Monitoring Task Fatigue in Contemporary and Future Vehicles: A Review

  • Gerald MatthewsEmail author
  • Ryan Wohleber
  • Jinchao Lin
  • Gregory Funke
  • Catherine Neubauer
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 780)


This article reviews advancements in methods for detection of task-induced driver fatigue. Early detection of the onset of fatigue may be enhanced by spectral frequency analysis of the electrocardiogram (ECG) and analysis of eye fixation durations. Validity may also be improved by developing algorithms that accommodate driver sleep history assessed using mobile actigraphic methods. Challenges to development of fatigue indices include ensuring that metrics are valid across the range of task demands encountered by drivers. Future autonomous vehicles will place novel demands on the driver, and research is needed to test the applicability of current fatigue metrics.


Driver fatigue Safety Autonomous vehicles Actigraphy Electrocardiogram Eye tracking Subjective stress 



Gerald Matthews and Ryan Wohleber gratefully acknowledge research support from DENSO Corporation.


  1. 1.
    Desmond, P.A., Hancock, P.A.: Active and passive fatigue states. In: Hancock, P.A., Desmond, P.A. (eds.) Stress, Workload and Fatigue, pp. 455–465. Lawrence Erlbaum, Mahwah (2001)Google Scholar
  2. 2.
    Saxby, D.J., Matthews, G., Warm, J.S., Hitchcock, E.M., Neubauer, C.: Active and passive fatigue in simulated driving: discriminating styles of workload regulation and their safety impacts. J. Exp. Psychol. Appl. 19, 287–300 (2013)CrossRefGoogle Scholar
  3. 3.
    Matthews, G.: Towards a transactional ergonomics for driver stress and fatigue. Theor. Issues Ergon. Sci. 3, 195–211 (2002)CrossRefGoogle Scholar
  4. 4.
    Hoddes, E., Zarcone, V., Smythe, H., Phillips, R., Dement, W.C.: Quantification of sleepiness: a new approach. Psychophysiology 10, 431–436 (1973)CrossRefGoogle Scholar
  5. 5.
    Connor, J., Norton, R., Ameratunga, S., Robinson, E., Civil, I., Dunn, R., Jackson, R.: Driver sleepiness and risk of serious injury to car occupants: population based case control study. Br. Med. J. 324, 1125–1128 (2002)CrossRefGoogle Scholar
  6. 6.
    Wohleber, R.W., Matthews, G., Funke, G.J., Lin, J.: Considerations in physiological metric selection for online detection of operator state: a case study. In: Schmorrow, D., Fidopiastis, C. (eds.) Foundations of Augmented Cognition: Neuroergonomics and Operational Neuroscience. Springer, Cham (2016)Google Scholar
  7. 7.
    Borghini, G., Astolfi, L., Vecchiato, G., Mattia, D., Babiloni, F.: Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness. Neurosci. Biobehav. Rev. 44, 58–67 (2014)CrossRefGoogle Scholar
  8. 8.
    Wierwille, W.W., Wreggit, S.S., Kirn, C.L., Ellsworth, L.A., Fairbanks, R.J.: Research on Vehicle-Based Driver Status/Performance Monitoring; Development, Validation, and Refinement of Algorithms for Detection of Driver Drowsiness (No. HS-808 247 VPISU ISE 94-04) (1994)Google Scholar
  9. 9.
    Mullaney, D.J., Kripke, D.F., Messin, S.: Wrist-actigraphic estimation of sleep time. Sleep 3, 83–92 (1980)CrossRefGoogle Scholar
  10. 10.
    Sadeh, A., Hauri, P.J., Kripke, D.F., Lavie, P.: The role of actigraphy in the evaluation of sleep disorders. Sleep 18, 288–302 (1995)CrossRefGoogle Scholar
  11. 11.
    de Souza, L., Benedito-Silva, A.A., Pires, M.L.N., Poyares, D., Tufik, S., Calil, H.M.: Further validation of actigraphy for sleep studies. Sleep 26, 81–85 (2003)CrossRefGoogle Scholar
  12. 12.
    Monk, T.H., Buysse, D.J., Rose, L.R.: Wrist actigraphic measures of sleep in space. Sleep 22, 948–954 (1999)Google Scholar
  13. 13.
    Ko, P.R.T., Kientz, J.A., Choe, E.K., Kay, M., Landis, C.A., Watson, N.F.: Consumer sleep technologies: a review of the landscape. J. Clin. Sleep Med. 11, 1455–1461 (2015)CrossRefGoogle Scholar
  14. 14.
