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• W2750254956 abstract "The ratio of accidents caused by drowsiness, increases slightly year by year. The most victims of this case are young adult and mostly happens in developed country. Therefore, to reduce the number of accidents caused by drowsiness, researchers around the world develope some methods for detecting drowsiness on driver's face automatically. They propose various features such as visual, non-visual, and vehicular. Visual features are extracted from driver's face and recorded by camera. Non-visual features are signals emerged from driver's body and to acquire those signals, they use special sensor attached to driver's body. Vehicular features are obtained by observing the behavior of driver during driving. From those features which are propsed by researchers, we discussed 3 ideas that can be considered as guidance to lead researcher in developing drowsiness detection. The first idea is creating the dataset of drowsiness facial expression because it can predict drowsiness and fatigue. Second idea is to combine visual, non-visual, and vehicular features into one for better detection. And last one is developing wearable hardwares such as smartwatch for drowsiness detection which are easy to use and user friendly." @default.
• W2750254956 created "2017-08-31" @default.
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• W2750254956 date "2017-07-01" @default.
• W2750254956 modified "2023-10-01" @default.
• W2750254956 title "A review on driver drowsiness based on image, bio-signal, and driver behavior" @default.
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• W2750254956 doi "https://doi.org/10.1109/icstc.2017.8011855" @default.
• W2750254956 hasPublicationYear "2017" @default.
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