Advertisement

Real-Time Fatigue Monitoring with Computational Cognitive Models

  • Leslie M. BlahaEmail author
  • Christopher R. FisherEmail author
  • Matthew M. Walsh
  • Bella Z. Veksler
  • Glenn Gunzelmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9743)

Abstract

Real-time monitoring with cognitive models offers the unique ability to both predict performance decrements from behavioral data and identify the responsible cognitive mechanisms for targeted interventions. However, their potential has not been realized because current parameter updating methods are prohibitively slow. We present a paradigm that enables real-time monitoring using cognitive models and demonstrate its implementation with a fatigue-sensitive task. In this demonstration, an operator workstation, a cognitive model, and a monitoring station are networked such that task performance data are sent to a central server that estimates model parameters and generates model-based performance metrics. These are sent to a monitoring station where they are summarized graphically together with model fit diagnostics. This constitutes an infrastructure that can be leveraged for future predictive adaptive system designs.

Keywords

Cognitive augmentation Real-time monitoring Parameter estimation Fatigue ACT-R Computational cognitive models 

Notes

Acknowledgments

We thank Brad Reynolds for software programming assistance. The views expressed in this paper are those of the authors and do not reflect the official policy or position of the Department of Defense or the U.S. Government. This research was supported by a \(711^{\text {th}}\) Human Performance Wing Chief Scientist Seedling grant to G.G. and L.M.B.

References

  1. 1.
    Anderson, J.R.: How Can the Human Mind Occur in the Physical Universe? Oxford University Press, New York (2007)Google Scholar
  2. 2.
    Basner, M., Dinges, D.F.: Maximizing sensitivity of the psychomotor vigilance test (PVT) to sleep loss. Sleep 34(5), 581–591 (2011)Google Scholar
  3. 3.
    Bezanson, J., Edelman, A., Karpinski, S., Shah, V.B.: Julia: a fresh approach to numerical computation (2014). arXiv preprint arXiv:1411.1607
  4. 4.
    Van den Bos, A.: Parameter Estimation for Scientists and Engineers. Wiley, New York (2007)zbMATHGoogle Scholar
  5. 5.
    Bostock, M., Ogievetsky, V., Heer, J.: D\(^3\) data-driven documents. IEEE Trans. Vis. Comput. Graph. 17(12), 2301–2309 (2011)CrossRefGoogle Scholar
  6. 6.
    Dinges, D.F., Powell, J.W.: Microcomputer analyses of performance on a portable, simple visual RT task during sustained operations. Behav. Res. Methods Instrum. Comput. 17(6), 652–655 (1985)CrossRefGoogle Scholar
  7. 7.
    Gluck, K.A., Gunzelmann, G.: Computational process modeling and cognitive stressors: background and prospects for application in cognitive. In: The Oxford Handbook of Cognitive Engineering, p. 424 (2013)Google Scholar
  8. 8.
    Gunzelmann, G., Gross, J.B., Gluck, K.A., Dinges, D.F.: Sleep deprivation and sustained attention performance: integrating mathematical and cognitive modeling. Cogn. Sci. 33(5), 880–910 (2009)CrossRefGoogle Scholar
  9. 9.
    Gunzelmann, G., Veksler, B.Z., Walsh, M.M., Gluck, K.A.: Understanding and predicting the cognitive effects of sleep loss through simulation. Trans. Issues Psychol. Sci. 1(1), 106 (2015)CrossRefGoogle Scholar
  10. 10.
    Hobbs, A., Avers, K.B., Hiles, J.J.: Fatigue risk management in aviation maintenance: current best practices and potential future countermeasures. Technical report, DTIC Document (2011)Google Scholar
  11. 11.
    Hursh, S.R., Redmond, D.P., Johnson, M.L., Thorne, D.R., Belenky, G., Balkin, T.J., Storm, W.F., Miller, J.C., Eddy, D.R.: Fatigue models for applied research in warfighting. Aviat. Space Environ. Med. 75(Supplement 1), A44–A53 (2004)Google Scholar
  12. 12.
    Lerman, S.E., Eskin, E., Flower, D.J., George, E.C., Gerson, B., Hartenbaum, N., Hursh, S.R., Moore-Ede, M., et al.: Fatigue risk management in the workplace. J. Occup. Environ. Med. 54(2), 231–258 (2012)CrossRefGoogle Scholar
  13. 13.
    Loh, S., Lamond, N., Dorrian, J., Roach, G., Dawson, D.: The validity of psychomotor vigilance tasks of less than 10 min duration. Behav. Res. Methods 36(2), 339–346 (2004)CrossRefGoogle Scholar
  14. 14.
    Rangaiah, G.P.: Stochastic Global Optimization: Techniques and Applications in Chemical Engineering, vol. 2. World Scientific, Singapore (2010)CrossRefGoogle Scholar
  15. 15.
    Veksler, B., Gunzelmann, G.: Functional equivalence of sleep loss and time on task effects in sustained attention (Under Review)Google Scholar
  16. 16.
    Walsh, M.M., Gunzelmann, G., Van Dongen, H.P.: Comparing accounts of psychomotor vigilance impairment due to sleep loss. In: Annual Meeting of the Cognitive Science Society, Pasadena, California, pp. 877–882 (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Leslie M. Blaha
    • 1
    Email author
  • Christopher R. Fisher
    • 1
    Email author
  • Matthew M. Walsh
    • 2
  • Bella Z. Veksler
    • 1
  • Glenn Gunzelmann
    • 1
  1. 1.Air Force Research LaboratoryWright-Patterson AFBUSA
  2. 2.Tier1 Performance SolutionsCovingtonUSA

Personalised recommendations