International Conference on Distributed, Ambient, and Pervasive Interactions

DAPI 2015: Distributed, Ambient, and Pervasive Interactions pp 397-407 | Cite as

The Capacitive Chair

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9189)

Abstract

Modern office work often consists of spending long hours in a sitting position. This can cause a number of health-related issues, including chronic back pain. Ergonomic sitting requires suitably adjusted chairs and switching through a variety of different sitting positions throughout the day. Smart furniture can support this positive behavior, by recognizing poses and activities and giving suitable feedback to the occupant. In this work we present the Capacitive Chair. A number of capacitive proximity sensors are integrated into a regular office chair and can sense various physiological parameters, ranging from pose to activity levels or breathing rate recognition. We discuss a suitable sensor layouts and processing methods that enable detecting activity levels, posture and breathing rate. The system is evaluated in two user studies that test the activity recognition throughout a work week and the recognition rate of different poses.

Keywords

Capacitive proximity sensor Posture recognition Smart furniture 

References

  1. 1.
    Maniadakis, N., Gray, A.: The economic burden of back pain in the UK. Pain 84, 95–103 (2000)CrossRefGoogle Scholar
  2. 2.
    Robertson, M., Amick III, B.C., DeRango, K., Rooney, T., Bazzani, L., Harrist, R., Moore, A.: The effects of an office ergonomics training and chair intervention on worker knowledge, behavior and musculoskeletal risk. Appl. Ergon. 40, 124–135 (2009)CrossRefGoogle Scholar
  3. 3.
    Braun, A.: Application and validation of capacitive proximity sensing systems in smart environments. Dissertation, TU Darmstadt (2014). http://tuprints.ulb.tu-darmstadt.de/4175/
  4. 4.
    Braun, A., Wichert, R., Kuijper, A., Fellner, D.W.: Capacitive proximity sensing in smart environments. J. Ambient Intell. Smart Environ. (2015, in press)Google Scholar
  5. 5.
    Griffiths, E., Saponas, T.S., Brush, A.J.B.: Health chair: implicitly sensing heart and respiratory rate. UbiComp Adjunct., pp. 661–671 (2014)Google Scholar
  6. 6.
    Braun, A., Heggen, H.: Context recognition using capacitive sensor arrays in beds. In: Proceedings AAL-Kongress (2012)Google Scholar
  7. 7.
    Djakow, M., Braun, A., Marinc, A.: MoviBed - sleep analysis using capacitive sensors. In: Proceedings UAHCI, pp. 171–181 (2014)Google Scholar
  8. 8.
    Grosse-Puppendahl, T., Marinc, A., Braun, A.: Classification of user postures with capacitive proximity sensors in AAL-environments. In: Proceedings AmI International, pp. 314–323 (2011)Google Scholar
  9. 9.
  10. 10.
    Hearst, M.A., Dumais, S.T., Osman, E., Platt, J., Scholkopf, B.: Support vector machines. IEEE Intell. Syst. Appl. 13, 18–28 (1998)CrossRefGoogle Scholar
  11. 11.
    Platt, J.C.: Fast training of support vector machines using sequential minimal optimization. Advances in Kernel Methods - Support Vector Learning, pp. 185–208. MIT Press, Cambridge (1999)Google Scholar
  12. 12.
    Wade, O.L.: Movements of the thoracic cage and diaphragm in respiration. J. Physiol. 124, 193–212 (1954)CrossRefGoogle Scholar
  13. 13.
    Grosse-Puppendahl, T., Berghoefer, Y., Braun, A., Wimmer, R., Kuijper, A.: OpenCapSense: a rapid prototyping toolkit for pervasive interaction using capacitive sensing. In: Proceedings PerCom, pp. 152–159 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Andreas Braun
    • 1
  • Sebastian Frank
    • 2
  • Reiner Wichert
    • 1
  1. 1.Fraunhofer Institute for Computer Graphics Research IGDDarmstadtGermany
  2. 2.Hochschule Rhein-MainWiesbadenGermany

Personalised recommendations