Activity-Aware Mental Stress Detection Using Physiological Sensors

  • Feng-Tso Sun
  • Cynthia Kuo
  • Heng-Tze Cheng
  • Senaka Buthpitiya
  • Patricia Collins
  • Martin Griss
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 76)


Continuous stress monitoring may help users better understand their stress patterns and provide physicians with more reliable data for interventions. Previously, studies on mental stress detection were limited to a laboratory environment where participants generally rested in a sedentary position. However, it is impractical to exclude the effects of physical activity while developing a pervasive stress monitoring application for everyday use. The physiological responses caused by mental stress can be masked by variations due to physical activity.

We present an activity-aware mental stress detection scheme. Electrocardiogram (ECG), galvanic skin response (GSR), and accelerometer data were gathered from 20 participants across three activities: sitting, standing, and walking. For each activity, we gathered baseline physiological measurements and measurements while users were subjected to mental stressors. The activity information derived from the accelerometer enabled us to achieve 92.4% accuracy of mental stress classification for 10-fold cross validation and 80.9% accuracy for between-subjects classification.


Mental stress electrocardiogram galvanic skin response physical activity heart rate variability decision tress Bayes net support vector machine stress classifier 


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  1. 1.
    Bernardi, L., et al.: Physical activity influences heart rate variability and very-low-frequency components in holster electrocardiograms. Cardiovascular Research 32(2), 234 (1996)CrossRefGoogle Scholar
  2. 2.
    Boucsein, H.: Electrodermal Activity. Plenum, New York (1992)CrossRefGoogle Scholar
  3. 3.
    Breslau, N., Kessler, R., Peterson, E.L.: Post-traumatic stress disorder assessment with a structured interview: reliability and concordance with a standardized clinical interview. International Journal of Methods in Psychiatric Research 7(3), 121–127 (1998)CrossRefGoogle Scholar
  4. 4.
    Burges, C.J.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)CrossRefGoogle Scholar
  5. 5.
    Cannon, W.: Bodily Change in Pain, Hunger, Fear and Rage: An Account of Recent Research into the Function of Emotional Excitement. Appleton, New York (1915)Google Scholar
  6. 6.
    Darrow, C.: The rationale for treating the change in galvanic skin response as a change in conductance. Psychophysiology 1, 31–38 (1964)CrossRefGoogle Scholar
  7. 7.
    Dedovic, K., Renwick, R., Mahani, N.K., Engert, V., Lupien, S.J., Pruessner, J.C.: The Montreal imaging stress task: Using functional imaging to investigate the effects of perceiving and processing psychosocial stress in the human brain. Journal Psychiatry Neuroscience 30, 319–325 (2005)Google Scholar
  8. 8.
    Dubin, D.: Rapid Interpretation of EKG’s. Cover Publishing (2000)Google Scholar
  9. 9.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. SIGKDD Explorations 11 (2009)Google Scholar
  10. 10.
    Healey, J.A., Picard, R.W.: Detecting stress during real-world driving tasks using physiological sensors. IEEE Transactions on Intelligent Transportation Systems 6(2), 156–166 (2005)CrossRefGoogle Scholar
  11. 11.
    Herbert, J.: Fortnightly review: Stress, the brain, and mental illness. British Medical Journal 315, 530–535 (1997)CrossRefGoogle Scholar
  12. 12.
    Holmes, S., Krantz, D.S., Rogers, H., Gottdiener, J., Contrad, R.J.: Mental stress and coronary artery disease: A multidisciplinary guide. Progress in Cardiovascular Disease 49, 106–122 (2006)CrossRefGoogle Scholar
  13. 13.
    Kusserow, M., Amft, O., Troster, G.: Psychophysiological body activation characteristics in daily routines. In: IEEE International Symposium on Wearable Computers, pp. 155–156 (2009)Google Scholar
  14. 14.
    Lim, T.-S., Loh, W.-Y., Cohen, W.: A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Machine Learning 39 (2000)Google Scholar
  15. 15.
    Lundberg, U., et al.: Psychophysiological stress and EMG activity of the trapezius muscle. International Journal of Behavioral Medicine 1(4), 354–370 (1994)CrossRefGoogle Scholar
  16. 16.
    Monnikes, H., Tebbe, J., Hildebrandt, M., et al.: Role of stress in functional gastrointestinal disorders. Digestive Diseases 19, 201–211 (2001)CrossRefGoogle Scholar
