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Stress Detection Using Wearable Physiological Sensors

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 9107)

Abstract

As the population increases in the world, the ratio of health carers is rapidly decreasing. Therefore, there is an urgent need to create new technologies to monitor the physical and mental health of people during their daily life. In particular, negative mental states like depression and anxiety are big problems in modern societies, usually due to stressful situations during everyday activities including work. This paper presents a machine learning approach for stress detection on people using wearable physiological sensors with the final aim of improving their quality of life. The presented technique can monitor the state of the subject continuously and classify it into ”stressful” or ”non-stressful” situations. Our classification results show that this method is a good starting point towards real-time stress detection.

Keywords

  • Stress detection
  • Wearable physiological sensors
  • Assistive technologies
  • Signal classification
  • Quality of life technologies

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References

  1. Biopac, http://www.biopac.com

  2. Mental Health Foundation in UK, http://www.mentalhealth.org.uk

  3. The Organisation for Economic Co-operation and Development (OECD), http://www.oecd.org

  4. World Health Organization (WHO), http://www.who.org

  5. Beck, A.T.: Cognitive therapy and the emotional disorders. International Universities Press, Inc., Madison (1975)

    Google Scholar 

  6. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer (2006)

    Google Scholar 

  7. Chang, C.-C., Lin, C.-J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm

  8. Cortes, C., Vapnik, V.: Support-vector network. Machine Learning 20, 273–297 (1995)

    MATH  Google Scholar 

  9. Dickerson, S.S., Kemeny, M.E.: Acute stressors and cortisol responses: A theoretical integration and synthesis of laboratory research. Psychological Bulletin 130(3), 355–391 (2004)

    CrossRef  Google Scholar 

  10. Elwood, L.S., Wolitzky-Taylor, K., Olatunji, B.O.: Measurement of anxious traits: a contemporary review and synthesis. Anxiety Stress Coping 25(6), 647–666 (2012)

    CrossRef  Google Scholar 

  11. Hsu, C.-W., Chang, C.-C., Lin, C.-J.: A practical guide to support vector classification (2010), http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf

  12. Kirschbaum, C., Pirke, K.M., Hellhammer, D.H.: The ‘Trier Social Stress Test’ – A tool for investigating psychobiological stress responses in a laboratory setting. Neuropsychobiology, 76–81 (1993)

    Google Scholar 

  13. Wikgren, M., Maripuu, M., Karlsson, T., Nordfjäll, K., Bergdahl, J., Hultdin, J., Del-Favero, J., Roos, G., Nilsson, L.G., Adolfsson, R., Norrback, K.F.: Short telomeres in depression and the general population are associated with a hypocortisolemic state. Biological Psychiatry 71(4), 294–300 (2012)

    CrossRef  Google Scholar 

  14. Peper, E., Harvey, R., Lin, I.-M., Tylova, H., Moss, D.: Is there more to blood volume pulse than heart rate variability, respiratory sinus arrhythmia, and cardiorespiratory synchrony? Biofeedback 35(2), 54–61 (2007)

    Google Scholar 

  15. Perkins, A.: Saving money by reducing stress. Harvard Business Review 72(12) (1994)

    Google Scholar 

  16. Rai, D., Kosidou, K., Lundberg, M., Araya, R., Lewis, G., Magnusson, C.: Psychological distress and risk of long-term disability: population-based longitudinal study. Journal of Epidemiology and Community Health 66(7), 586–592 (2011)

    CrossRef  Google Scholar 

  17. Sun, F.-T., Kuo, C., Cheng, H.-T., Buthpitiya, S., Collins, P., Griss, M.: Activity-aware mental stress detection using physiological sensors. In: Griss, M., Yang, G. (eds.) MobiCASE 2010. LNICST, vol. 76, pp. 211–230. Springer, Heidelberg (2012)

    CrossRef  Google Scholar 

  18. Sung, M., Pentland, A.: PokerMetrics: Stress and Lie Detection through Non-invasive Physiological Sensing. PhD thesis, MIT Media Laboratory (2005)

    Google Scholar 

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Correspondence to Virginia Sandulescu .

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© 2015 Springer International Publishing Switzerland

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Sandulescu, V., Andrews, S., Ellis, D., Bellotto, N., Mozos, O.M. (2015). Stress Detection Using Wearable Physiological Sensors. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo-Moreo, F., Adeli, H. (eds) Artificial Computation in Biology and Medicine. IWINAC 2015. Lecture Notes in Computer Science(), vol 9107. Springer, Cham. https://doi.org/10.1007/978-3-319-18914-7_55

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  • DOI: https://doi.org/10.1007/978-3-319-18914-7_55

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18913-0

  • Online ISBN: 978-3-319-18914-7

  • eBook Packages: Computer ScienceComputer Science (R0)