Capacitive Sensors for Whole Body Interaction

  • Raphael Wimmer
Part of the Human-Computer Interaction Series book series (HCIS)


Capacitive proximity sensors can be used to implement a variety of expressive input devices. They are especially suitable for Whole Body Interaction as they are small, robust, flexible, and can be both worn on the body or embedded into the environment. This chapter discusses technical challenges that arise when using capacitive sensors for tracking human motion, namely sensor shielding and ensuring both low latency and high sensitivity. A custom sensor design and an adaptive moving average filter presented here address these challenges. Two user studies evaluated these sensors as input modalities for different computer games. They found evidence that capacitive sensors offer a friendly but challenging behavior, being easy to learn but hard to master.


Simple Game Sensor Reading Capacitance Change Body Interaction Fine Motor Skill 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The user studies were planned, conducted, and analyzed by Annette Reiter, a graduate student I supervised.


  1. 1.
    Cremer, M.: Ueber die Registrierung mechanischer Vorgänge auf electrischem Wege, speziell mit Hilfe des Saitengalvanometers und Saitenelectrometers. Münch. Med. Wochenschr. 54, 1629–1630 (1907)Google Scholar
  2. 2.
    Glinsky, A.V.: The theremin in the emergence of electronic music. Ph.D. thesis, New York University, New York (1992)Google Scholar
  3. 3.
    Koster, R.: A Theory of Fun for Game Design. Paraglyph Press (2004)Google Scholar
  4. 4.
    Lee, C.H., Hu, Y., Selker, T.: iSphere: A proximity-based 3D input interface. In: Proceedings of CAAD Futures 2005.
  5. 5.
    Lion Precision: Capacitive sensor operation and optimization. Tech. rep. (2006)Google Scholar
  6. 6.
    Mason, C.: Terpsitone.A new electronic novelty. Radio Craft 335(1936)Google Scholar
  7. 7.
    Myers, D.:A q-study of game player aesthetics. Simul.Gaming 21(4), 375-396 (1990). doi: Google Scholar
  8. 8.
    Plamondon, R., Alimi, A.M.:Speed/accuracy trade-offs in target-directed movements. Behav. Brain Sci. 20(2), 279–303; discussion 303–349 (1997). Google Scholar
  9. 9.
    Rekimoto, J.:Gesturewrist and gesturepad:unobtrusive wearable interaction devices.
  10. 10.
    Rekimoto, J., Wang, H.: Sensing gamepad: electrostatic potential sensing for enhancing entertainment oriented interactions. In: CHI ’04, pp. 1457–1460. ACM Press, New York (2004). doi:
  11. 11.
    Reverter, F., Li, X., Meijer, G.: Stability and accuracy of active shielding for grounded capacitive sensors. Meas. Sci. Technol. 17, 2884 (2006)CrossRefGoogle Scholar
  12. 12.
    Smith, J.: Electric field imaging. Ph.D. thesis, Massachusetts Institute of Technology (1999).
  13. 13.
    Smith, J., White, T., Dodge, C.: Electric field sensing for graphical interfaces. Comput. Graph. Appl. 18(3), 54-61 (1998).
  14. 14.
    Smith, S.: Digital Signal Processing: A Practical Guide for Engineers and Scientists, p. 278f. Newnes (2003)Google Scholar
  15. 15.
    Taylor, B.T., Bove, M.V.: Graspables: grasp-recognition as a user interface. In: In Proceedings CHI’09, pp. 917-926. ACM, New York (2009). doi:10.1145/1518701.1518842.
  16. 16.
    Teixeira, T., Dublon, G., Savvides, A.: A Survey of Human-Sensing: Methods for Detecting Presence, Count, Location, Track, and IdentityGoogle Scholar
  17. 17.
    Valtonen, M., Maentausta, J., Vanhala, J.: Tiletrack: Capacitive human tracking using floor tiles. In: PERCOM ’09: Proceedings of the 2009 IEEE International Conference on Pervasive Computing and Communications, pp. 1–10. IEEE Computer Society, Washington, DC (2009). doi:
  18. 18.
    Wimmer, R., Holleis, P., Kranz, M., Schmidt, A.: Thracker – using capacitive sensing for gesture recognition. ICDCSW 0, 64 (2006). doi:
  19. 19.
    Wimmer, R., Kranz, M., Boring, S., Schmidt, A.: A capacitive sensing toolkit for pervasive activity detection and recognition. In: PerCom ’07 (2007)Google Scholar
  20. 20.
    Wimmer, R., Kranz, M., Boring, S., Schmidt, A.: CapTable and capShelf-unobtrusive activity recognition using networked capacitive sensors. In: Fourth International Conference on Networked Sensing Systems, 2007. INSS’07, pp. 85–88 (2007)Google Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.University of MunichMunichGermany

Personalised recommendations