    de Zambotti, M., Baker, F.C., Willoughby, A.R., Godino, J.G., Wing, D., Patrick, K., Colrain, I.M.: Measures of sleep and cardiac functioning during sleep using a multi-sensory commercially-available wristband in adolescents. Physiol. Behav. 158, 143–149 (2016)CrossRefGoogle Scholar
  15. 15.
    Kang, S.G., Kang, J.M., Ko, K.P., Park, S.C., Mariani, S., Weng, J.: Validity of a commercial wearable sleep tracker in adult insomnia disorder patients and good sleepers. J. Psychosom. Res. 97, 38–44 (2017)CrossRefGoogle Scholar
  16. 16.
    Philip, P., Sagaspe, P., Moore, N., Taillard, J., Charles, A., Guilleminault, C., Bioulac, B.: Fatigue, sleep restriction and driving performance. Accid. Anal. Prev. 37, 473–478 (2005)CrossRefGoogle Scholar
  17. 17.
    Vakulin, A., Baulk, S.D., Catcheside, P.G., Anderson, R., van den Heuvel, C.J., Banks, S., McEvoy, R.D.: Effects of moderate sleep deprivation and low-dose alcohol on driving simulator performance and perception in young men. Sleep 30, 1327–1333 (2007)CrossRefGoogle Scholar
  18. 18.
    Ware, J.C., Risser, M.R., Manser, T., Karlson Jr., K.H.: Medical resident driving simulator performance following a night on call. Behav. Sleep Med. 4, 1–12 (2006)CrossRefGoogle Scholar
  19. 19.
    Hanowski, R.J., Hickman, J., Fumero, M.C., Olson, R.L., Dingus, T.A.: The sleep of commercial vehicle drivers under the 2003 hours-of-service regulations. Accid. Anal. Prev. 39, 1140–1145 (2007)CrossRefGoogle Scholar
  20. 20.
    Matthews, G.: Multidimensional profiling of task stress states for human factors: a brief review. Hum. Fact 58, 801–813 (2016)CrossRefGoogle Scholar
  21. 21.
    Matthews, G., Reinerman-Jones, L., Abich IV, J., Kustubayeva, A.: Metrics for individual differences in EEG response to cognitive workload: optimizing performance prediction. Pers. Indiv. Differ. 118, 22–28 (2017)CrossRefGoogle Scholar
  22. 22.
    Matthews, G., Neubauer, C.E., Saxby, D.J., Wohleber, R.W., Lin, J.: Dangerous intersections? A review of studies of fatigue and distraction in the automated vehicle. Accid. Anal. Prev. (in press)Google Scholar
  23. 23.
    Neubauer, C.E., Matthews, G., Saxby, D.J.: The effects of cell phone use and automation on driver performance and subjective state in simulated driving. In: Proceedings of the Human Factors and Ergonomics Society, vol. 56, pp. 1987–1991 (2012)Google Scholar
  24. 24.
    Neubauer, C.E., Saxby, D.J., Matthews, G.: Fatigue in the automated vehicle: do games and conversation distract or energize the driver? In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 58, pp. 2053–2057 (2014)Google Scholar
  25. 25.
    May, J.F., Baldwin, C.L.: Driver fatigue: the importance of identifying causal factors of fatigue when considering detection and countermeasure technologies. Transp. Res. F Traffic Psychol. Behav. 12, 218–224 (2009)CrossRefGoogle Scholar
  26. 26.
    Jap, B.T., Lal, S., Fischer, P., Bekiaris, E.: Using EEG spectral components to assess algorithms for detecting fatigue. Expert Syst. Appl. 36, 2352–2359 (2009)CrossRefGoogle Scholar
  27. 27.
    O’Hanlon, J.F.: Heart rate variability: a new index of driver alertness/fatigue (No. 720141). SAE Technical Paper (1972)Google Scholar
  28. 28.
    Vicente, J., Laguna, P., Bartra, A., Bailón, R.: Drowsiness detection using heart rate variability. Med. Biol. Eng. Comput. 54, 927–937 (2016)CrossRefGoogle Scholar
  29. 29.
    Patel, M., Lal, S.K., Kavanagh, D., Rossiter, P.: Applying neural network analysis on heart rate variability data to assess driver fatigue. Expert Syst. Appl. 38, 7235–7242 (2011)CrossRefGoogle Scholar
  30. 30.
    Awais, M., Badruddin, N., Drieberg, M.: A hybrid approach to detect driver drowsiness utilizing physiological signals to improve system performance and wearability. Sensors 17, 1991 (2017)CrossRefGoogle Scholar
  31. 31.
