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Personal and Ubiquitous Computing

, Volume 17, Issue 2, pp 229–239 | Cite as

Monitoring of mental workload levels during an everyday life office-work scenario

  • Burcu CinazEmail author
  • Bert Arnrich
  • Roberto La Marca
  • Gerhard Tröster
Original Article

Abstract

Personal and ubiquitous healthcare applications offer new opportunities to prevent long-term health damage due to increased mental workload by continuously monitoring physiological signs related to prolonged high workload and providing just-in-time feedback. In order to achieve a quantification of mental load, different load levels that occur during a workday have to be discriminated. In this work, we present how mental workload levels in everyday life scenarios can be discriminated with data from a mobile ECG logger by incorporating individual calibration measures. We present an experiment design to induce three different levels of mental workload in calibration sessions and to monitor mental workload levels in everyday life scenarios of seven healthy male subjects. Besides the recording of ECG data, we collect subjective ratings of the perceived workload with the NASA Task Load Index (TLX), whereas objective measures are assessed by collecting salivary cortisol. According to the subjective ratings, we show that all participants perceived the induced load levels as intended from the experiment design. The heart rate variability (HRV) features under investigation can be classified into two distinct groups. Features in the first group, representing markers associated with parasympathetic nervous system activity, show a decrease in their values with increased workload. Features in the second group, representing markers associated with sympathetic nervous system activity or predominance, show an increase in their values with increased workload. We employ multiple regression analysis to model the relationship between relevant HRV features and the subjective ratings of NASA-TLX in order to predict the mental workload levels during office-work. The resulting predictions were correct for six out of the seven subjects. In addition, we compare the performance of three classification methods to identify the mental workload level during office-work. The best results were obtained with linear discriminant analysis (LDA) that yielded a correct classification for six out of the seven subjects. The k-nearest neighbor algorithm (k-NN) and the support vector machine (SVM) resulted in a correct classification of the mental workload level during office-work for five out of the seven subjects.

Keywords

Personal and ubiquitous healthcare Mental workload Office-work Heart rate variability Stress 

