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|Title:||Novel tools for driving fatigue prediction: (1) Dry eeg sensor and (2) eye tracker|
|Source:||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. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8027 LNAI : 618-627. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-39454-6_66|
|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 . 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.|
|Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Appears in Collections:||Staff Publications|
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