    Sahayadhas, A., Sundaraj, K., Murugappan, M.: Drowsiness detection during different times of day using multiple features. Australas. Phys. Eng. Sci. Med. 36, 243–250 (2013)CrossRefGoogle Scholar
  32. 32.
    Zhao, X., Wei, Z., Li, Z., Zhang, Y., Feng, X.: Threshold research on highway length under typical landscape patterns based on drivers’ physiological performance. Discret. Dyn. Nat. Soc. 1–15 (2015)Google Scholar
  33. 33.
    Liang, W.C., Yuan, J., Sun, D.C., Lin, M.H.: Changes in physiological parameters induced by indoor simulated driving: effect of lower body exercise at mid-term break. Sensors 9, 6913–6933 (2009)CrossRefGoogle Scholar
  34. 34.
    Schmidt, E., Decke, R., Rasshofer, R.: Correlation between subjective driver state measures and psychophysiological and vehicular data in simulated driving. In: 2016 IEEE Intelligent Vehicles Symposium (IV), pp. 1380–1385. IEEE (2016)Google Scholar
  35. 35.
    Jiao, K., Li, Z., Chen, M., Wang, C., Qi, S.: Effect of different vibration frequencies on heart rate variability and driving fatigue in healthy drivers. Int. Arch. Occup. Env. Health 77, 205–212 (2004)CrossRefGoogle Scholar
  36. 36.
    Muñoz-Organero, M., Corcoba-Magaña, V.: Predicting upcoming values of stress while driving. IEEE Trans. Intell. Transp. Syst. 18, 1802–1811 (2017)CrossRefGoogle Scholar
  37. 37.
    Nickel, P., Nachreiner, F.: Sensitivity and diagnosticity of the 0.1-Hz component of heart rate variability as an indicator of mental workload. Hum. Fact. 45, 575–590 (2003)CrossRefGoogle Scholar
  38. 38.
    Wang, L., Wang, H., Jiang, X.: A new method to detect driver fatigue based on EMG and ECG collected by portable non-contact sensors. PROMET-Traffic Transp. 29, 479–488 (2017)CrossRefGoogle Scholar
  39. 39.
    Zhao, C., Zhao, M., Liu, J., Zheng, C.: Electroencephalogram and electrocardiograph assessment of mental fatigue in a driving simulator. Accid. Anal. Prev. 45, 83–90 (2012)CrossRefGoogle Scholar
  40. 40.
    Dinges, D.F., Mallis, M.M., Maislin, G., Powell, IV, J.W.: Evaluation of Techniques for Ocular Measurement as an Index of Fatigue and the Basis for Alertness Management (Monograph No. DOT HS 808 762). National Highway Traffic Safety Administration, Washington, DC (1998)Google Scholar
  41. 41.
    Schleicher, R., Galley, N., Briest, S., Galley, L.: Blinks and saccades as indicators of fatigue in sleepiness warnings: looking tired? Ergonomics 51, 982–1010 (2008)CrossRefGoogle Scholar
  42. 42.
    Briest, S., Karrer, K., Schleicher, R.: Driving without awareness: examination of the phenomenon. In: Gale, A. (ed.) Vision in Vehicles XI, pp. 89–141. Elsevier, Amsterdam (2006)Google Scholar
  43. 43.
    Poole, A., Ball, L.J.: Eye tracking in HCI and usability research. In: Encyclopedia of Human Computer Interaction, vol. 1, pp. 211–219 (2006)Google Scholar
  44. 44.
    Russell, S.M., Funke, G.J., Flach, J.M., Watamaniuk, S.N., Strang, A.J., Miller, B.T., Dukes, A., Menke, L., Brown, R.: Alternative indices of performance: an exploration of eye gaze metrics in a visual puzzle task. Technical report (No. AFRL-RH-WP-TR-2014-0095), Air Force Research Laboratory, Wright-Patterson Air Force Base (2014)Google Scholar
  45. 45.
    Kloos, H., Van Orden, G.: Voluntary behavior in cognitive and motor tasks. Mind Matter 8, 19–43 (2010)Google Scholar
  46. 46.
    Furman, G.D., Baharav, A., Cahan, C., Akselrod, S.: Early detection of falling asleep at the wheel: a heart rate variability approach. In: Computers in Cardiology, pp. 1109–1112. IEEE (2008)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Gerald Matthews
    • 1
    Email author
  • Ryan Wohleber
    • 1
  • Jinchao Lin
    • 1
  • Gregory Funke
    • 2
  • Catherine Neubauer
    • 3
  1. 1.Institute for Simulation and TrainingUniversity of Central FloridaOrlandoUSA
  2. 2.Air Force Research LaboratoryDaytonUSA
  3. 3.U.S Army Research LaboratoryUniversity of Southern CaliforniaLos AngelesUSA

Personalised recommendations