Skip to main content

Advertisement

SpringerLink
Log in

Off-line and On-line Stress Detection Through Processing of the Pupil Diameter Signal

  • Published:
Annals of Biomedical Engineering Aims and scope Submit manuscript

Abstract

The pupil diameter (PD), controlled by the autonomic nervous system, seems to provide a strong indication of affective arousal, as found by previous research, but it has not been investigated fully yet. In this study, new approaches based on monitoring and processing the PD signal for off-line and on-line “relaxation” vs. “stress” differentiation are proposed. For the off-line approach, wavelet denoising, Kalman filtering, data normalization, and feature extraction are sequentially utilized. For the on-line approach, a hard threshold, a moving average window and three stress detection steps are implemented. In order to use only the most reliable data, two types of data selection methods (paired t test based on galvanic skin response (GSR) data and subject self-evaluation) are applied, achieving average classification accuracies up to 86.43 and 87.20% for off-line and 72.30 and 73.55% for on-line algorithms, with each set of selected data, respectively. The GSR was also monitored and processed in our experiments for comparison purposes, with the highest classification rate achieved being only 63.57% (based on the off-line processing algorithm). The overall results show that the PD signal is more effective and robust for differentiating “relaxation” vs. “stress,” in comparison with the traditionally used GSR signal.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9

Similar content being viewed by others

References

  1. Andreassi, J. L. Psychophysiology: Human Behavior & Physiological Response. Mahwah, NJ: Lawrence Erlbaum Associates, 2007.

    Google Scholar 

  2. Andren, J., and P. Funk. A case-based approach using behavioural biometrics to determine a user’s stress level. In: ICCBR Workshops, 2005, pp. 9–17.

  3. Beatty, J., and B. Lucero-Wagoner. Handbook of Psychophysiology. Cambridge: Cambridge University Press, 2000.

    Google Scholar 

  4. Begum, S., M. U. Ahmed, P. Funk, and N. Xiong. Using calibration and fuzzification of cases for improved diagnosis and treatment of stress. In: 8th European Conference on Case-Based Reasoning Workshop Proceedings, edited by M. Minor, 2006, pp. 113–122.

  5. Cano-Vindel, A., J. J. Miguel-Tobal, H. Gonzalez-Ordi, and I. Iruarrizaga-Diez. Hyperventilation and anxiety experience. Anxiety Stress 13(2–3):291–302, 2007.

    Google Scholar 

  6. Fellous, M., and M. A. Arbib. Who Needs Emotions? The Brain Meets the Robot. New York: Oxford University Press, 2005.

    Book  Google Scholar 

  7. Field, A. Discovering Statistics Using SPSS (3rd ed.). New York: Sage, 2009.

    Google Scholar 

  8. Gao, Y., A. Barreto, and M. Adjouadi. Monitoring and Processing of the Pupil Diameter Signal for Affective Assessment of a Computer User. Lecture Notes in Computer Science (LNCS). LNCS 5610, 2009, pp. 49–58.

  9. Gao, Y., A. Barreto, and M. Adjouadi. Affective assessment of a computer user through the processing of the pupil diameter signal. In: Innovations in Computing Sciences and Software Engineering, edited by T. Sobh, and K. Elleithy. New York: Springer, 2010, pp. 189–194.

    Chapter  Google Scholar 

  10. Granholm, E., and S. R. Steinhauer. Introduction: Pupillometric measures of cognitive and emotional processing. Int. J. Psychophysiol. 52:1–6, 2004.

    Article  PubMed  Google Scholar 

  11. Grewal, M. S., and A. P. Andrews. Kalman Filtering: Theory and Practice Using MATLAB (3rd ed.). Hoboken, NJ: Wiley-IEEE Press, 2008.

    Book  Google Scholar 

  12. Healey, J. Wearable and Automotive Systems for Affect Recognition from Physiology. Ph.D. dissertation, MIT Media Lab, 2000.

  13. Healey, J. A., and R. W. Picard. Detecting stress during real-world driving tasks using physiological sensors. IEEE Trans. Intell. Transp. Syst. 6(2):156–166, 2005.

    Article  Google Scholar 

  14. Hjemdahl, P., U. Freyschuss, A. Juhlin-Dannfelt, and B. Linde. Differentiated sympathetic activation during mental stress evoked by the Stroop test. Acta Physiol. Scand. Suppl. 527:25–29, 1984.

    CAS  PubMed  Google Scholar 

  15. Holmqvist, K., M. Nystrom, R. Andersson, R. Dewhurst, H. Jarodzka, and J. Weijer. Eye Tracking: A Comprehensive Guide to Methods and Measures. Oxford: Oxford University Press, 2011.

    Google Scholar 

  16. Klingner, J. The pupillometric precision of a remote video eye tracker. In: Proceedings of the 2010 Symposium on Eye-Tracking Research Applications (ETRA ‘10). New York: ACM, 2010, pp. 259–262.

  17. Lim, C. L., C. Rennie, R. J. Barry, H. Bahramali, I. Lazzaro, and B. Manor. Decomposing skin conductance into tonic and phasic components. Int. J. Psychophysiol. 24(2):97–109, 1997.

    Article  CAS  Google Scholar 

  18. Martini, F. H., and J. L. Nath. Fundamentals of Anatomy & Physiology (8th ed.). San Francisco: Benjamin Cummings, 2008.

    Google Scholar 

  19. Morgante, J. D., R. Zolfaghari, and S. P. Johnson. A critical test of temporal and spatial accuracy of the Tobii T60XL eye tracker. Infancy 17:9–32, 2012.

