Recognition of Bed Postures Using Mutual Capacitance Sensing

  • Silvia RusEmail author
  • Tobias Grosse-Puppendahl
  • Arjan Kuijper
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8850)


In recent years, mutual capacitive sensing made significant advances in the field of gathering implicit contextual data. These systems find broad usage in pervasive activity-recognition systems, installed stationary or made portable. In the domain of context recognition new ways of interaction with the environment opened up since conductive objects can be detected under certain conditions at distances up to 50 cm.

This paper investigates an approach to recognize bed postures using mutual capacitance sensing. The overall goal is to develop a technological concept that can be applied to recognize bed postures of patients in elderly homes. The use of this contextual data may lead to many desired benefits in elderly care e.g. the better prevention of decubitus ulcer, a condition caused by prolonged pressure on the skin resulting in injuries to skin and underlying tissues. For this, we propose a low-cost grid of crossed wires of 48 measurement points placed between the mattress and the bed sheet. The experimental results analyze a set of five lying positions. We achieved for all tested individuals an accuracy of 80.76% and for several individuals of the same bodysize an accuracy of 93.8%.


Activity recognition Capacitive sensing Ambient assisted living 


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  1. 1.
    Barrett, G., Omote, R.: Projected-capacitive touch technology. Information Display 26(3), 16–21 (2010)Google Scholar
  2. 2.
    Borazio, M., Blanke, U., Van Laerhoven, K.: Characterizing sleeping trends from postures. In: 2010 International Symposium on Wearable Computers (ISWC), pp. 1–2. IEEE (2010)Google Scholar
  3. 3.
    Diraco, G., Leone, A., Siciliano, P.: Human posture recognition with a time-of-flight 3d sensor for in-home applications. Expert Systems with Applications (2012)Google Scholar
  4. 4.
    Foubert, N., McKee, A., Goubran, R., Knoefel, F.: Lying and sitting posture recognition and transition detection using a pressure sensor array. In: 2012 IEEE International Symposium on Medical Measurements and Applications Proceedings (MeMeA), pp. 1–6 (2012)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: 2013 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 152–159 (2013)Google Scholar
  6. 6.
    Grosse-Puppendahl, T., Berlin, E., Borazio, M.: Enhancing accelerometer-based activity recognition with capacitive proximity sensing. In: Paternò, F., de Ruyter, B., Markopoulos, P., Santoro, C., van Loenen, E., Luyten, K. (eds.) AmI 2012. LNCS, vol. 7683, pp. 17–32. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  7. 7.
    Hamisu, P., Braun, A.: Analyse des schlafverhaltens durch kapazitive sensorarrays zur ermittlung der wirbelsäulenbelastung. Ambient Assisted Living-AAL (2010)Google Scholar
  8. 8.
    Hsia, C., Liou, K.J., Aung, A.P.W., Foo, V., Huang, W., Biswas, J.: Analysis and comparison of sleeping posture classification methods using pressure sensitive bed system. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2009, pp. 6131–6134 (2009)Google Scholar
  9. 9.
    Liu, J.J., Huang, M.C., Xu, W., Alshurafa, N., Sarrafzadeh, M.: On-bed monitoring for range of motion exercises with a pressure sensitive bedsheet. In: 2013 IEEE International Conference on Body Sensor Networks (BSN), pp. 1–6. IEEE (2013)Google Scholar
  10. 10.
    Liu, J.J., Xu, W., Huang, M.C., Alshurafa, N., Sarrafzadeh, M., Raut, N., Yadegar, B.: A dense pressure sensitive bedsheet design for unobtrusive sleep posture monitoring. In: IEEE International Conference on Pervasive Computing and Communications (PerCom), vol. 18, p. 22 (2013)Google Scholar
  11. 11.
    Ni, H., Abdulrazak, B., Zhang, D., Wu, S.: Unobtrusive sleep posture detection for elder-care in smart home. In: Lee, Y., Bien, Z.Z., Mokhtari, M., Kim, J.T., Park, M., Kim, J., Lee, H., Khalil, I. (eds.) ICOST 2010. LNCS, vol. 6159, pp. 67–75. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    für Innovationen im Gesundheitswesen und angewandte Pflegeforschung e.V., I.: Dekubitus - immer noch ein problem in der pflege (2003–2013). (October 31, 2013)
  13. 13.
    Pratt, S.: Ask the application engineer 35 (2006)Google Scholar
  14. 14.
    Shotton, J., Sharp, T., Kipman, A., Fitzgibbon, A., Finocchio, M., Blake, A., Cook, M., Moore, R.: Real-time human pose recognition in parts from single depth images. Communications of the ACM 56(1), 116–124 (2013)CrossRefGoogle Scholar
  15. 15.
    Smith, J.R.: Electric field imaging. Ph.D. thesis (1999)Google Scholar
  16. 16.
    Teske, P.: Ein Multisensor-System zur Sturzerkennung. Master’s thesis, HAW Hamburg, Berliner Tor 5, 20099 Hamburg (2012)Google Scholar
  17. 17.
    Van Laerhoven, K., Borazio, M., Kilian, D., Schiele, B.: Sustained logging and discrimination of sleep postures with low-level, wrist-worn sensors. In: Proceedings of the 12th International Symposium on Wearable Computers (ISWC 2008), pp. 69–77. IEEE Press (2008)Google Scholar
  18. 18.
    Walsh, L., McLoone, S.: Non-contact under-mattress sleep monitoring. Journal of Ambient Intelligence and Smart Environments 6(4), 385–401 (2014)Google Scholar
  19. 19.
    Wimmer, R., Kranz, M., Boring, S., Schmidt, A.: A Capacitive Sensing Toolkit for Pervasive Activity Detection and Recognition. In: IEEE International Conference on Pervasive Computing and Communications (PerCom 2007). IEEE Computer Society (2007)Google Scholar
  20. 20.
    Wrzus, C., Brandmaier, A.M., Von Oertzen, T., Müller, V., Wagner, G.G., Riediger, M.: A new approach for assessing sleep duration and postures from ambulatory accelerometry. PloS one 7(10), e48089 (2012)CrossRefGoogle Scholar
  21. 21.
    Yu, M., Naqvi, S., Wang, L., Chambers, J., et al.: Posture recognition based fall detection system for monitoring an elderly person in a smart home environment (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Silvia Rus
    • 1
    Email author
  • Tobias Grosse-Puppendahl
    • 1
  • Arjan Kuijper
    • 1
    • 2
  1. 1.Fraunhofer IGDDarmstadtGermany
  2. 2.Technische Universität DarmstadtDarmstadtGermany

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