Measuring Cognitive Workload in Non-military Scenarios Criteria for Sensor Technologies

  • Jörg Voskamp
  • Bodo Urban
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5638)


Augmented Cognition manifesting in the DARPA project is becoming of more and more interest to non-military application areas. First areas it is going to be applied are in flight control and power plant control. Measuring cognitive workload in the context of Augmented Cognition is bound to the application of sensor technologies and frameworks which are going to be applied to users. It is necessary to make Augmented Cognition Application in non-military areas as comfortable to the user as possible as we do not want to disturb her but to support her in her tasks. In this paper we will define criteria to be considered when designing Augmented Cognition applications in non-military environments.


Augmented Cognition Application Sensors systems sensor chriteria 


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  1. 1.
    Kruse, A.A., Schmorrow, D.D.: Session overview: Foundations of augmented cognition. In: Salvendy, G. (ed.) HCI International 2005 - 11th International Conference on Human-Computer Interaction, Caesars Palace, Las Vegas, Nevada, USA, July 22 - 27, vol. 11. Erlbaum, Mahwah (2005)Google Scholar
  2. 2.
    Orden, F.K., Viirre, E., Kobus, A.D.: Augmenting task-centered design with operator state assessment technologies. In: [43], pp. 212–219Google Scholar
  3. 3.
    Oertel, K., Kaiser, R., Voskamp, J., Urban, B.: Affectix - an affective component as part of an e-learning-system. In: [43], pp. 385–393Google Scholar
  4. 4.
    Forsythe, C., Berka, C., Matthews, R., Wagner, J.: Making the giant leap with augmented cognition technologies: What will be the first ‘killer app’? In: [43], pp. 434–438Google Scholar
  5. 5.
    Prinzel, J.L., Freeman, G.F., Scerbo, W.M., Mikulka, J.P., Pope, T.A.: A closed-loop system for examining psychophysiological measures for adaptive task allocation. International Journal of Aviation Psychology 10(4), 393–410 (2000)CrossRefPubMedGoogle Scholar
  6. 6.
    Scerbo, W.M., Freeman, G.F., Mikulka, J.P.: A biocybernetic system for adaptive automation. In: [44], pp. 241–253Google Scholar
  7. 7.
    Ward, R.D., Marsden, P.H.: Physiological responses to different web page designs. Int. J. Hum.-Comput. Stud. 59(1-2), 199–212 (2003)CrossRefGoogle Scholar
  8. 8.
    Veltman, A.J., Jansen, C.: The role of operator state assessment in adaptive automation (2006)Google Scholar
  9. 9.
    Cooper, E.G., Harper, P.R.: The use of pilot ratings in the evaluation of aircraft handlicng characteristics, Washington, D.C . (1969)Google Scholar
  10. 10.
    Hart, G.S., Staveland, E.L.: Development of nasa-tlx(task load index) resultsof empirical and theoretical research. In: [45]Google Scholar
  11. 11.
    Reid, B.G., Nygren, T.E.: The subjective workload assessment technique: a scaling procedure for measuring mental workload. In: [45], pp. 185–215Google Scholar
  12. 12.
    Embrey, D., Blackett, C., Marsden, P., Peachey, J.: Development of a human cognitive workload assessment tool (2006)Google Scholar
  13. 13.
    Freude, G., Ullsperger, P.: Slow brain potentials as a measure of effort? applications in mental workload studies in laboratory settings. In: [44], pp. 255–267Google Scholar
  14. 14.
