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Indoor Localization Based on Passive Electric Field Sensing

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Book cover Ambient Intelligence (AmI 2017)

Abstract

The ability to perform accurate indoor positioning opens a wide range of opportunities, including smart home applications and location-based services. Smart floors are a well-established technology to enable marker-free indoor localization within an instrumented environment. Typically, they are based on pressure sensors or varieties of capacitive sensing. These systems, however, are often hard to deploy as mechanical or electrical features are required below the surface. They might also have a limited range or not be compatible with different floor materials. In this paper, we present a novel indoor positioning system using an uncommon form of passive electric field sensing, which detects the change in body electric potential during movement. It is easy to install by deploying a grid of passive wires underneath any non-conductive floor surface. The proposed architecture achieves a high position accuracy and an excellent spatial resolution. In our evaluation, we measure a mean positioning error of only 12.7 cm. The proposed system also combines the advantages of very low power consumption, easy installation, easy maintenance, and the preservation of privacy.

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References

  1. Bahl, P., Padmanabhan, V.N.: Radar: an in-building RF-based user location and tracking system. In: INFOCOM 2000, Proceedings of the Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies, vol. 2, pp. 775–784. IEEE (2000)

    Google Scholar 

  2. Braun, A., Heggen, H., Wichert, R.: CapFloor – a flexible capacitive indoor localization system. In: Chessa, S., Knauth, S. (eds.) EvAAL 2011. CCIS, vol. 309, pp. 26–35. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33533-4_3

    Chapter  Google Scholar 

  3. Dockstader, S.L., Tekalp, A.M.: Multiple camera tracking of interacting and occluded human motion. Proc. IEEE 89(10), 1441–1455 (2001)

    Article  MATH  Google Scholar 

  4. Elble, R.J., Thomas, S.S., Higgins, C., Colliver, J.: Stride-dependent changes in gait of older people. J. Neurol. 238(1), 1–5 (1991)

    Article  Google Scholar 

  5. Ficker, T.: Electrification of human body by walking. J. Electrostat. 64(1), 10–16 (2006)

    Article  Google Scholar 

  6. Filippoupolitis, A., Oliff, W., Loukas, G.: Occupancy detection for building emergency management using BLE beacons. In: Czachórski, T., Gelenbe, E., Grochla, K., Lent, R. (eds.) ISCIS 2016. CCIS, vol. 659, pp. 233–240. Springer, Cham (2016). doi:10.1007/978-3-319-47217-1_25

    Chapter  Google Scholar 

  7. Grosse-Puppendahl, T., Dellangnol, X., Hatzfeld, C., Fu, B., Kupnik, M., Kuijper, A., Hastall, M., Scott, J., Gruteser, M.: Platypus - indoor localization and identification through sensing electric potential changes in human bodies. In: 14th ACM International Conference on Mobile Systems, Applications and Services (MobiSys). ACM (2016)

    Google Scholar 

  8. Harland, C., Clark, T., Prance, R.: Electric potential probes-new directions in the remote sensing of the human body. Meas. Sci. Technol. 13(2), 163 (2001)

    Article  Google Scholar 

  9. Holm, S., Nilsen, C.I.C.: Robust ultrasonic indoor positioning using transmitter arrays. In: 2010 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–5. IEEE (2010)

    Google Scholar 

  10. Ibara, K., Kanetsuna, K., Hirakawa, M.: Identifying individuals’ footsteps walking on a floor sensor device. In: Yoshida, T., Kou, G., Skowron, A., Cao, J., Hacid, H., Zhong, N. (eds.) AMT 2013. LNCS, vol. 8210, pp. 56–63. Springer, Cham (2013). doi:10.1007/978-3-319-02750-0_6

    Chapter  Google Scholar 

  11. Kirchbuchner, F., Grosse-Puppendahl, T., Hastall, M.R., Distler, M., Kuijper, A.: Ambient intelligence from senior citizens’ perspectives: understanding privacy concerns, technology acceptance, and expectations. In: Ruyter, B., Kameas, A., Chatzimisios, P., Mavrommati, I. (eds.) AmI 2015. LNCS, vol. 9425, pp. 48–59. Springer, Cham (2015). doi:10.1007/978-3-319-26005-1_4

    Chapter  Google Scholar 

  12. Kruskal, W., Wallis, W.: Use of ranks in one-criterion variance analysis. J. Am. Stat. Assoc. 47, 583–621 (1952)

    Article  MATH  Google Scholar 

  13. Lee, C., Chang, Y., Park, G., Ryu, J., Jeong, S.G., Park, S., Park, J.W., Lee, H.C., Hong, K.S., Lee, M.H.: Indoor positioning system based on incident angles of infrared emitters. In: 30th Annual Conference of IEEE Industrial Electronics Society, IECON 2004, vol. 3, pp. 2218–2222. IEEE (2004)

    Google Scholar 

  14. Li, N., Becerik-Gerber, B.: Performance-based evaluation of rfid-based indoor location sensing solutions for the built environment. Adv. Eng. Inform. 25(3), 535–546 (2011)

    Article  Google Scholar 

  15. Lim, C.H., Wan, Y., Ng, B.P., See, C.M.S.: A real-time indoor WiFi localization system utilizing smart antennas. IEEE Trans. Consum. Electron. 53(2), 618–622 (2007)

    Article  Google Scholar 

  16. Pu, Q., Gupta, S., Gollakota, S., Patel, S.: Whole-home gesture recognition using wireless signals. In: Proceedings of the 19th Annual International Conference on Mobile Computing and Networking, MobiCom 2013, pp. 27–38. ACM, New York (2013)

    Google Scholar 

  17. Rubio, J.P.B., Zhou, C., Hernández, F.S.: Vision-based walking parameter estimation for biped locomotion imitation. In: Cabestany, J., Prieto, A., Sandoval, F. (eds.) IWANN 2005. LNCS, vol. 3512, pp. 677–684. Springer, Heidelberg (2005). doi:10.1007/11494669_83

    Chapter  Google Scholar 

  18. Saad, S.S., Nakad, Z.S.: A standalone RFID indoor positioning system using passive tags. IEEE Trans. Ind. Electron. 58(5), 1961–1970 (2011)

    Article  Google Scholar 

  19. Steinhage, A., Lauterbach, C.: Sensfloor (r): Ein aal sensorsystem für sicherheit, homecare und komfort. Ambient Assisted Living-AAL (2008)

    Google Scholar 

  20. Valtonen, M., Maentausta, J., Vanhala, J.: Tiletrack: capacitive human tracking using floor tiles. In: 2009 IEEE International Conference on Pervasive Computing and Communications, pp. 1–10 (2009)

    Google Scholar 

  21. Williams, A., Ganesan, D., Hanson, A.: Aging in place: fall detection and localization in a distributed smart camera network. In: Proceedings of the 15th International Conference on Multimedia, pp. 892–901. ACM (2007)

    Google Scholar 

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Correspondence to Biying Fu .

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Fu, B., Kirchbuchner, F., von Wilmsdorff, J., Grosse-Puppendahl, T., Braun, A., Kuijper, A. (2017). Indoor Localization Based on Passive Electric Field Sensing. In: Braun, A., Wichert, R., Maña, A. (eds) Ambient Intelligence. AmI 2017. Lecture Notes in Computer Science(), vol 10217. Springer, Cham. https://doi.org/10.1007/978-3-319-56997-0_5

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  • DOI: https://doi.org/10.1007/978-3-319-56997-0_5

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