Abstract
In today’s society, working environments are becoming more stressful. The problem of occupational stress is generally recognized as one of the major factors leading to a wide spectrum of health problems. However work should, ideally, be a source of health, pride and happiness, in the sense of enhancing motivation and strengthening personal development. In this work, we present StayActive, a system which aims to detect stress and burn-out risks by analyzing the behaviour of the users via their smartphone. The main purpose of StayActive is the use of the mobile sensor technology for detecting stress. Then a mobile service can recommend and present various relaxation activities “just in time” in order to allow users to carry out and solve everyday tasks and problems at work. In particular, we collect data from people’s daily phone usage gathering information about the sleeping pattern, the social interaction and the physical activity of the user. We assign a weight factor to each of these three dimensions of wellbeing according to the user’s personal perception and build a stress detection system. We evaluate our system in a real world environment with young adults and people working in the transportation company of Geneva. This paper highlights the architecture and model of this innovative stress detection system. The main innovation of this work is addressed in the fact that the way the stress level is computed is as less invasive as possible for the users.
This work was co-funded by the State Secretariat for Education, Research and Innovation of the Swiss federal government and the European Union, in the frame of the EU AAL project StayActive (aal-2013-6-126).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Sano, A., Picard, R.W.: Stress recognition using wearable sensors and mobile phones. In: Humaine Association Conference on Affective Computing and Intelligent Interaction, pp. 671–676 (2013)
Cohen, S., Kamarck, T.W., Mermelstein, R.: A global measure of perceived stress. J. Health Soc. Behav. 24, 385–396 (1983)
Norris, R., Carroll, D., Cochrane, R.: The effects of physical activity and exercise training on psychological stress and well-being in an adolescent population. J. Psychosom. Res. 36, 55–65 (1992)
Alvarez, G., Ayas, N.T.: The impact of daily sleep duration on health: a review of the literature. Prog. Cardiovasc. Nurs. 19, 56–59 (2004)
Fox, K.R.: The influence of physical activity on mental well-being. Public Health Nutr. 2, 411–418 (1999)
Paffenbarger, R.S., Hyde, R., Wing, A.L., Hsieh, C.: Physical activity, all-cause mortality, and longevity of college alumni. New England J. Med. 314, 605–613 (1986)
Nevit, M.C., Cummings, S.R., Kidd, S., Black, D.: Risk factors for recurrent nonsyncopal fall. A prospective study. J. Am. Med. Assoc. 261, 2663–2668 (1986)
Li, S., Chung, T.: Internet function and Internet addictive behavior. Comput. Hum. Behav. 22, 1067–1071 (2006)
Oulasvirta, A., Rattenbury, T., Ma, L., Raita, E.: Habits make smartphone use more pervasive. Pers. Ubiquit. Comput. 16, 105–114 (2012)
Muaremi, A., Arnrich, B., Trster, G.: Towards measuring stress with smartphones and wearable devices during workday and sleep. BioNanoScience 3, 172–183 (2013)
Moturu, S., Khayal, I., Aharony, N., Pan, W., Pentland, A.: Sleep, mood and sociability in a healthy population. In: 33rd Annual International Conference of the IEEE EMBS, pp. 5267–5270 (2011)
Lane, N.D., et al.: BeWell: a smartphone application to monitor, model and promote wellbeing. In: 5th ICST/IEEE Conference on Pervasive Computing Technologies for Healthcare, pp. 23–26. IEEE Press (2011)
Rimmele, U., Seiler, R., Wirtz, P.H., Ehlert, U., Heinrichs, M.: The level of physical activity affects adrenal and cardiovascular reactivity to phychosocial stress. Psychoneuroendocrinology 34, 190–198 (2009)
Rusell, A.J.: A circumplex model of affect. J. Pers. Soc. Psychol. 39, 1161–1178 (1980)
Kostopoulos, P., Nunes, T., Salvi, M., Togneri, D., M.: StayActive: an application for Detecting Stress. In: INNOV 2015: The fourth International Conference on Communications, Computation, Networks and Technologies (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Kostopoulos, P., Kyritsis, A.I., Deriaz, M., Konstantas, D. (2017). Stress Detection Using Smart Phone Data. In: Giokas, K., Bokor, L., Hopfgartner, F. (eds) eHealth 360°. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 181. Springer, Cham. https://doi.org/10.1007/978-3-319-49655-9_41
Download citation
DOI: https://doi.org/10.1007/978-3-319-49655-9_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-49654-2
Online ISBN: 978-3-319-49655-9
eBook Packages: Computer ScienceComputer Science (R0)