    Marshall, P.S.: The index of cognitive activity: Measuring cognitive workload. In: Persensky, J.J. (ed.) New century, new trends - Proceedings of the 2002 IEEE 7th Conference on Human Factors and Power Plants, Scottsdale, Arizona, New York, NY, September 15 - 19, pp. 7.5–7.9. Institute of Electrical and Electronics Engineers (2002)Google Scholar
  15. 15.
    O’Donnel, D.R., Eggemeier, T.F.: Workload assessment methodology. In: Boff, R.K. (ed.) Handbook of perception and human performance, vol. 2, pp. 42/1–42/49. A Wiley-Interscience publication, New York (1986)Google Scholar
  16. 16.
    Guhe, M., Liao, W., Zhu, Z., Ji, Q., Gray, D.W., Schoelles, J.M.: Non-intrusive measurement of workload in real-time. Human Factors and Ergonomics Society Annual Meeting Proceedings 5, 1157–1161 (2005)CrossRefGoogle Scholar
  17. 17.
    Ikehara, S.C., Biagioni, E., Crosby, E.M.: Ad-hoc wireless body area network for augmented cognition sensors. In: [43], pp. 38–46Google Scholar
  18. 18.
    Mader, S., Peter, C., Göcke, R., Schultz, R., Voskamp, J., Urban, B.: A freely configurable, multi-modal sensor system for affective computing. In: André, E., Dybkjær, L., Minker, W., Heisterkamp, P. (eds.) ADS 2004. LNCS, vol. 3068, pp. 313–318. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  19. 19.
    Grootjen, M., Neerincx, A.M., Weert, C.J.: Task-based interpretation of operator state information for adaptive support. In: Schmorrow, D.D., Stanney, M.K., Reeves, M.L. (eds.) Foundations of Augmented Cognition, 2nd edn., Strategic Analysis, Arlington, Virginia, pp. 236–242 (2006)Google Scholar
  20. 20.
    Luczak, H., Göbel, M.: Signal processing and analysis in application. In: [44], pp. 79–110Google Scholar
  21. 21.
    Greef, T., Dongen, K., Grootjen, M., Lindenberg, J.: Augmented cognition: Reviewing the symbiotic relation between man and machine. In: [43], pp. 439–448Google Scholar
  22. 22.
    Castellano, G., Kessous, L., Caridakis, G.: Emotion recognition through multiple modalities: Face, body gesture, speech. In: Peter, C., Beale, R. (eds.) Affect and Emotion in Human-Computer Interaction, pp. 92–103. Springer, Berlin (2008)CrossRefGoogle Scholar
  23. 23.
    Ribback, S.: Psychophysiologische Untersuchung mentaler Beanspruchung in simulierten Mensch-Maschine-Interaktionen. PhD thesis, Universität Potsdam, Potsdam (2003)Google Scholar
  24. 24.
    Belyavin, A., Ryder, C., Dickson, B.: Development of gauges for the qinetiq cognition monitor. In: [43], pp. 3–12Google Scholar
  25. 25.
    Izzetoglu, M., Izzetoglu, K., Bunce, S., Ayaz, H., Devaraj, A., Onaral, B., Pourrezaei, K.: Functional near-infrared neuroimaging. IEEE Transactions on Neural Systems and Rehabilitation Engineering 13(2), 153–159 (2005)CrossRefPubMedGoogle Scholar
  26. 26.
    He, P., Yang, B., Hubbard, S., Estepp, J., Wilson, G.: A sensor positioning system for functional near-infrared neuroimaging. In: [43], pp. 30–37Google Scholar
  27. 27.
    Klingner, J., Kumar, R., Hanrahan, P.: Measuring the task-evoked pupillary response with a remote eye tracker. In: Räihä, K.J., Duchowski, T.A. (eds.) Proceedings of the 2008 symposium on Eye tracking research & applications, pp. 69–72. ACM, New York (2008)CrossRefGoogle Scholar
  28. 28.
    Tsai, Y.F., Viirre, E., Strychacz, C., Chase, B., Jung, T.P.: Task performance and eye activity - predicting behavior relating to cognitive workload. Aviation, Space, and Environmental Medicine 78(suppl. 1), B176–B185 (2007)Google Scholar
