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Introduction to Real-Time State Assessment

  • Brett J. BorghettiEmail author
  • Christina F. Rusnock
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9743)

Abstract

Real-Time State Assessment (RTSA) is the act of continuously monitoring an individual in order to estimate the human’s current state. Examples of real time state assessment include estimating workload, fatigue, stress, and attention from physiological measures such as Electroencephalogram (EEG) or eye-tracking inputs. When estimated in real-time, the state of the human can aid dynamic task allocation systems in determining when to intervene and what course of action should be taken to mitigate potential problems or to improve system performance. In this paper we provide an introduction to the field of RTSA study, including an overview of modeling techniques and assessment methods. RTSA’s challenges are discussed, and recent work in the area is reviewed.

Keywords

Real-time State assessment Modeling Human performance modeling Neuroergonomics Dynamic task allocation 

Notes

Acknowledgements

The views in this article are those of the authors and do not necessarily reflect the official policy or position of the Department of the Air Force, Department of Defense nor the U.S. Government.

References

  1. 1.
    James, G., et al.: An Introduction to Statistical Learning with Applications in R, 1st edn. Springer, New York (2013)CrossRefzbMATHGoogle Scholar
  2. 2.
    Lin, C.-T., et al.: Development of wireless brain computer interface with embedded multitask scheduling and its application on real-time driver’s drowsiness detection and warning. IEEE Trans. Biomed. Eng. 55(5), 1582–1591 (2008)CrossRefGoogle Scholar
  3. 3.
    Ganesan, S., et al.: Real-Time non linear bio-signals detection using fuzzy logic for wireless brain computer interface. Ijcns.Com, vol. 2 (2010)Google Scholar
  4. 4.
    Byrne, E.A., Parasuraman, R.: Psychophysiology and adaptive automation. Biol. Psychol. 42(3), 249–268 (1996)CrossRefGoogle Scholar
  5. 5.
    Jung, T.P., et al.: Estimating alertness from the EEG power spectrum. IEEE Trans. Biomed. Eng. 44, 60–69 (1997)CrossRefGoogle Scholar
  6. 6.
    Poythress, M., et al.: Correlation between expected workload and EEG indices of cognitive workload and task engagement. Found. Augment. Cogn. 1, 32–44 (2006)Google Scholar
  7. 7.
    Giametta, J.J., Borghetti, B.J.: EEG-based secondary task detection in a multiple objective operational environment. In: Proceedings of the 14th International Conference on Machine Learning and Applications (ICMLA) (2015)Google Scholar
  8. 8.
    Gevins, A., Smith, M.E.: Neurophysiological measures of cognitive workload during human-computer interaction. Theor. Issues Ergon. Sci. 4(1–2), 113–131 (2003)CrossRefGoogle Scholar
  9. 9.
    Smith, A.M., et al.: Improving model cross-applicability for operator workload estimation. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 59(1), 681–685 (2015)CrossRefGoogle Scholar
  10. 10.
    Jo, S., et al.: Quantitative prediction of mental workload with the ACT-R cognitive architecture. Int. J. Ind. Ergon. 42(4), 359–370 (2012)CrossRefGoogle Scholar
  11. 11.
    Fairclough, S.H.: Fundamentals of physiological computing. Interact. Comput. 21(1–2), 133–145 (2009)CrossRefGoogle Scholar
  12. 12.
    Prinzel, L.J., et al.: Effects of a psychophysiological system for adaptive automation on performance, workload, and the event-related potential P300 component. Hum. Factors 45(4), 601–613 (2003)CrossRefGoogle Scholar
  13. 13.
    Parasuraman, R.: Neuroergonomics: research and practice. Theor. Issues Ergon. Sci. 4(1–2), 5–20 (2003)CrossRefGoogle Scholar
  14. 14.
    Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Pearson, London (2009)zbMATHGoogle Scholar
  15. 15.
    Fan, X., Yen, J.: Modeling cognitive loads for evolving shared mental models in human-agent collaboration. IEEE Trans. Syst. Man Cybern. B Cybern. 41(2), 354–367 (2011)CrossRefGoogle Scholar
  16. 16.
    Hsu, S.-H., Jung, T.-P.: Modeling and tracking brain nonstationarity in a sustained attention task. In: Human-Computer Interaction International (2016)Google Scholar
  17. 17.
    Du, W., Kim, J.H.: Performance-based eye-tracking analysis in a dynamic monitoring task. In: Human Computer Interaction International (2016)Google Scholar
  18. 18.
    Hart, S.G., Staveland, L.E.: Development of NASA-TLX (Task Load Index): results of empirical and theoretical research. Adv. Psychol. 52(C), 139–183 (1988)CrossRefGoogle Scholar
  19. 19.
    Blaha, L.M., et al.: Real-time fatigue monitoring with computational cognitive models. In: Human Computer Interaction International (2016)Google Scholar
  20. 20.
    Klass, D.W.: The continuing challenge of artifacts in the EEG. Am. J. EEG Technol. 35, 239–269 (1995)Google Scholar
  21. 21.
    Jung, T.P., et al.: Removing electroencephalographic artifacts by blind source separation. Psychophysiology 37(2), 163–178 (2000)CrossRefGoogle Scholar
  22. 22.
    Fatourechi, M., et al.: EMG and EOG artifacts in brain computer interface systems: a survey. Clin. Neurophysiol. 118(3), 480–494 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Air Force Institute of Technology, Wright-Patterson AFBDaytonUSA

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