Reference : Multi-timescale drowsiness characterization based on a video of a driver’s face
Scientific journals : Article
Engineering, computing & technology : Multidisciplinary, general & others
http://hdl.handle.net/2268/227258
Multi-timescale drowsiness characterization based on a video of a driver’s face
English
Massoz, Quentin mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Exploitation des signaux et images >]
Verly, Jacques mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Dép. d'électric., électron. et informat. (Inst.Montefiore) >]
Van Droogenbroeck, Marc mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications >]
25-Aug-2018
Sensors
Multidisciplinary Digital Publishing Institute (MDPI)
18
9
Special Issue: Perception Sensors for Road Applications
2801
Yes (verified by ORBi)
International
1424-8220
1424-3210
Switzerland
[en] drowsiness ; driver monitoring ; multi-timescale ; eye closure dynamics ; psychomotor vigilance task ; reaction time ; convolution neural network
[en] Drowsiness is a major cause of fatal accidents, in particular in transportation. It is therefore crucial to develop automatic, real-time drowsiness characterization systems designed to issue accurate and timely warnings of drowsiness to the driver. In practice, the least intrusive, physiology-based approach is to remotely monitor, via cameras, facial expressions indicative of drowsiness such as slow and long eye closures. Since the system’s decisions are based upon facial expressions in a given time window, there exists a trade-off between accuracy (best achieved with long windows, i.e., at long timescales) and responsiveness (best achieved with short windows, i.e., at short timescales). To deal with this trade-off, we develop a multi-timescale drowsiness characterization system composed of four binary drowsiness classifiers operating at four distinct timescales (5 s, 15 s, 30 s, and 60 s) and trained jointly. We introduce a multi-timescale ground truth of drowsiness, based on the reaction times (RTs) performed during standard Psychomotor Vigilance Tasks (PVTs), that strategically enables our system to characterize drowsiness with diverse trade-offs between accuracy and responsiveness. We evaluated our system on 29 subjects via leave-one-subject-out cross-validation and obtained strong results, i.e., global accuracies of 70%, 85%, 89%, and 94% for the four classifiers operating at increasing timescales, respectively.
Montefiore Institute of Electrical Engineering and Computer Science - Montefiore Institute
Fonds pour la formation à la Recherche dans l'Industrie et dans l'Agriculture (Communauté française de Belgique) - FRIA
http://hdl.handle.net/2268/227258
10.3390/s18092801
http://www.telecom.ulg.ac.be/mts-drowsiness

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