Abstract
Previous research has indicated that physiological signals can be used to detect mental stress. There is however no consensus on the optimal algorithm for this detection. The aim of this study is to compare different machine learning techniques for the measurement of stress based on physiological responses in a controlled environment. Electrocardiogram (ECG), galvanic skin response (GSR), temperature and respiration were measured during a laboratory stress test. Six machine learning techniques were investigated using a general and personal approach. The results show that personalized dynamic Bayesian networks and generalized support vector machines render the best average classification results with 84.6 % and 82.7 % respectively.
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
Kawakami, N., Haratani, T.: Epidemiology of job stress and health in Japan: review of current evidence and future direction. Ind. Health 37, 174–186 (1999)
Bakker, A., Demerouti, E.V.W.: Using the job demands-resources model to predict burnout and performance. Hum. Resour. Manag. 43, 83–104 (2004)
Hoehn, T., Braune, S., Scheibe, G., Albus, M.: Physiological, biochemical and subjective parameters in anxiety patients with panic disorder during stress exposure as compared with healthy controls. Eur. Arch. Psychiatry Clin. Neurosci. 247, 264–274 (1997)
Karthikeyan, P., Muragappan, M., Yaacob, S.: Analysis of Stroop Color-Word test-based human stress detection using electrocardiography and heart rate variability signals. Arab. J. Sci. Eng. 39, 1835–1847 (2014)
Wijsman, J., Grundlehner, B., Hermens, H.: Wearable physiological sensors reflect mental stress state in office-like situations. In: Humaine Association Conference on Affective Computing and Intelligent Interaction (2013)
Healey, J., Picard, R.: Detecting stress during real-world driving tasks using physiological sensors. IEEE Trans. Intell. Transp. Sig. 6, 156–166 (2005)
Zhai, J., Barreto, A.: Stress detection in computer users based on digital signal processing of noninvasive physiological variables. In: Proceedings of the 28th IEEE EMBS Annual International Conference, pp. 1355–1358 (2006)
Sano, A., Picard, R.: Stress recognition using wearable sensors and mobile phones. In: Humaine Association Conference on Affective Computing and Intelligent Interaction (2013)
Rigas, G., Goletsis, Y., Fotiadis, D.: Real-time driver’s stress event detection. IEEE Trans. Intell. Transp. Syst. 13(1), 221–234 (2012)
Liao, W., Zhang, W., Zhu, Z., Ji, Q.: A real-time human stress monitoring system using dynamic Bayesian networks. In: Computer Society Conference on Computer Vision and Pattern Recognition (2005)
Sharma, N., Gedeon, T.: Objective measures, sensors and computational techniques for stress recognition and classification: a survey. Comput. Methods Programs Biomed. 108, 1287–1301 (2012)
Stoney, C., Davis, M., Matthews, K.: Sex differences in physiological responses to stress and in coronary heart disease: a causal link? Psychophysiology 24(2), 127–131 (1987)
Van der Elst, W., Van Boxtel, P., Van Breukelen, J., Jolles, J.: The Stroop Color-Word test: influence of age, sex, and education; and normative data for a large sample across the adult age range. Assessment 13, 62–79 (2006)
Brown, L., Grundlehner, B., van de Molengraft, J., Penders, J., Gyselinckx, B.: Body area network for monitoring autonomic nervous system responses. In: Pervasive Computing Technologies for Healthcare (2009)
Singh, R., Conjeti, S., Banerjee, R.: A comparative evaluation of neural network classifiers for stress level analysis of automotive drivers using physiological signals. Biomed. Sig. Process. Control 8, 740–754 (2013)
Karthikeyan, P., Murugappan, M., Yaacob, S.: Multiple physiological signal-based human stress identification using non-linear classifiers. Elektronika ir elektrotechnika 19(7), 80–85 (2013)
Hayes, A., Matthes, J.: Computational procedures for probing interactions in OLS and logistic regression: SPSS and SAS implementations. Behav. Res. Methods 41(3), 924–936 (2009)
Burges, C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Disc. 2(2), 121–167 (1998)
Murthy, S.: Automatic construction of decision trees from data: a multi-disciplinary survey. Data Min. Knowl. Disc. 2(4), 345–389 (1998)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Mach. Learn. 29(2), 131–163 (1997)
Mezzacappa, E., Kelsey, R., Katkin, E., Sloan, R.: Vagal rebound and recovery from psychological stress. Psychosom. Med. 63(4), 650–657 (2001)
Giakoumis, D., Tzovaras, D., Hassapis, G.: Subject-dependent biosignal features for increased accuracy in psychological stress detection. Int. J. Hum.-Comput. Stud. 71, 425–439 (2013)
Acknowledgements
Authors acknowledge the Institute for the Promotion of Innovation through Science and Technology in Flanders (IWT-Vlaanderen, Brussels, Belgium) for financial support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Smets, E. et al. (2016). Comparison of Machine Learning Techniques for Psychophysiological Stress Detection. In: Serino, S., Matic, A., Giakoumis, D., Lopez, G., Cipresso, P. (eds) Pervasive Computing Paradigms for Mental Health. MindCare 2015. Communications in Computer and Information Science, vol 604. Springer, Cham. https://doi.org/10.1007/978-3-319-32270-4_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-32270-4_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-32269-8
Online ISBN: 978-3-319-32270-4
eBook Packages: Computer ScienceComputer Science (R0)