Using Height Sensors for Biometric Identification in Multi-resident Homes

  • Vijay Srinivasan
  • John Stankovic
  • Kamin Whitehouse
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6030)


In this study, we evaluate the use of height for biometric identification of residents, by mounting ultrasonic distance sensors above the doorways in a home. Height sensors are cheap, are convenient for the residents, are simple to install in an existing home, and are perceived to be less invasive than cameras or microphones. Height is typically only a weak biometric, but we show that it is well suited for identifying among a few residents in the home, and can potentially be improved by using the history of height measurements at multiple doorways in a tracking approach. We evaluate this approach using 20 people in a controlled laboratory environment and by installing in 3 natural, home environments. We combine these results with public anthropometric data sets that contain the heights of residents in 2077 elderly multi-resident homes to conclude that height sensors could potentially achieve at least 95% identification accuracy in 95% of elderly homes in the US.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Health and retirement study (2006),
  2. 2.
    Quietcare systems - living independently,
  3. 3.
    Vernier go motion ultrasonic sensor,
  4. 4.
    Wellaware systems for elderly monitoring,
  5. 5.
    Abdelkader, B., et al.: Person identification using automatic height and stride estimation. In: International Conference on Pattern Recognition (2002)Google Scholar
  6. 6.
    Addlesee, M., Jones, A., Livesey, F., Samaria, F.: The ORL active floor. IEEE Personal Communications (1997)Google Scholar
  7. 7.
    Ester, M., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: International Conference on Knowledge Discovery and Data Mining (1996)Google Scholar
  8. 8.
    Gao, G., Whitehouse, K.: The Self-Programming Thermostat: Optimizing Setback Schedules based on Home Occupancy Patterns. In: First ACM Workshop on Embedded Sensing Systems For Energy-Efficiency In Buildings (2009)Google Scholar
  9. 9.
    Jenkins, J., Ellis, C.: Using ground reaction forces from gait analysis: body mass as a weak biometric. In: LaMarca, A., Langheinrich, M., Truong, K.N. (eds.) Pervasive 2007. LNCS, vol. 4480, pp. 251–267. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  10. 10.
    Jenkins, J., et al.: Weakly identifying system for doorway monitoring. Duke Fontiers Poster Session (May 2006)Google Scholar
  11. 11.
    Klasnja, P., Consolvo, S., Choudhury, T., Beckwith, R., Hightower, J.: Exploring privacy concerns about personal sensing. In: Proceedings of the Seventh International Conference on Pervasive Computing, Nara, Japan (May 2009)Google Scholar
  12. 12.
    Köhler, M., Patel, S., Summet, J., Stuntebeck, E., Abowd, G.: TrackSense: Infrastructure free precise indoor positioning using projected patterns. In: LaMarca, A., Langheinrich, M., Truong, K.N. (eds.) PERVASIVE 2007. LNCS, vol. 4480, pp. 334–350. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Lorincz, K., Welsh, M.: MoteTrack: a robust, decentralized approach to RF-based location tracking. Personal and Ubiquitous Computing, 489–503 (2007)Google Scholar
  14. 14.
    Nishida, Y., Murakami, S., Hori, T., Mizoguchi, H.: Minimally privacy-violative human location sensor by ultrasonic radar embedded on ceiling. In: Proceedings of IEEE Sensors (2004)Google Scholar
  15. 15.
    Patel, S., Truong, K., Abowd, G.: Powerline positioning: A practical sub-room-level indoor location system for domestic use. In: Dourish, P., Friday, A. (eds.) UbiComp 2006. LNCS, vol. 4206, pp. 441–458. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  16. 16.
    Shakhnarovich, G., et al.: Integrated face and gait recognition from multiple views. In: Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, Los Alamitos (2001)Google Scholar
  17. 17.
    Shnayder, V., et al.: Sensor networks for medical care. In: Sensys. ACM Press, New York (2005)Google Scholar
  18. 18.
    Smith, A.: Exploring the acceptability of biometrics and fingerprint technologies. International Journal of Services and Standards (2005)Google Scholar
  19. 19.
    Want, R., Hopper, A., Falcao, V., Gibbons, J.: The active badge location system. ACM Transactions on Information Systems, TOIS (1992)Google Scholar
  20. 20.
    Wilson, D., Atkeson, C.: Simultaneous tracking and activity recognition (STAR) using many anonymous, binary sensors. In: Gellersen, H.-W., Want, R., Schmidt, A. (eds.) PERVASIVE 2005. LNCS, vol. 3468, pp. 62–79. Springer, Heidelberg (2005)Google Scholar
  21. 21.
    Woodman, O., Harle, R.: Pedestrian localisation for indoor environments. In: Proceedings of the 10th international conference on Ubiquitous computing, pp. 114–123. ACM, New York (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Vijay Srinivasan
    • 1
  • John Stankovic
    • 1
  • Kamin Whitehouse
    • 1
  1. 1.Department of Computer scienceUniversity of VirginiaCharlottesville, VirginiaUSA

Personalised recommendations