ExerSeat - Sensor-Supported Exercise System for Ergonomic Microbreaks
The percentage of older adult workers in Europe has been increasing in the last decades. They are an important part of the work force, highly experienced and often hard to replace. However, their productivity can be affected by health problems, such as lower back pain. This increases the cost for employers and reduces the quality of life of the office workers. Knowledge workers that spend a large part of their day in front of a screen are particularly affected by pack pain. Regular exercise can help to mitigate some of these issues. This training can be performed in microbreaks that are taken at regular intervals during the work day. In this work we present ExerSeat, a combination of a smart sensing chair that uses eight capacitive proximity sensors to precisely track the posture of persons on or near an office chair. It is augmented by a desktop training software that is able to track exercises and training units during microbreaks, by analyzing frequency and form. We have performed a pilot over eight weeks with ten office workers. They performed training units at regular intervals during their work day. We report on the findings.
KeywordsSmart furniture Capacitive proximity sensing Office exercise Microbreaks Ergonomics New Ways of Working Well-being
We would like to extend our gratitude to the Operations Support Services Unit volunteers at VTT Espoo for participating in our pilot, providing detailed feedback and to their management for allowing our minor intervention to their daily office routine. This work was supported by EIT Digital under the project number SSP14267.
- 1.Aaltonen, I., Ala-Kotila, P., Järnström, H., Laarni, J., Määttä, H., Nykänen, E., Schembri, I., Lönnqvist, A., Ruostela, J.: State-of-the-Art Report on Knowledge Work (2012)Google Scholar
- 4.Braun, A., Heggen, H.: Context recognition using capacitive sensor arrays in beds. In: Proceedings AAL-Kongress (2012)Google Scholar
- 7.Brooke, J.: SUS-A quick and dirty usability scale. Usability Eval. Ind. 189(194), 4–7 (1996)Google Scholar
- 8.Davenport, T.H., Thomas, R.J., Cantrell, S.: The mysterious art and science of knowledge-worker performance. MIT Sloan Manag. Rev. 44(1), 23–30 (2012)Google Scholar
- 9.European Foundation for the Improvement of Living and Working Conditions: Employment trends and policies for older workers in the recession. Technical report (2011). http://ec.europa.eu/social/BlobServlet?docId=9590&langId=en
- 10.Griffiths, E., Saponas, T.S., Brush, A.J.B.: Health chair: implicitly sensing heart and respiratory rate. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp 2014 Adjunct, pp. 661–671. ACM Press, New York, September 2014Google Scholar
- 11.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 2013, pp. 152–159 (2013)Google Scholar
- 13.Harada, T., Mori, T., Nishida, Y., Yoshimi, T., Sato, T.: Body parts positions and posture estimation system based on pressure distribution image. In: Proceedings 1999 IEEE International Conference on Robotics and Automation, vol. 2, pp. 968–975 (1999)Google Scholar
- 14.Liang, G., Cao, J., Liu, X., Han, X.: Cushionware: A practical sitting posture-based interaction system. In: CHI 2014 Extended Abstracts on Human Factors in Computing Systems, pp. 591–594 (2014)Google Scholar
- 17.Rus, S., Grosse-Puppendahl, T., Kuijper, A.: Recognition of bed postures using mutual capacitance sensing. AmI 2014. LNCS, vol. 8850, pp. 51–66. Springer, Heidelberg (2014) Google Scholar
- 21.TriSun Software Inc.: PC Work Break (2015). http://www.trisunsoft.com/pc-work-break/
- 22.Valat, J.P., Goupille, P., Védere, V.: Low back pain: risk factors for chronicity. Revue du rhumatisme (English ed.) 64(3), 189–194 (1997)Google Scholar