The Capacitive Chair
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.
KeywordsCapacitive proximity sensor Posture recognition Smart furniture
We would like to thank all volunteers that participated in our studies and provided valuable feedback for future iterations. This work was partially funded by EIT ICT Labs SSP14267 and HWB13031.
- 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.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.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.Braun, A., Heggen, H.: Context recognition using capacitive sensor arrays in beds. In: Proceedings AAL-Kongress (2012)Google Scholar
- 7.Djakow, M., Braun, A., Marinc, A.: MoviBed - sleep analysis using capacitive sensors. In: Proceedings UAHCI, pp. 171–181 (2014)Google Scholar
- 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.Steelcase Inc.: Global posture study. http://www.steelcase.com/en/products/category/seating/task/gesture/pages/global-posture-study.aspx. Accessed 30 Jan 2015
- 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
- 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