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Experimental Study of the Stress Level at the Workplace Using an Smart Testbed of Wireless Sensor Networks and Ambient Intelligence Techniques

  • F. Silva
  • Teresa Olivares
  • F. Royo
  • M. A. Vergara
  • C. Analide
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7931)

Abstract

This paper combines techniques of ambient intelligence and wireless sensor networks with the objective of obtain important conclusions to increase the quality of life of people. In particular, we oriented our study to the stress at the workplace, because stress is a leading cause of illness and disease. This article presents a wireless sensor network obtaining information of the environment, a pulse sensor obtaining hear rate values and a complete data analysis applying techniques of ambient intelligence to predict stress from these environment variables and people attributes. Results show promise on the identification of stressful situations as well as stress inference through the use of predictive algorithms.

Keywords

Ambient Intelligence Intelligent Environments Wireless Sensor networks Body Area Networks Environmental Monitoring Stress Detection 

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References

  1. 1.
    Weiser, M.: The Computer for the Twenty-First Century. Scientific American 265(3), 94–104 (1991)CrossRefGoogle Scholar
  2. 2.
    Ullah, S., Higgins, H., Braem, B., Latre, B., Blondia, C., Moerman, I., Saleem, S., Rahman, Z., Kwak, K.S.: A comprehensive survey of Wireless Body Area Networks: on PHY, MAC, and Network layers solutions. Journal of Medical Systems 36(3), 1065–1094 (2012)CrossRefGoogle Scholar
  3. 3.
    Chen, M., Gonzalez, S., Vasilakos, A., Cao, H., Leung, V.C.M.: Body Area Networks: A Survey. Mobile Networks and Applications 16(2), 171–193 (2010)CrossRefGoogle Scholar
  4. 4.
    Cooperating Objects NETwork of Excellence: Recognizing Emotions using Wireless Sensor Networks (2011)Google Scholar
  5. 5.
    World Health Organization: Stress at the workplace (2013), http://www.who.int/occupational_health/topics/stressatwp/en/
  6. 6.
    Brun, J.-P.: Work-related stress: scientific evidence-base of risk factors, prevention and costsGoogle Scholar
  7. 7.
    Choi, J., Ahmed, B., Gutierrez-Osuna, R.: Ambulatory Stress Monitoring with Minimally-Invasive Wearable Sensors. Comput. Sci. and Eng., Texas A&M (2010)Google Scholar
  8. 8.
    Tauseef, M.: Human Emotion Recognition Using Smart Sensors. Ph.D. dissertation, Massey University (2012)Google Scholar
  9. 9.
    Acampora, G., Loia, V.: A proposal of ubiquitous fuzzy computing for Ambient Intelligence. Inf. Sci. 178(3), 631–646 (2008)CrossRefGoogle Scholar
  10. 10.
    Liu, W., Lian, Z., Liu, Y.: Heart rate variability at different thermal comfort levels. European Journal of Applied Physiology 103(3), 361–366 (2008)CrossRefGoogle Scholar
  11. 11.
    Paola, A.D., Gaglio, S., Re, G.L., Ortolani, M.: Sensor 9 k: A testbed for designing and experimenting with WSN-based ambient intelligence applications. Pervasive and Mobile Computing 8(3), 448–466 (2012)CrossRefGoogle Scholar
  12. 12.
    R Core Team: R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria (2012), http://www.r-project.org/
  13. 13.
    Hall, M., National, H., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. SIGKDD Explor. Newsl. 11(1), 10–18 (2009)CrossRefGoogle Scholar
  14. 14.
    Ortiz, A., Royo, F., Galindo, R., Olivares, T.: I3ASensorBed: a testbed for wireless sensor networks. Tech. Rep. (2011)Google Scholar
  15. 15.
  16. 16.
    Fanger, P.O.: Thermal comfort: Analysis and applications in environmental engineering. Danish Technical Press (1970)Google Scholar
  17. 17.
    Höppe, P.: The physiological equivalent temperature - a universal index for the biometeorological assessment of the thermal environment. International Journal of Biometeorology 43(2), 71–75 (1999)CrossRefGoogle Scholar
  18. 18.
    Quazi, M., Mukhopadhyay, S.: Continuous monitoring of physiological parameters using smart sensors. In: 2011 Fifth International Conference on Sensing Technology, pp. 464–469 (November 2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • F. Silva
    • 1
  • Teresa Olivares
    • 2
  • F. Royo
    • 2
  • M. A. Vergara
    • 2
  • C. Analide
    • 1
  1. 1.University of MinhoBragaPortugal
  2. 2.Albacete Research Institute of InformaticsUniversity of Castilla-La ManchaAlbaceteSpain

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