Detection of Stress Level and Phases by Advanced Physiological Signal Processing Based on Fuzzy Logic

  • Unai ZalabarriaEmail author
  • Eloy Irigoyen
  • Raquel Martínez
  • Asier Salazar-RamirezEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 527)


Stress has a big impact in the current society, being the cause or the incentive of several diseases. Therefore, its detection and monitorization has been the focus of a big number of investigations in the last decades. This work proposes the use of physiological variables such as the electrocardiogram (ECG), the galvanic skin response (GSR) and the respiration (RSP) in order to estimate the level and classify the type of stress. On that purpose, an algorithm based on fuzzy logic has been implemented. This computer-intelligent technique has been combined with a structured processing shaped in state machine. This processing classifies stress in 3 different phases or states: alarm, continued stress and relax. An improved estimation of stress level is obtained at the end, considering the last progresses made by different authors. All this is accompanied by stress classification, which is the novelty compared to other works.


Fuzzy logic State machine Stress Physiological signal 



This work has been performed partially thanks to the support of the Foundation Jesús de Gangoiti Barrera, to which we are deeply grateful. It would not have been possible to perform it without the involvement of the biomedical investigation group of GICI, to which we also thank its effort and dedication.


  1. 1.
    Morris, C.G., Maisto, A.A.: Introducción a la Psicología. Pearson Educación, Mexico (2005)Google Scholar
  2. 2.
    Kreibig, S.D.: Autonomic nervous system activity in emotion: A review. Biol. Psychol. 3(3), 394–421 (2010)CrossRefGoogle Scholar
  3. 3.
    Lee, C.K. et al.: Using neural network to recognize human emotions from heart rate variability and skin resistance. In: 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5523–5525 (2006)Google Scholar
  4. 4.
    Porges, S.W.: The polyvagal theory: phylogenetic substrates of a social nervous system. Int. J. Psychophysiol. 42(2), 123–146 (2001)CrossRefGoogle Scholar
  5. 5.
    Bloch, S., et al.: Specific respiratory patterns distinguish among human basic emotions. Int. J. Psychophysiol. 11(2), 141–154 (1991)CrossRefGoogle Scholar
  6. 6.
    Ekman, P., et al.: Autonomic nervous system activity distinguishes among emotions. Science 221(4616), 1208–1210 (1983)CrossRefGoogle Scholar
  7. 7.
    De Rivera, J.G., et al.: La valoración de sucesos vitales: adaptación española de la escala de Holmes y rahe. Psiquis 4(1), 7–11 (1983)Google Scholar
  8. 8.
    De Camargo, B.: Estrés, síndrome general de adaptación o reacción general de alarma. Revista Médico Científica 17(2), 78–86 (2010)Google Scholar
  9. 9.
    Nelson, R.J.: An introduction to behavioral endocrinology. Sinauer Associates, Sunderland (2005)Google Scholar
  10. 10.
    Cacioppo, J.T., et al.: Handbook of Psychophysiology. Cambridge University Press, Cambridge (2007)CrossRefGoogle Scholar
  11. 11.
    Healey, J.A., Picard, R.W.: Detecting stress during real-world driving tasks using physiological sensors. IEEE Trans. Intell. Transp. Syst. 6(2), 156–166 (2005). IEEECrossRefGoogle Scholar
  12. 12.
    Cannon, W.B.: Stresses and strains of homeostasis. Am. J. Med. Sci. 189(1), 13–14 (1935). LWWCrossRefGoogle Scholar
  13. 13.
    Wozniak, M., et al.: A survey of multiple classifier systems as hybrid systems. Inf. Fusion 16, 3–17 (2014). ElsevierCrossRefGoogle Scholar
  14. 14.
    Salazar-Ramirez, A., Irigoyen, E., Martinez, R.: Enhancements for a robust fuzzy detection of stress. In: de la Puerta, J.G., Ferreira, I.G., Bringas, P.G., Klett, F., Abraham, A., de Carvalho, A.C., Herrero, A., Baruque, B., Quintián, H., Corchado, E. (eds.) International Joint Conference SOCO 2014-CISIS 2014-ICEUTE 2014. AISC, vol. 299, pp. 229–238. Springer, Heidelberg (2014)Google Scholar
  15. 15.
    Chang, C.-Y., et al.: Physiological emotion analysis using support vector regression. Neurocomputing 122, 79–87 (2013)CrossRefGoogle Scholar
  16. 16.
    De Santos Sierra, A., et al.: A stress-detection system based on physiological signals and fuzzy logic. IEEE Trans. Ind. Electron. 58(10), 4857–4865 (2011). IEEECrossRefGoogle Scholar
  17. 17.
    Sakr, G.E., et al.: Support vector machines to define and detect agitation transition. IEEE Trans. Affect. Comput. 1(2), 98–108 (2010). IEEECrossRefGoogle Scholar
  18. 18.
    Martinez, R.: Diseño de un sistema de detección y clasificación de cambios emocionales basados en el análisis de señales fisiológicas no intrusivas. University of the Basque Country (2016)Google Scholar
  19. 19.
    Pauws, S.C., et al.: Insightful stress detection from physiology modalities using learning vector quantization. Neurocomputing 151, 873–882 (2015). ElsevierCrossRefGoogle Scholar
  20. 20.
    Subramanya, K., et al.: A wearable device for monitoring galvanic skin response to accurately predict changes in blood pressure indexes and cardiovascular dynamics. In: 2013 Annual IEEE India Conference (INDICON), pp. 1–4 (2013)Google Scholar
  21. 21.
    Martinez, R., et al.: First results in modelling stress situations by analysing physiological human signals. In: Proceedings of IADIS International Conference on e-Health, pp. 171–175 (2012)Google Scholar
  22. 22.
    Zadeh, L.A.: Fuzzy logic = computing with words. IEEE Trans. Fuzzy Syst. 4, 103–111 (1996). IEEECrossRefGoogle Scholar
  23. 23.
    Gross, J.J., Levenson, R.W.: Emotion elicitation using films. Cogn. Emot. 9(1), 87–108 (1995). Taylor & FrancisCrossRefGoogle Scholar
  24. 24.
    Bradley, M.M., Lang, P.J.: Measuring emotion: the self-assessment manikin and the semantic differential. J. Behav. Ther. Exp. Psychiatry 25(1), 49–59 (1994). ElsevierCrossRefGoogle Scholar
  25. 25.
    Zalabarria, U., et al.: Procesamiento robusto para el análisis avanzado de señales electrocardiográficas afectadas por perturbaciones. Actas de las XXXVI Jornadas de Automática, pp. 807–814 (2015)Google Scholar
  26. 26.
    Bari, V., et al.: Nonlinear effects of respiration on the crosstalk between cardiovascular and cerebrovascular control systems. Phil. Trans. R. Soc. A 374(2067), 20150179 (2016). The Royal SocietyCrossRefGoogle Scholar
  27. 27.
    Porta, A., et al.: Conditional symbolic analysis detects nonlinear influences of respiration on cardiovascular control in humans. Phil. Trans. R. Soc. A 373(2034), 1–21 (2015). The Royal Society MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (, which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Authors and Affiliations

  1. 1.University of the Basque Country (UPV/EHU)BilbaoSpain

Personalised recommendations