New Approaches for Localization and Activity Sensing in Smart Environments

  • Florian Kirchbuchner
  • Biying Fu
  • Andreas Braun
  • Julian von Wilmsdorff
Part of the Advanced Technologies and Societal Change book series (ATSC)


Smart environments need to be able to fulfill the wishes of its occupants unobtrusively. To achieve this goal, it has to be guaranteed that the current state environment is perceived at all times. One of the most important aspects is to find the current position of the inhabitants and to perceive how they move in this environment. Numerous technologies enable such supervision. Particularly challenging are marker-free systems that are also privacy-preserving. In this paper, we present two such systems for localizing inhabitants in a Smart Environment using—electrical potential sensing and ultrasonic Doppler sensing. We present methods that infer location and track the user, based on the acquired sensor data. Finally, we discuss the advantages and challenges of these sensing technologies and provide an overview of future research directions.


Smart environment Electric potential sensing Ultrasonic sensing Data mining Indoor localization 


  1. 1.
    Braun, A., Heggen, H., Wichert, R.: Capfloor–a flexible capacitive indoor localization system. In: Evaluating AAL Systems Through Competitive Benchmarking. Indoor Localization and Tracking, pp. 26–35. Springer (2012)Google Scholar
  2. 2.
    Breitenstein, M.D., Reichlin, F., Leibe, B., Koller-Meier, E., Van Gool, L.: Online multiperson tracking-by-detection from a single, uncalibrated camera. IEEE Trans. Pattern Anal. Mach. Intell. 33(9), 1820–1833 (2011)CrossRefGoogle Scholar
  3. 3.
    Dellangnol, X.: Indoor localization based on electric potential sensing. Master’s thesis, Darmstadt, TU, Master Thesis (2015), 81 pGoogle Scholar
  4. 4.
    Demiris, G., Rantz, M.J., Aud, M.A., Marek, K.D., Tyrer, H.W., Skubic, M., Hussam, A.A.: Older adults’ attitudes towards and perceptions of smart home technologies: a pilot study. Inform. Health Soc. Care 29(2), 87–94 (2004)Google Scholar
  5. 5.
    Demirkus, M., Garg, K., Guler, S.: Automated person categorization for video surveillance using soft biometrics. In: SPIE Defense, Security, and Sensing, pp. 76670P–76670P. International Society for Optics and Photonics (2010)Google Scholar
  6. 6.
    Ficker, T.: Electrification of human body by walking. J. Electrostat. 64(1), 10–16 (2006)CrossRefGoogle Scholar
  7. 7.
    Fu, B., Karolus, J., Grosse-Puppendahl, T., Hermann, J., Kuijper, A.: Opportunities for activity recognition using ultrasound doppler sensing on unmodified mobile phones. In: iWOAR 2015, p. 10. Association for Computing Machinery (ACM), ACM Press, New York (2015)Google Scholar
  8. 8.
    Gabriel, Z., Bowling, A.: Quality of life from the perspectives of older people. Ageing Soc. 24(05), 675–691 (2004)CrossRefGoogle Scholar
  9. 9.
    Gilbert, W., Mottelay, P., Wright, E.: William Gilbert of Colchester, Physician of London: On the Load Stone and Magnetic Bodies. Wiley (1893)Google Scholar
  10. 10.
    Gupta, S., Morris, D., Patel, S., Tan, D.: Soundwave: using the doppler effect to sense gestures. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1911–1914. CHI ’12 (2012)Google Scholar
  11. 11.
    Hao-hao, H., Jun-Qiao, X.: A method of liquid level measurement based on ultrasonic echo characteristics. In: 2010 International Conference on Computer Application and System Modeling (ICCASM), vol. 11, pp. V11-682–V11-684 (2010)Google Scholar
  12. 12.
    Keller, H.J.: Advanced passive infrared presence detectors as key elements in integrated security and building automation systems. In: Security Technology, 1993. In: 1993 International Carnahan Conference on Security Technology, Proceedings. Institute of Electrical and Electronics Engineers, pp. 75–77. IEEE (1993)Google Scholar
  13. 13.
    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: Ambient Intelligence, pp. 48–59. Springer (2015)Google Scholar
  14. 14.
    Kurita, K.: New approach to touch sensing technique based on measurement of current generated by electrostatic inductionGoogle Scholar
  15. 15.
    Lee, H.K., Kim, J.H.: An hmm-based threshold model approach for gesture recognition. IEEE Trans. Pattern Anal. Mach. Intell. 21(10), 961–973 (1999)CrossRefGoogle Scholar
