Skip to main content
SpringerLink
Log in

Stress Recognition in Daily Work

  • Conference paper
  • First Online:
Pervasive Computing Paradigms for Mental Health (MindCare 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 604))

Abstract

Automatic detection of work-related stress has attracted an increasing amount of attention from researchers from various disciplines and industries. An experiment is discussed in this paper that was designed to evaluate the efficacy of multimodal sensor measures that have often been used but not yet been systematically tested and compared with each other in previous work, such as pressure distribution sensor, physiological sensors, and an eye tracker. We used the Stroop test and information pick up task as the stressors. In the subject independent case in particular, signals from the combined (chair and floor) pressure distribution sensors, which we consider the most feasible sensors in the office environment, resulted in higher recognition accuracy rates than the physiological or eye tracker signals for the two stressors.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Demerouti, E., Bakker, A.B., Nachreiner, F., Schaufeli, W.B.: The job demands-resources model of burnout. J. Appl. Psychol. 86(3), 499–512 (2001)

    Article  Google Scholar 

  2. Grafsgaard, J.F., Boyer, K.E., Wiebe, E.N., Lester, J.C.: Analyzing posture and affect in task-oriented tutoring. In: 25th International Florida Artificial Intelligence Research Society Conference (2012)

    Google Scholar 

  3. Lang, P.J., Bradley, M.M., Cuthbert, B.N.: Emotion, motivation, and anxiety: brain mechanisms and psychophysiology. Biol. Psychiatry 44(12), 1248–1263 (1998)

    Article  Google Scholar 

  4. Arnrich, B., Setz, C., La Marca, R., Tröster, G., Ehlert, U.: What does your chair know about your stress level? IEEE Trans. Inf. Technol. Biomed. 14(2), 207–214 (2009)

    Article  Google Scholar 

  5. Frank, K., Robertson, P., Gross, M., Wiesner, K.: Sensor-based identification of human stress levels. In: International Conference on Pervasive Computing and Communications Workshops, pp. 127–132 (2013)

    Google Scholar 

  6. Calibo, T.K., Blanco, J.A., Firebaugh, S.L.: Cognitive stress recognition. In: Instrumentation and Measurement Technology Conference, pp. 1471–1474 (2013)

    Google Scholar 

  7. Meyer, J., Arnrich, B., Schumm, J., Tröster, G.: Design and modeling of a textile pressure sensor for sitting posture classification. IEEE Sens. J. 10(8), 1391–1398 (2010)

    Article  Google Scholar 

  8. Hernandez, J., Paredes, P., Roseway, A., Czerwinski, M.: Under pressure: sensing stress of computer users. In: SIGCHI Conference on Human Factors in Computing Systems, pp. 51–60 (2014)

    Google Scholar 

  9. Hart, S.G., Staveland, L.E.: Development of NASA-TLX (Task Load Index): results of empirical and theoretical research. Adv. Psychol. 52, 139–183 (1998)

    Article  Google Scholar 

  10. Giraud, T., Soury, M., Hua, J., Delaborde, A., Tahon, M., Antonio, D., Jauregui, G., Eyharabide, V., Filaire, E., Le Scanff, C., Devillers, L., Isableu B., Martin, J.C.: Multimodal expressions of stress during a public speaking task. In: 5th Biannual Conference of the Humaine-Association on Affective Computing and Intelligent Interaction, pp. 417–422 (2013)

    Google Scholar 

  11. Plarre, K., Raij, A., Hossain, S.M., Ahsan Ali, A., Nakajima, M., al’Absi, M., Ertin, E., Kamarck, T., Kumar, S., Scott, M., Siewiorek, D., Smailagic, A., Wittmers, L.E., Jr.: Continuous inference of psychological stress from sensory measurements collected in the natural environment. In: 10th International Conference of Information Processing in Sensor Networks, pp. 97–108 (2011)

    Google Scholar 

  12. Renaud, P., Blondin, J.P.: The stress of Stroop performance: physiological and emotional responses to color word interference, task pacing, and pacing speed. Int. J. Psychophysiol. 27(2), 87–92 (1997)

    Article  Google Scholar 

  13. Zhai, J., Barreto, A.: Stress detection in computer users based on digital signal processing of noninvasive physiological variables. In: 28th Annual Conference of IEEE Engineering in Medicine and Biology Society, vol. 1–15, pp. 1999–2002 (2006)