References

  1. 1.
    Arnrich B, Setz C, La Marca R, Tröster G, Ehlert U (2010) What does your chair know about your stress level? IEEE Trans Info Technol Biomed Affect Perv Comput HealthcGoogle Scholar
  2. 2.
    Brain workshop—a dual n-back game. http://brainworkshop.sourceforge.net/
  3. 3.
    Chang C–C, Lin C-J (2011) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol 2:27:1–27:27Google Scholar
  4. 4.
    Cinaz B, La Marca R, Arnrich B, Tröster G (2010) Monitoring of mental workload levels. In: Proceedings of IADIS eHealth conferenceGoogle Scholar
  5. 5.
    Clifford GD (2002) Signal processing methods for heart rate variability analysis. PhD thesis, St Cross CollegeGoogle Scholar
  6. 6.
    Dickerson SS, Kemeny ME (2004) Acute stressors and cortisol responses: a theoretical integration and synthesis of laboratory research. Psychol Bull 130:355–391CrossRefGoogle Scholar
  7. 7.
    Dressendörfer RA, Kirschbaum C, Rohde W, Stahl F, Strasburger CJ (1992) Synthesis of a cortisol-biotin conjugate and evaluation as a tracer in an immunoassay for salivary cortisol measurement. J Steroid Biochem Mol Biol 43(7):683–692CrossRefGoogle Scholar
  8. 8.
    European Foundation for the Improvement of Living and Working Conditions (2007) Work-related stress. http://www.eurofound.europa.eu/
  9. 9.
    Hart SG, Stavenland LE (1988) Development of NASA-TLX (task load index): results of empirical and theoretical research. In: Hancock PA, Meshkati N (eds) Human mental workload, chapter 7. Elsevier, Amsterdam, pp 139–183CrossRefGoogle Scholar
  10. 10.
    Henelius A, Hirvonen K, Holm A, Korpela J, Muller K (2009) Mental workload classification using heart rate metrics. Conf Proc IEEE Eng Med Biol Soc 1:1836–1839Google Scholar
  11. 11.
    Jaeggi SM, Buschkuehl M, Jonides J, Perrig WJ (2008) Improving fluid intelligence with training on working memory. Proc Natl Acad Sci USA 105:6829–6833CrossRefGoogle Scholar
  12. 12.
    Kim D, Seo Y, Salahuddin L (2008) Decreased long term variations of heart rate variability in subjects with higher self reporting stress scores. Perv HealthcGoogle Scholar
  13. 13.
    Kim D, Seo Y, Cho J, Cho C-H (2008) Detection of subjects with higher self-reporting stress scores using heart rate variability patterns during the day. Conference Proceedings IEEE Engineering in Medicine and Biology Society, pp 682–685Google Scholar
  14. 14.
    Kirschbaum C, Kudielka BM, Gaab J, Schommer NC, Hellhammer DH (1999) Impact of gender, menstrual cycle phase, and oral contraceptives on the activity of the hypothalamus-pituitary-adrenal axis. Psychosom Med 61:154–162Google Scholar
  15. 15.
    Kramer AF (1991) Physiological metrics of mental workload: a review of recent progress. Multiple-task performance, pp 279–328Google Scholar
  16. 16.
    Marca RL, Waldvogel P, Thorn H, Tripod M, Wirtz PH, Pruessner JC, Ehlert U (2010) Association between cold face test-induced vagal inhibition and cortisol response to acute stress. PsychophysiologyGoogle Scholar
  17. 17.
    Marek M, Bigger JT, Camm AJ, Kleiger RE, Malliani A, Moss AJ, Schwartz PJ (1996) Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Circulation 93:1043–1065CrossRefGoogle Scholar
  18. 18.
    Morris M, Guilak F (2009) Mobile heart health: project highlight. IEEE Perv Comput 8(2):57–61CrossRefGoogle Scholar
  19. 19.
    Riener A, Ferscha A, Aly M (2009) Heart on the road: Hrv analysis for monitoring a driver’s affective state. In: AutomotiveUI ‘09: proceedings of the 1st international conference on automotive user interfaces and interactive vehicular applications. ACM, New York, NY, USA, pp 99–106Google Scholar
  20. 20.
    Sato N, Miyake S, Akatsu J, Kumashiro M (1995) Power spectral analysis of heart rate variability in healthy young women during the normal menstrual cycle. Psychosom Med 57:331–335Google Scholar
  21. 21.
    Setz C, Arnrich B, Schumm J, La Marca R, Tröster G, Ehlert U (2010) Discriminating stress from cognitive load using a wearable EDA device. IEEE Trans Inf Technol Biomed Person Health SystGoogle Scholar
  22. 22.
    Soga C, Miyake S, Wada C (2007) Recovery patterns in the physiological responses of the autonomic nervous system induced by mental workload. In: SICE, 2007 annual conference, pp 1366–1371Google Scholar
  23. 23.
    van Daalen G, Willemsen TM, Sanders K, van Veldhoven MJPM (2009) Emotional exhaustion and mental health problems among employees doing people work: the impact of job demands, job resources and family-to-work conflict. Int Arch Occup Environ Health 82:291–303CrossRefGoogle Scholar
  24. 24.
    Wilson GF (2002) An analysis of mental workload in pilots during flight using multiple psychophysiological measures. Int J Aviat Psychol 12:3–18CrossRefGoogle Scholar
  25. 25.
    Wilson GF, Eggemeier FT (1991) Psychophysiological assessment of workload in multitask environments. Multiple-task performance, pp 329–360Google Scholar
  26. 26.
    Yan Rong (2006) MatlabARSENAL: a MATLAB package for classification algorithms. School of Computer Science, Carnegie Mellon University, PittsburghGoogle Scholar
  27. 27.

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Burcu Cinaz
    • 1
    Email author
  • Bert Arnrich
    • 1
  • Roberto La Marca
    • 1
  • Gerhard Tröster
    • 1
  1. 1.ETH ZurichElectronics LaboratoryZurichSwitzerland

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