A Stress Classification System Based on Arousal Analysis of the Nervous System

  • R. Martínez
  • J. Abascal
  • A. Arruti
  • E. Irigoyen
  • J. I. MartínEmail author
  • J. MuguerzaEmail author
Conference paper
Part of the Biosystems & Biorobotics book series (BIOSYSROB, volume 15)


Detection of an increment in stress levels is a step towards improving the quality of people’s lives, especially in the case of people with intellectual disabilities, as they have fewer resources to deal with this situation. This paper presents a biophysical stress classification system that is able to classify the detected stress situations at three intensity levels: low, medium and high. Furthermore, the system distinguishes between continued stress and a momentary alert depending on the subject’s arousal. The system uses two non-invasive physiological signals for the classification: the galvanic skin response and the heart rate variability. The experiment shows that the proposed system is able to detect and classify the different stress states achieving an accuracy of 97.5 % with a 0.9 % FN rate.



This work described in this paper was partially supported by the University of the Basque Country (BAILab, grant UFI11/45); by the Department of Education, Universities and Research (grant IT-395-10); and by the Ministry of Economy and Competitiveness of the Spanish Government and by the European Regional Development Fund—ERDF (eGovernAbility, grant TIN2014-52665-C2-1-R).


  1. 1.
    A. Abellán, C. Esparza, P. Castejón, J. Pérez, Epidemiología de la discapacidad y la dependencia de la vejez en España. Gac. Sanit. 25, 5–11 (2011)CrossRefGoogle Scholar
  2. 2.
    A. De Santos Sierra, C.S. Ávila, J.G. Casanova, G.B.D. Pozo, A stress-detection system based on physiological signals and fuzzy logic. IEEE Trans. Ind. Electron. 58(10), 4857–4865 (2011)Google Scholar
  3. 3.
    G. de Vries, S.C. Pauws, M. Biehl, Insightful stress detection from physiology modalities using learning vector quantization. Neurocomputing 151, 873–888 (2015)CrossRefGoogle Scholar
  4. 4.
    J. Healey, R.W. Picard, Detecting stress during real-world driving tasks using physiological sensors. IEEE Trans. Intell. Transp. Syst. 6(2), 156–166 (2005)CrossRefGoogle Scholar
  5. 5.
    J.F. Thayer, S.S. Yamamoto, J.F. Brosschot, The relationship of autonomic imbalance, heart rate variability and cardiovascular disease risk factors. Int. J. Cardiol. 141(2), 122–131 (2010)CrossRefGoogle Scholar
  6. 6.
    L.H. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann, 2011)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Computer Architecture and Technology Department (UPV/EHU)DonostiaSpain
  2. 2.System Engineering and Automation Department (UPV/EHU)BilbaoSpain

Personalised recommendations