Abstract
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 is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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)
G. de Vries, S.C. Pauws, M. Biehl, Insightful stress detection from physiology modalities using learning vector quantization. Neurocomputing 151, 873–888 (2015)
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)
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)
L.H. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann, 2011)
Acknowledgments
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).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Martínez, R., Abascal, J., Arruti, A., Irigoyen, E., Martín, J.I., Muguerza, J. (2017). A Stress Classification System Based on Arousal Analysis of the Nervous System. In: Ibáñez, J., González-Vargas, J., Azorín, J., Akay, M., Pons, J. (eds) Converging Clinical and Engineering Research on Neurorehabilitation II. Biosystems & Biorobotics, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-319-46669-9_128
Download citation
DOI: https://doi.org/10.1007/978-3-319-46669-9_128
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46668-2
Online ISBN: 978-3-319-46669-9
eBook Packages: EngineeringEngineering (R0)