    Article  Google Scholar 

  20. Partala, T., and V. Surakka. Pupil size variation as an indication of affective processing. Int. J. Hum.–Comput. Stud. 59:185–198, 2003.

    Article  Google Scholar 

  21. Picard, W., and J. A. Healey. Wearable and automotive systems for affect recognition from physiology. Technical report, MIT, 2000.

  22. Raymond, J. C. Dictionary of Psychology. New York: Routledge, 1999.

    Google Scholar 

  23. Shi, Y., N. Ruiz, R. Taib, E. Choi, and F. Chen. Galvanic skin response as an index of cognitive load. In: Proceeding of Computer–Human Interaction conference on Human Factors in Computing System, 2007, pp. 2651–2656.

  24. Siegle, G. J., S. R. Steinhauer, and M. E. Thase. Pupillary assessment and computational modeling of early and sustained processing on the Stroop task in depression. Int. J. Psychophysiol. 52:63–76, 2004.

    Article  PubMed  Google Scholar 

  25. Sierra, A. S., C. S. Ávila, J. G. Casanova, and G. B. Pozo. A stress-detection system based on physiological signals and fuzzy logic. IEEE Trans. Ind. Electron. 58(10):4857–4865, 2011.

    Article  Google Scholar 

  26. Sierra, A. S., C. S. Ávila, A. Mendaza-Ormaza, and J. G. Casanova. An approach to hand biometrics in mobile devices. SIViP 5(4):469–475, 2011.

    Article  Google Scholar 

  27. Smith, S. W. The Scientist and Engineer’s Guide to Digital Signal Processing. San Diego, CA: California Technical Publishing, 1997.

    Google Scholar 

  28. Steinhauer, S. R., G. J. Siegle, R. Condray, and M. Pless. Sympathetic and parasympathetic innervation of pupillary dilation during sustained processing. Int. J. Psychophysiol. 52:77–86, 2004.

    Article  PubMed  Google Scholar 

  29. Stern, R. M., W. J. Ray, and K. S. Quigley. Psychophysiological Recording. Oxford: Oxford University Press, 2000.

    Book  Google Scholar 

  30. Stroop, J. R. Studies of interference in serial verbal reactions. J. Exp. Psychol. 18:643–662, 1935.

    Article  Google Scholar 

  31. Sun, F., C. Kuo, H. Cheng, S. Buthpitiya, P. Collins, and M. L. Griss. Activity-Aware Mental Stress Detection Using Physiological Sensors. Silicon Valley Campus, Paper 23, 2010.

  32. Tognetti, S., M. Garbarino, M. Matteucci, and A. Bonarini. The affective triad: stimuli, questionnaires, and measurements. In: Proceeding of ACII’11, Vol. II, 2011, pp. 101–110.

  33. Tourangeau, R., L. J. Rips, and K. A. Rasinski. The Psychology of Survey Response. Cambridge: Cambridge University Press, 2000.

    Book  Google Scholar 

  34. Tsai, S. S. Power Transformer Partial Discharge (PD) Acoustic Signal Detection Using Fiber Sensors and Wavelet Analysis, Modeling, and Simulation. Master’s Thesis, Virginia Polytechnic Institute and State University, Blacksburg, VA, 2002.

  35. Tulen, J., H. P. Moleman, H. G. Steenis, and F. Boomsma. Characterization of stress reactions to the Stroop color word test. Pharmacol. Biochem. Behav. 32(1):9–15, 1989.

    Article  CAS  PubMed  Google Scholar 

  36. University of California-Irvine. Short-term stress can affect learning and memory. ScienceDaily. March, 11, 2008.

  37. Verney, S. P., E. Granholm, and S. P. Marshall. Pupillary responses on the visual backward masking task reflect general cognitive ability. Int. J. Psychophysiol. 52:23–36, 2004.

    Article  PubMed  Google Scholar 

  38. Weigle, C., and D. C. Banks. Analysis of eye-tracking experiments performed on a Tobii T60. In: Proceedings of the SPIE6809, Visualization and Data Analysis, January, 2008.

  39. Zhai, J., and A. Barreto. Stress detection in computer users through noninvasive monitoring of physiological signals. Biomed. Sci. Instrum. 42:495–500, 2006.

    PubMed  Google Scholar 

Download references

Acknowledgments

This work was sponsored by NSF grants HRD-0833093 and CNS-0959985.

Author information

Authors and Affiliations

  1. Biomedical Engineering Department, Florida International University, Miami, FL, 33174, USA

    Peng Ren

  2. Electrical and Computer Engineering Department, Florida International University, Miami, FL, 33174, USA

    Armando Barreto, Jian Huang & Malek Adjouadi

  3. Electrical Engineering Department, University of Wisconsin-Platteville, Platteville, WI, 53818, USA

    Ying Gao

  4. School of Computing and Information Sciences, Florida International University, Miami, FL, 33199, USA

    Francisco R. Ortega

Authors
  1. Peng Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Armando Barreto
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jian Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ying Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Francisco R. Ortega
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Malek Adjouadi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peng Ren.

Additional information

Associate Editor Leonidas D Iasemidis oversaw the review of this article.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ren, P., Barreto, A., Huang, J. et al. Off-line and On-line Stress Detection Through Processing of the Pupil Diameter Signal. Ann Biomed Eng 42, 162–176 (2014). https://doi.org/10.1007/s10439-013-0880-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10439-013-0880-9

Keywords

Search

Navigation