    Google Scholar 

  14. Di Stasi, L.L., Catenad, A., Cañasc, J.J., Macknike, S.L., Martinez-Condea, S.: Saccadic velocity as an arousal index in naturalistic tasks. Neurosci. Biobehav. Rev. 37(5), 968–975 (2013)

    Article  Google Scholar 

  15. SensingTex. http://sensingtex.com/

  16. Wild Divine. http://www.wilddivine.com/

  17. The EyeTribe. https://theeyetribe.com/

  18. Wagner, J., Lingenfelser, F., André, E.: The social signal interpretation framework (SSI) for real time signal processing and recognition. In: 12th Annual Conference of the International Speech Communication Association, vol. 1–5, pp. 3252–3255 (2011)

    Google Scholar 

  19. Jackson, M.D., McClelland, J.L.: Sensory and cognitive determinants of reading speed. J. Verbal Learn. Verbal Behav. 14(6), 565–574 (1975)

    Article  Google Scholar 

  20. Picard, R.W., Vyzas, E., Healey, J.: Toward machine emotional intelligence: analysis of affective physiological state. IEEE Trans. Pattern Anal. Mach. Intell. 23(10), 1175–1191 (2001)

    Article  Google Scholar 

  21. Peterka, R.J., Loughlin, P.J.: Dynamic regulation of sensorimotor integration in human postural control. J. Neurophysiol. 91(1), 410–423 (2004)

    Article  Google Scholar 

  22. Krafczyk, S., Schlamp, V., Dieterich, M., Haberhauer, P., Brandt, T.: Increased body sway at 3.5–8 Hz in patients with phobic postural vertigo. Neurosci. Lett. 259(3), 149–152 (1998)

    Article  Google Scholar 

  23. Nodine, C.F., Kundel, H.L., Toto, L.C., Krupinski, E.A.: Recording and analyzing eye-position data using a microcomputer workstation. Behav. Res. Methods Instrum. Comput. 24(3), 475–485 (1992)

    Article  Google Scholar 

  24. Manor, B.R., Gordon, E.: Defining the temporal threshold for ocular fixation in free-viewing visuocognitive tasks. J. Neurosci. Methods 128(1–2), 85–93 (2003)

    Article  Google Scholar 

  25. Zhai, J., Barreto, A.B., Chin, C., Li, C.: Realization of stress detection using psychophysiological signals for improvement of human-computer interaction. In: Proceedings of IEEE SoutheastCon, pp. 415–420 (2005)

    Google Scholar 

  26. Mokhayeri, F., Akbarzadeh, M.R., Toosizadeh, T, S.: Mental stress detection using physiological signals based on soft computing techniques. In: 18th Iranian Conference of Biomedical Engineering (ICBME), pp. 232–237 (2011)

    Google Scholar 

  27. Koldijk, S., Sappelli, M., Verberne, S., Neerincx, M.A., Kraaij, W.: The SWELL knowledge work dataset for stress and user modeling research. In: 16th International Conference of Multimodal Interaction, pp. 291–298 (2014)

    Google Scholar 

  28. Kim, J.: Bimodal emotion recognition using speech and physiological changes. In: Grimm, M., Kroschel, K. (eds.) Robust Speech Recognition and Understanding, pp. 265–280. I-Tech Education and Publishing, Vienna (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Smart Energy Research Laboratories, NEC Corporation, 34 Miyukigaoka, Tsukuba, Ibaraki, 305-8501, Japan

    Yoshiki Nakashima

  2. Institute of Computer Science, University of Augsburg, Universitätsstraße 6a, 86159, Augsburg, Germany

    Jonghwa Kim, Simon Flutura, Andreas Seiderer & Elisabeth André

Authors
  1. Yoshiki Nakashima
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jonghwa Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Simon Flutura
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andreas Seiderer
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Elisabeth André
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yoshiki Nakashima .

Editor information

Editors and Affiliations

  1. Istituto Auxologico Italiano, Milano, Italy

    Silvia Serino

  2. Telefonica I+D, Barcelona, Spain

    Aleksandar Matic

  3. The Centre for Research and Technology, Thessalomiki/Thermi, Greece

    Dimitris Giakoumis

  4. Aoyama Gakuin University, Tokyo, Japan

    Guillaume Lopez

  5. Istituto Auxologico Italiano, Milano, Italy

    Pietro Cipresso

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Nakashima, Y., Kim, J., Flutura, S., Seiderer, A., André, E. (2016). Stress Recognition in Daily Work. 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_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-32270-4_3

  • 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)

Publish with us

Policies and ethics

Search

Navigation