Abstract
National Sleep Foundation’s Sleep in America (2005) reported 60% of adult drivers driving a vehicle while feeling drowsy in the past year, and more than 37% have actually fallen asleep at the wheel [1]. This paper presented the findings of two novel fatigue prediction tools. The first study presents a 4-channel dry EEG under simulated driving being able to predict when the driver will develop microsleep in the next 10 minutes using only 3 minutes data of collected, with an accuracy of more than 80%. The second study uses an eye tracker to assess the percentage of time that the eyelids were closed (PERCLOS) as a potential marker for fatigue. Results showed that the average magnitude of oscillation (amount of pupil fluctuation), known as Coefficient Magnitude (CM), is generated from real-time wavelet analysis, has the potential to predict fatigue 8-12 minutes ahead with 84% accuracy ahead of compromised driving behavior.
Chapter PDF
Similar content being viewed by others
References
Summary Findings of the Sleep in America poll (2005), http://www.sleepfoundation.org/sites/default/files/2005_summary_of_findings.pdf
Rosekind, M.R.: Underestimating the societal costs of impaired alertness: safety, health and productivity risks. Sleep Med. 25 (suppl. 1), S21–S25 (2005)
Horne, J.A., Baulk, S.D.: Awareness of sleepiness when driving. Psychophysiology 41, 161–165 (2004)
Smiley, A.: Fatigue management: lessons from research. In: Hartley, L. (ed.) Managing Fatigue in Transportation, pp. 1–23. Elsevier, Oxford (1998)
Thiffault, P., Bergeron, J.: Monotony of road environment and driver fatigue: a simulator study. Accident Analysis & Prevention 35, 381–391 (2003)
Summala, H., Mikkola, T.: Fatal accidents among car and truck drivers: effects of fatigue, age and alcohol consumption, age and alcohol consumption. Human Factors 36, 315–326 (1994)
Van Dongen, H.P.A., Dinges, D.F.: Sleep, circadian rythms, and psychomotorvigilance. Clinics in Sport Medicine 24, 237–249 (2005)
Moore, R.Y.: Organization of the mammalian circadian system. In: Circadian Clocks and Their Adjustments, pp. 88–106 (1995)
Pack, A.I., Pack, A.M., Rodgman, E., Cucchiara, A., Dinges, D.F., Schwab, C.W.: Characteristics of crashes attributed to the driver having fallen asleep. Accident Anal. Prevention 27, 769–775 (1995)
Park, S.-W., Mukherjee, A., Gross, F., Jovanis, P.P.: Safety Implications of Multi-day. Driving Schedules for Truck Drivers: Comparison of Field Experiments and Crash Data Analysis. Transportation Research Board Annual Meeting CD, Journal of the Transportation Research Board (2005)
Philip, P., Sagaspe, P., Moore, N., Taillard, J., Charles, A., Guilleminault, C., Bioulac, B.: Fatigue, sleep restriction and driving performance. Accident Analysis & Prevention 37, 473–478 (2005)
Larue, G.S., Rakotonirainy, A., Pettitt, A.N.: Driving performance impairments due to hypovigilance on monotonous roads. Accident Analysis & Prevention 43, 2037–2046 (2011)
Åkerstedt, T., Czeisler, C.A., Dinges, D.F., Horne, J.A.: Accidents and sleepiness: a consensus statement from the International Conference on Work Hours, Sleepiness and Accidents, Stockholm, September 8-10, pp. 8–10 (1994); J. Sleep Res. 3, 195
Loewenfeld, I.: The Pupil. Anatomy, physiology and clinical applications. Wayne State University Press, Detroit (1993)
McLaren, J., Erie, J., Brubaker, R.: Computerized analysis of pupillograms in studies of alertness. Investigative Ophthalmology & Visual Science 33(3), 671–676 (1992)
Nishiyama, J., Tanida, K., Kusumi, M., Hirata, Y.: The pupil as a possible premonitor of drowsiness. IEEE, 1586–1589 (2007)
Lüdtke, H., Wilhelm, B., Adler, M., Schaeffel, F., Wilhelm, H.: Mathematical procedures in data recording and processing of pupillary fatigue waves. Vision Research 38, 2889–2896 (1998)
Wilhelm, H., Lüdtke, H., Wilhelm, B.: Pupillographic sleepiness testing in hypersomniacs and normals. Graefe’s Archive for Clinical and Experimental Ophthalmology 236, 725–729 (1998)
Nakayama, M., Yamamoto, K., Kobayashi, F.: Estimation of sleepiness using frequency components of pupillary response. IEEE, 357–360 (2008)
Henson, D., Emuh, T.: Monitoring vigilance during perimetry by using pupillography. Investigative Ophthalmology & Visual Science 51(7), 3540–3543 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tey, F., Lin, S.T., Tan, Y.Y., Li, X.P., Phillipou, A., Abel, L. (2013). Novel Tools for Driving Fatigue Prediction: (1) Dry Eeg Sensor and (2) Eye Tracker. In: Schmorrow, D.D., Fidopiastis, C.M. (eds) Foundations of Augmented Cognition. AC 2013. Lecture Notes in Computer Science(), vol 8027. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39454-6_66
Download citation
DOI: https://doi.org/10.1007/978-3-642-39454-6_66
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39453-9
Online ISBN: 978-3-642-39454-6
eBook Packages: Computer ScienceComputer Science (R0)