  17. 17.
    T.F. of the European Society of Cardiology, The North American Society of Pacing, and Electrophysiology. Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. European Heart Journal 17(2), 1043–1065 (1996)Google Scholar
  18. 18.
    Olguin, D., Pentland, Y.: Human activity recognition: Accuracy across common locations for wearable sensors. In: Proc. 10th International Symposium Wearable Computer, pp. 11–13 (2006)Google Scholar
  19. 19.
    Pan, J., Tompkins, W.J.: A real-time QRS detection algorithm. IEEE Transactions on Biomedical Engineering BME-32(3), 230–236 (1985)Google Scholar
  20. 20.
    Picard, R.W., Vyzas, E., Healey, J.: Toward machine emotional intelligence: Analysis of affective physiological state. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(10), 1175–1191 (2001)CrossRefGoogle Scholar
  21. 21.
    Pichon, A., Bisschop, C., Roulaud, M., et al.: Spectral analysis of heart rate variability during exercise in trained subjects. Medicine and Science in Sports and Exercise 36, 1702–1708 (2004)CrossRefGoogle Scholar
  22. 22.
    Pickering, T.: Mental stress as a causal factor in the development of hypertension and cardiovascular disease. Current Hypertension Report 3(3), 249–254 (2001)CrossRefGoogle Scholar
  23. 23.
    Salahuddin, L., Cho, J., Jeong, M.G., Kim, D.: Ultra short term analysis of heart rate variability for monitoring mental stress in mobile settings. In: 29th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society, EMBS 2007, pp. 4656–4659 (August 2007)Google Scholar
  24. 24.
    Schumm, J., Bächlin, M., Setz, C., Arnrich, B., Roggen, D., Tröster, G.: Effect of movements on the electrodermal response after a startle event. In: Proceedings of 2nd International Conference on Pervasive Computing Technologies for Healthcare, Pervasive Health (2008)Google Scholar
  25. 25.
    Setz, C., Arnrich, B., Schumm, J., La Marca, R., Troster, G., Ehlert, U.: Discriminating stress from cognitive load using a wearable EDA device. IEEE Transactions on Information Technology in Biomedicine 14(2), 410–417 (2010)CrossRefGoogle Scholar
  26. 26.
    Sloten, J.V., Verdonck, P., Nyssen, M., Haueisen, J.: Influence of mental stress on heart rate and heart rate variability. In: International Federation for Medical and Biological Engineering Proceedings, pp. 1366–1369 (2008)Google Scholar
  27. 27.
    Sriram, J.C., Shin, M., Choudhury, T., Kotz, D.: Activity-aware ECG-based patient authentication for remote health monitoring. In: ICMI-MLMI 2009: Proceedings of the 2009 International Conference on Multimodal Interfaces, pp. 297–304. ACM, New York (2009)CrossRefGoogle Scholar
  28. 28.
    Stroop, J.: Studies of interference in serial verbal reactions. Journal of Experimental Psychology 18, 643–661 (1935)CrossRefGoogle Scholar
  29. 29.
    Tarvainen, M., Koistinen, A., Valkonen-Korhonen, M., Partanen, J., Karjalainen, P.: Analysis of galvanic skin responses with principal components and clustering techniques. IEEE Transactions on Biomedical Engineering 48(10), 1071–1079 (2001)CrossRefGoogle Scholar
  30. 30.
    Van Steenis, H., Tulen, J.: The effects of physical activities on cardiovascular variability in ambulatory situations. In: Proceedings of the 19th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society, vol. 1, pp. 105–108 (November 1997)Google Scholar
  31. 31.
    Vrijkotte, T., et al.: Effects of work stress on ambulatory blood pressure, heart rate, and heart rate variability. Hypertension 35(4), 880–886 (2000)CrossRefGoogle Scholar
  32. 32.
    Wilhelm, F.H., Pfaltz, M.C., Grossman, P., Roth, W.T.: Distinguishing emotional from physical activation in ambulatory psychophysiological monitoring. Biomedical Sciences Instrumentation 42, 458–463 (2006)Google Scholar
  33. 33.
    Zhai, J., Barreto, A.: Stress detection in computer users based on digital signal processing of noninvasive physiological variables. In: 28th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society, EMBS 2006, pp. 1355–1358 (September 2006)Google Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2012

Authors and Affiliations

  • Feng-Tso Sun
    • 1
  • Cynthia Kuo
    • 1
    • 2
  • Heng-Tze Cheng
    • 1
  • Senaka Buthpitiya
    • 1
  • Patricia Collins
    • 1
  • Martin Griss
    • 1
  1. 1.Carnegie Mellon UniversityUSA
  2. 2.Nokia Research CenterUSA

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