Ambient Gesture-Recognizing Surfaces with Visual Feedback

  • Tobias Grosse-Puppendahl
  • Sebastian Beck
  • Daniel Wilbers
  • Steeven Zeiß
  • Julian von Wilmsdorff
  • Arjan Kuijper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8530)

Abstract

In recent years, gesture-based interaction gained increasing interest in Ambient Intelligence. Especially the success of camera-based gesture recognition systems shows that a great variety of applications can benefit significantly from natural and intuitive interaction paradigms. Besides camera-based systems, proximity-sensing surfaces are especially suitable as an input modality for intelligent environments. They can be installed ubiquitously under any kind of non-conductive surface, such as a table. However, interaction barriers and the types of supported gestures are often not apparent to the user. In order to solve this problem, we investigate an approach which combines a semi-transparent capacitive proximity-sensing surface with an LED array. The LED array is used to indicate possible gestural movements and provide visual feedback on the current interaction status. A user study shows that our approach can enhance the user experience, especially for inexperienced users.

Keywords

gesture recognition capacitive sensing proximity sensing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ballagas, R., Borchers, J., Rohs, M., Sheridan, J.G.: The smart phone: A ubiquitous input device. IEEE Pervasive Computing 5(1), 70–77 (2006)CrossRefGoogle Scholar
  2. 2.
    Braun, A., Hamisu, P.: Using the human body field as a medium for natural interaction. In: PETRA 2009, pp. 50:1–50:7 (2009)Google Scholar
  3. 3.
    Cohn, G., Morris, D., Patel, S., Tan, D.: Humantenna: Using the body as an antenna for real-time whole-body interaction. In: CHI 2012, pp. 1901–1910 (2012)Google Scholar
  4. 4.
    Glinsky, A.: Theremin: Ether Music and Espionage. University of Illinois Press (2000)Google Scholar
  5. 5.
    Grosse-Puppendahl, T., Berghoefer, Y., Braun, A., Wimmer, R., Kuijper, A.: Opencapsense: A rapid prototyping toolkit for pervasive interaction using capacitive sensing. In: PerCom 2013, pp. 152–159 (2013)Google Scholar
  6. 6.
    Grosse-Puppendahl, T., Braun, A., Kamieth, F., Kuijper, A.: Swiss-cheese extended: An object recognition method for ubiquitous interfaces based on capacitive proximity sensing. In: CHI 2013, pp. 1401–1410 (2013)Google Scholar
  7. 7.
    Harrison, C., Sato, M., Poupyrev, I.: Capacitive fingerprinting: Exploring user differentiation by sensing electrical properties of the human body. In: UIST 2012, pp. 537–544 (2012)Google Scholar
  8. 8.
    Majewski, M., Braun, A., Marinc, A., Kuijper, A.: Providing visual support for selecting reactive elements in intelligent environments. In: Gavrilova, M.L., Tan, C.J.K., Kuijper, A. (eds.) Transactions on Computational Science XVIII. LNCS, vol. 7848, pp. 248–263. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  9. 9.
    Microsoft: http://www.xbox.com/kinect/ (accessed June 20, 2013)
  10. 10.
    Poupyrev, I., Yeo, Z., Griffin, J.D., Hudson, S.: Sensing human activities with resonant tuning. In: CHI 2010 EA, pp. 4135–4140 (2010)Google Scholar
  11. 11.
    Sato, M., Poupyrev, I., Harrison, C.: Touché: Enhancing touch interaction on humans, screens, liquids, and everyday objects. In: CHI 2012, pp. 483–492 (2012)Google Scholar
  12. 12.
    Smith, J.R., Gershenfeld, N., Benton, S.A.: Electric Field Imaging. Ph.D. thesis, Massachusetts Institute of Technology (1999)Google Scholar
  13. 13.
    Sodhi, R., Benko, H., Wilson, A.: Lightguide: Projected visualizations for hand movement guidance. In: CHI 2012, pp. 179–188 (2012)Google Scholar
  14. 14.
    Sousa, M., Techmer, A., Steinhage, A., Lauterbach, C., Lukowicz, P.: Human tracking and identification using a sensitive floor and wearable accelerometers. In: PerCom 2013, vol. 18, p. 22 (2013)Google Scholar
  15. 15.
    Valtonen, M., Vuorela, T., Kaila, L., Vanhala, J.: Capacitive indoor positioning and contact sensing for activity recognition in smart homes. JAISE 4, 1–30 (2012)Google Scholar
  16. 16.
    Wimmer, R., Kranz, M., Boring, S., Schmidt, A.: Captable and capshelf - unobtrusive activity recognition using networked capacitive sensors. In: INSS 2007, pp. 85–88 (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Tobias Grosse-Puppendahl
    • 1
  • Sebastian Beck
    • 2
  • Daniel Wilbers
    • 1
  • Steeven Zeiß
    • 1
  • Julian von Wilmsdorff
    • 1
  • Arjan Kuijper
    • 1
    • 2
  1. 1.Fraunhofer IGDDarmstadtGermany
  2. 2.Technische Universität DarmstadtDarmstadtGermany

Personalised recommendations