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- W2910005613 abstract "Driver drowsiness is a major cause of road accidents, many of which result in fatalities. A solution to this problem is the inclusion of a drowsiness detector in vehicles to alert the driver if sleepiness is detected. To detect drowsiness, physiologic, behavioral (visual) and vehicle-based methods can be used, however, only measures that can be acquired non-intrusively are viable in a real life application. This work uses data from a real-road experiment with sleep deprived drivers to compare the performance of driver drowsiness detection using intrusive acquisition methods, namely electrooculogram (EOG), with camera-based, non-intrusive, methods. A hybrid strategy, combining the described methods with electrocardiogram (ECG) measures, is also evaluated. Overall, the obtained results show that drowsiness detection performance is similar using non-intrusive camera-based measures or intrusive EOG measures. The detection performance increases when combining two methods (ECG + visual) or (ECG + EOG)." @default.
- W2910005613 created "2019-01-25" @default.
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- W2910005613 date "2018-11-01" @default.
- W2910005613 modified "2023-10-04" @default.
- W2910005613 title "Driver drowsiness detection: a comparison between intrusive and non-intrusive signal acquisition methods" @default.
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- W2910005613 doi "https://doi.org/10.1109/euvip.2018.8611704" @default.
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