor: real-time imputing taxi trip purpose leveraging multi-sourced urban data. IEEE Trans Intell Transp Syst PP(99):1–13Google Scholar
  7. Du H, Yu Z, Yi F, Wang Z, Han Q, Guo B (2018) Recognition of group mobility level and group structure with mobile devices. IEEE Trans Mob Comput 17(4):884–897CrossRefGoogle Scholar
  8. Duta N (2009) A survey of biometric technology based on hand shape. Pattern Recogn 42(11):2797–2806CrossRefGoogle Scholar
  9. Gong L, Yang W, Zhou Z, Man D, Cai H, Zhou X et al (2016) An adaptive wireless passive human detection via fine-grained physical layer information. Ad Hoc Netw 38(C):38–50CrossRefGoogle Scholar
  10. Guo B, Han Q, Chen H, Shangguan L, Zhou Z, Yu Z (2017) The emergence of visual crowdsensing: challenges and opportunities. IEEE Commun Surv Tutor 19(4):2526–2543CrossRefGoogle Scholar
  11. Halperin D, Hu W, Sheth A, Wetherall D (2011) Tool release: gathering 802.11n traces with channel state information. ACM Sigcomm Comput Commun Rev 41(1):53–53CrossRefGoogle Scholar
  12. Han C, Wu K, Wang Y, Ni LM (2014) Wifall: Device-free fall detection by wireless networks. In: Proceedings of the international conference on computer communications. IEEE, pp 271–279Google Scholar
  13. Huang YF, Yao TY, Yang HJ (2015) Performance of hand gesture recognition based on received signal strength with weighting signaling in wireless communications. In: Proceedings of the international conference on network-based information systems. IEEE, pp 596–600Google Scholar
  14. Little JJ, Boyd JE (1998) Recognizing people by their gait: the shape of motion. J Comput Vis Res 1(2):1–32Google Scholar
  15. Liu W, Gao X, Wang L, Wang D (2015) Bfp: behavior-free passive motion detection using phy information. Wireless Pers Commun 83(2):1035–1055CrossRefGoogle Scholar
  16. Mulyono D, Jinn HS (2008) A study of finger vein biometric for personal identification. International Symposium on Biometrics and Security Technologies, pp 1–8Google Scholar
  17. Nandakumar R, Kellogg B, Gollakota S (2014) Wi-fi gesture recognition on existing devices. Eprint Arxiv 3(2):17–17Google Scholar
  18. Nickel C, Busch C, Rangarajan S, Möbius M (2011) Using hidden markov models for accelerometer-based biometric gait recognition. In: Proceedings of the international colloquium on signal processing and its applications. IEEE, pp 58–63Google Scholar
  19. Parekh D (2012) Fingerprint classification. LAP LAMBERT Academic Publishing, Saarbrücken, pp 809–815Google Scholar
  20. Park HA, Kang RP (2007) Iris recognition based on score level fusion by using svm. Pattern Recogn Lett 28(15):2019–2028CrossRefGoogle Scholar
  21. Qian K, Wu C, Yang Z, Liu Y, Zhou Z (2015) PADS: passive detection of moving targets with dynamic speed using PHY layer information. In: Proceedings of the international conference on parallel and distributed systems. IEEE, pp 1–8Google Scholar
  22. Sen S, Lee J, Kim KH, Congdon P (2013) Avoiding multipath to revive inbuilding WiFi localization. In: Proceeding of the international conference on mobile systems, applications, and services. ACM, pp 249–262Google Scholar
  23. Wang Y, Liu J, Chen Y, Gruteser M, Yang J, Liu H (2014a) E-eyes: device-free location-oriented activity identification using fine-grained WiFi signatures. In: Proceedings of the 20th annual international conference on mobile computing and networking. ACM, pp 617–628Google Scholar
  24. Wang G, Zou Y, Zhou Z, Wu K, Ni LM (2014b) We can hear you with Wi-Fi!. In: Proceedings of the international conference on mobile computing and networking. ACM, pp 593–604Google Scholar
  25. Wang L, Zhang D, Wang Y, Chen C, Han X, M’Hamed A (2016a) Sparse mobile crowdsensing: challenges and opportunities. IEEE Commun Mag 54(7):161–167CrossRefGoogle Scholar
  26. Wang W, Liu AX, Shahzad M (2016b) Gait recognition using wifi signals. In: Proceedings of the international joint conference on pervasive and ubiquitous computing. ACM, pp 363–373Google Scholar
  27. Wang Z, Guo B, Yu Z, Zhou X (2018) Wi-Fi CSI based behavior recognition: from signals, actions to activities. IEEE Communications Magazine (Accepted)Google Scholar
  28. Xi W, Zhao J, Li XY, Zhao K (2014) Electronic frog eye: counting crowd using WiFi. In: Proceedings of the international conference on computer communications. IEEE, pp 361–369Google Scholar
  29. Yang Z, Zhou Z, Liu Y (2014) From rssi to csi: indoor localization via channel response. Acm Comput Surv 46(2):1–32CrossRefGoogle Scholar
  30. Yu Z, Du H, Xiao D, Wang Z, Han Q, Guo B (2018) Recognition of human computer operations based on keystroke sensing by smartphone microphone. IEEE Internet Things J PP(99):1Google Scholar
  31. Zanca G, Zorzi F, Zanella A, Zorzi M (2008) Experimental comparison of RSSI-based localization algorithms for indoor wireless sensor networks. The Workshop on Real-World Wireless Sensor Networks, pp 1–5Google Scholar
  32. Zeng Y, Pathak PH, Mohapatra P (2016) WiWho: wifi-based person identification in smart spaces. In: Proceedings of the 15th international conference on information processing in sensor networks. ACM/IEEE, pp 1–12Google Scholar
  33. Zhou Z, Yang Z, Wu C, Shangguan L, Liu Y (2014) Omnidirectional coverage for device-free passive human detection. IEEE Trans Parallel Distrib Syst 25(7):1819–1829CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer ScienceNorthwestern Polytechnical UniversityXi’anPeople’s Republic of China

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