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Novel tools for driving fatigue prediction: (1) Dry eeg sensor and (2) eye tracker
conference contribution
posted on 2023-01-27, 04:40 authored by F Tey, S T Lin, Y Y Tan, X P Li, A Phillipou, Larry AbelLarry AbelNational 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. © 2013 Springer-Verlag Berlin Heidelberg.
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Volume
8027 LNAIPagination
618 - 627Publisher DOI
ISSN
0302-9743eISSN
1611-3349ISBN-13
9783642394539Publication classification
E1.1 Full written paper - refereedTitle of proceedings
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Usage metrics
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