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dc.contributor.authorLemkaddem, Alia
dc.contributor.authorDelgado-Gonzalo, Ricard
dc.contributor.authorTüretken, Engin
dc.contributor.authorDasen, Stephan
dc.contributor.authorMoser, Virginie
dc.contributor.authorGressum, Carl
dc.contributor.authorSola, Josep
dc.contributor.authorFerrario, Damien
dc.contributor.authorVerjus, Christophe
dc.date.accessioned2022-02-14T17:07:47Z
dc.date.available2022-02-14T17:07:47Z
dc.date.issued2018
dc.identifier.citation2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), Las Vegas, NV (USA), pp. 9-12
dc.identifier.isbn978-1-5386-2405-0
dc.identifier.urihttps://yoda.csem.ch/handle/20.500.12839/680
dc.description.abstractDriver drowsiness is a significant contributing factor to road accidents and can lead to severe physical injuries, deaths, and significant economic losses. Earlier research primarily concentrated on estimating the level of drowsiness using a single measure such as lane or steering monitoring, behavioral measures or physiological ones. Hybrid systems employing multiple measures provide more reliable solutions because they minimize the number of false alarms and maintain a high recognition rate, which promote their acceptance. In this paper, we present an unobtrusive system that combines a dashboard-mount camera alongside with a watch-like wearable system recording optical (PPG) signals. Statistical analysis performed on 17 subjects demonstrates that our hybrid drowsiness detection system, which leverages both physiological and behavioral measures can reliably determine the drowsiness level with an accuracy ranging from 86% to 96% depending on the number of levels and the classification method used.
dc.titleMulti-modal driver drowsiness detection: A feasibility study
dc.typeProceedings Article
dc.type.csemdivisionsDiv-E
dc.type.csemresearchareasDigital Health
dc.identifier.urlhttp://ieeexplore.ieee.org/document/8333357/
dc.identifier.doi10.1109/BHI.2018.8333357


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