  16. 16.
    Lo, D., Mendonça, P.R., Hopper, A., et al.: Trip: a low-cost vision-based location system for ubiquitous computing. Pers. Ubiquitous Comput. 6(3), 206–219 (2002)CrossRefGoogle Scholar
  17. 17.
    Mandlik, M., Němec, Z., Vaňkát, T.: Real-time ultrasonic localization using an ultrasonic sensor arrayGoogle Scholar
  18. 18.
    Mautz, R., Tilch, S.: Survey of optical indoor positioning systems. In: 2011 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–7. IEEE (2011)Google Scholar
  19. 19.
    Milstein, A., Sánchez, J.N., Williamson, E.T.: Robust global localization using clustered particle filtering. In: AAAI/IAAI, pp. 581–586 (2002)Google Scholar
  20. 20.
    Mulloni, A., Wagner, D., Barakonyi, I., Schmalstieg, D.: Indoor positioning and navigation with camera phones. IEEE Pervasive Comput. 8(2), 22–31 (2009)CrossRefGoogle Scholar
  21. 21.
    Naghibzadeh, S., Pandharipande, A., Caicedo, D., Leus, G.: Indoor granular presence sensing with an ultrasonic circular array sensor. In: 2014 IEEE International Symposium on Intelligent Control (ISIC), pp. 1644–1649. IEEE (2014)Google Scholar
  22. 22.
    Nandakumar, R., Gollakota, S., Watson, N.: Contactless sleep apnea detection on smartphones. In: Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services, pp. 45–57. MobiSys ’15 (2015)Google Scholar
  23. 23.
    Nirjon, S., Liu, J., DeJean, G., Priyantha, B., Jin, Y., Hart, T.: Coin-gps: indoor localization from direct gps receiving. In: Proceedings of the 12th Annual International Conference on Mobile Systems, Applications, and Services, pp. 301–314. ACM (2014)Google Scholar
  24. 24.
    Nishida, Y., Hori, T., Murakami, S., Mizoguchi, H.: Minimally privacy-violative system for locating human by ultrasonic radar embedded on ceiling. In: 2004 IEEE International Conference on Systems, Man and Cybernetics, vol. 2, pp. 1549–1554 (2004)Google Scholar
  25. 25.
    Prance, H., Watson, P., Prance, R.J., Beardsmore-Rust, S.T.: Position and movement sensing at metre standoff distances using ambient electric field. Measur. Sci. Technol. 23(11), 115101 (2012)CrossRefGoogle Scholar
  26. 26.
    Prance, R.J., Beardsmore-Rust, S.T., Watson, P., Harland, C.J., Prance, H.: Remote detection of human electrophysiological signals using electric potential sensors. Appl. Phys. Lett. 93(3) (2008)Google Scholar
  27. 27.
    Prance, R., Debray, A., Clark, T., Prance, H., Nock, M., Harland, C., Clippingdale, A.: An ultra-low-noise electrical-potential probe for human-body scanning. Measur. Sci. Technol. 11(3), 291–297 (2000)CrossRefGoogle Scholar
  28. 28.
    Priyantha, N.B.: The cricket indoor location system. Ph.D. Thesis, Massachusetts Institute of Technology (2005)Google Scholar
  29. 29.
    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, pp. 27–38. MobiCom ’13, ACM, New York (2013). doi: 10.1145/2500423.2500436
  30. 30.
    von Ramm, O.T., Smith, S.W.: Real-Time Volumetric Ultrasound Imaging System (1990)Google Scholar
  31. 31.
    Rekimoto, J., Wang, H.: Sensing gamepad: electrostatic potential sensing for enhancing entertainment oriented interactions. In: Extended Abstracts on Human Factors in Computing Systems, pp. 1457–1460 (2004)Google Scholar
  32. 32.
    Steinhage, A., Hoffmann, R., Lauterbach, C.: Automatische unterscheidung von personen und haustieren auf dem assistenzsystem sensfloor. In: AAL-Kongress 2015. VDE VERLAG GmbH (2015)Google Scholar
  33. 33.
    Steinhausen, N.: Applications of the electric potential sensor for healthcare and assistive technologies. Ph.D. Thesis, University of Sussex (2014)Google Scholar
  34. 34.
    Wang, X.J., Lambert, M.F., Simpson, A.R., Vitkovsky, J.P., et al.: Leak detection in pipelines and pipe networks: a review (2001)Google Scholar
  35. 35.
    Yarovoy, A., Ligthart, L., Matuzas, J., Levitas, B.: Uwb radar for human being detection. IEEE Aerosp. Electron. Syst. Mag. 21(3), 10–14 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Florian Kirchbuchner
    • 1
  • Biying Fu
    • 1
  • Andreas Braun
    • 1
  • Julian von Wilmsdorff
    • 1
  1. 1.Fraunhofer Institute for Computer Graphics Research IGDDarmstadtGermany

Personalised recommendations