  29. 29.
    Ahlstrom, U., Frieman-Berg, J.F.: Using eye movement activity as a correlate of cognitive workload. International Journal of Aviation Psychology 36(7), 623–636 (2006)Google Scholar
  30. 30.
    Wang, L.M., Duffy, G.V., Du, Y.: A composite measure for the evaluation of mental workload. In: Duffy, V.G. (ed.) HCII 2007 and DHM 2007. LNCS, vol. 4561, pp. 460–466. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  31. 31.
    Zhang, Y., Owechko, Y., Zhang, J.: Driver cognitive workload estimation - a data-driven perspective. In: The 7th International IEEE Conference on Intelligent Transportation Systems Proceedings, Piscataway, NJ, pp. 642–647. IEEE Operations Center (2004)Google Scholar
  32. 32.
    Sirevaag, J.E., Stern, A.J.: Ocular measures of fatigue and cognitive factors. In: [44], pp. 269–287Google Scholar
  33. 33.
    Byrne, A.E., Parasuraman, R.: Psychophysiology and adaptive automation. Biological Psychology 42(3), 249–268 (1996)CrossRefPubMedGoogle Scholar
  34. 34.
    Veltman, A.J., Gaillard, W.A.: Physiological workload reactions to increasing levels of task difficulty (1996-06-21)Google Scholar
  35. 35.
    Lenneman, K.J., Backs, W.R.: The validity of factor analytically derivced cardiac autonomic components for mental workload assessment. In: [44], pp. 161–175Google Scholar
  36. 36.
    Boehm-Davis, A.D., Gray, D.W., Schoelles, J.M.: The eye blink as a physiological indicator of cognitive workload. Human Factors and Ergonomics Society Annual Meeting Proceedings 5, 116–119 (2006)Google Scholar
  37. 37.
    Heishman, R., Duric, Z.: Using eye blinks as a tool for augmented cognition. In: [43], pp. 84–93Google Scholar
  38. 38.
    Or, K.L.C., Duff, G.V.: Development of a facial skin temperature-based methodology for non-intrusive mental workload measurement. Occupational Ergonomics 7(2), 83–94 (2007)Google Scholar
  39. 39.
    Picard, W.R., Liu, K.K.: Relative subjective count and assessment of interruptive technologies applied to mobile monitoring of stress. International Journal Of Human-Computer Studies 65(4), 361–375 (2007)CrossRefGoogle Scholar
  40. 40.
    Göcke, R., Voskamp, J.: Optische mimikanalyse. In: Hambach, S., Urban, B. (eds.) Multimedia & Bildung - Beiträge zu den 4. IuK-Tagen Mecklenburg-Vorpommern, pp. 192–196. Fraunhofer-IRB-Verl., Stuttgart (2003)Google Scholar
  41. 41.
    Truong, P.K., Leeuwen, A.D., Neerincx, A.M.: Unobtrusive multimodal emotion detection in adaptive interfaces - speech and facial expressions. In: [43], pp. 354–363Google Scholar
  42. 42.
    Bieber, G., Peter, C.: Using physical activity for user behavior analysis. In: 1st ACM International Conference on PErvasive Technologies Related to Assistive Environments (2008)Google Scholar
  43. 43.
    Schmorrow, D.D., Reeves, M.L. (eds.): HCII 2007 and FAC 2007. LNCS (LNAI), vol. 4565. Springer, Heidelberg (2007)Google Scholar
  44. 44.
    Backs, W.R., Boucsein, W. (eds.): Engineering Psychophysiology - Issues and applications. Erlbaum, Mahwah (2000)Google Scholar
  45. 45.
    Hancock, A.P., Meshkati, N. (eds.): Human Mental Workload. Advances in psychology, vol. 52. North-Holland, Amsterdam (1988)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jörg Voskamp
    • 1
  • Bodo Urban
    • 1
  1. 1.Institutsteil RostockFraunhofer-Institut für Graphische DatenverarbeitungGermany

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