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Erschienen in:
Determining the Ecological Validity of Simulation Environments in Support of Human Competency Development Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Monitoring Task Fatigue in Contemporary and Future Vehicles: A Review

verfasst von : Gerald Matthews, Ryan Wohleber, Jinchao Lin, Gregory Funke, Catherine Neubauer

Erschienen in: Advances in Human Factors in Simulation and Modeling

Verlag: Springer International Publishing

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Abstract

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.

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Vorheriges Kapitel Manned-Unmanned Teaming: US Army Robotic Wingman Vehicles
Nächstes Kapitel Estimating Human State from Simulated Assisted Driving with Stochastic Filtering Techniques
Literatur
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)
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)CrossRef
3.
Matthews, G.: Towards a transactional ergonomics for driver stress and fatigue. Theor. Issues Ergon. Sci. 3, 195–211 (2002)CrossRef
4.
Hoddes, E., Zarcone, V., Smythe, H., Phillips, R., Dement, W.C.: Quantification of sleepiness: a new approach. Psychophysiology 10, 431–436 (1973)CrossRef
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)CrossRef
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)
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)CrossRef
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)
9.
Mullaney, D.J., Kripke, D.F., Messin, S.: Wrist-actigraphic estimation of sleep time. Sleep 3, 83–92 (1980)CrossRef
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)CrossRef
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)CrossRef
12.
Monk, T.H., Buysse, D.J., Rose, L.R.: Wrist actigraphic measures of sleep in space. Sleep 22, 948–954 (1999)
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)CrossRef
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)CrossRef
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)CrossRef
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)CrossRef
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)CrossRef
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)CrossRef
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)CrossRef
20.
Matthews, G.: Multidimensional profiling of task stress states for human factors: a brief review. Hum. Fact 58, 801–813 (2016)CrossRef
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)CrossRef
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)
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)
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)
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)CrossRef
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)CrossRef
27.
O’Hanlon, J.F.: Heart rate variability: a new index of driver alertness/fatigue (No. 720141). SAE Technical Paper (1972)
28.
Vicente, J., Laguna, P., Bartra, A., Bailón, R.: Drowsiness detection using heart rate variability. Med. Biol. Eng. Comput. 54, 927–937 (2016)CrossRef
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)CrossRef
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)CrossRef
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)CrossRef
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)
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)CrossRef
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)
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)CrossRef
36.
Muñoz-Organero, M., Corcoba-Magaña, V.: Predicting upcoming values of stress while driving. IEEE Trans. Intell. Transp. Syst. 18, 1802–1811 (2017)CrossRef
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)CrossRef
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)CrossRef
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)CrossRef
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)
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)CrossRef
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)
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)
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)
45.
Kloos, H., Van Orden, G.: Voluntary behavior in cognitive and motor tasks. Mind Matter 8, 19–43 (2010)
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)
Metadaten
Titel
Monitoring Task Fatigue in Contemporary and Future Vehicles: A Review
verfasst von
Gerald Matthews
Ryan Wohleber
Jinchao Lin
Gregory Funke
Catherine Neubauer
Copyright-Jahr
2019
Buch
Advances in Human Factors in Simulation and Modeling

Print ISBN: 978-3-319-94222-3

Electronic ISBN: 978-3-319-94223-0

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-319-94223-0_10