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Automatic driver sleepiness detection using EEG, EOG and contextual information
Malardalen Univ, Sweden.
Malardalen Univ, Sweden.
Linköpings universitet, Institutionen för medicinsk teknik, Fysiologisk mätteknik. Linköpings universitet, Tekniska fakulteten. Swedish Natl Rd and Transport Res Inst VTI, SE-58195 Linkoping, Sweden.
Malardalen Univ, Sweden.
2019 (engelsk)Inngår i: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 115, s. 121-135Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

The many vehicle crashes that are caused by driver sleepiness each year advocates the development of automated driver sleepiness detection (ADSD) systems. This study proposes an automatic sleepiness classification scheme designed using data from 30 drivers who repeatedly drove in a high-fidelity driving simulator, both in alert and in sleep deprived conditions. Driver sleepiness classification was performed using four separate classifiers: k-nearest neighbours, support vector machines, case-based reasoning, and random forest, where physiological signals and contextual information were used as sleepiness indicators. The subjective Karolinska sleepiness scale (KSS) was used as target value. An extensive evaluation on multiclass and binary classifications was carried out using 10-fold cross-validation and leave-one-out validation. With 10-fold cross-validation, the support vector machine showed better performance than the other classifiers (79% accuracy for multiclass and 93% accuracy for binary classification). The effect of individual differences was also investigated, showing a 10% increase in accuracy when data from the individual being evaluated was included in the training dataset. Overall, the support vector machine was found to be the most stable classifier. The effect of adding contextual information to the physiological features improved the classification accuracy by 4% in multiclass classification and by and 5% in binary classification. (C) 2018 Elsevier Ltd. All rights reserved.

sted, utgiver, år, opplag, sider
PERGAMON-ELSEVIER SCIENCE LTD , 2019. Vol. 115, s. 121-135
Emneord [en]
Driver sleepiness; Electroencephalography; Electrooculography; Contextual information; Machine learning
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-152588DOI: 10.1016/j.eswa.2018.07.054ISI: 000448097700009OAI: oai:DiVA.org:liu-152588DiVA, id: diva2:1262147
Merknad

Funding Agencies|VINNOVA (Swedish Governmental Agency for Innovation Systems)

Tilgjengelig fra: 2018-11-09 Laget: 2018-11-09 Sist oppdatert: 2018-11-09

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