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Automatic driver sl...
Automatic driver sleepiness detection using EEG, EOG and contextual information
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- Barua, Shaibal (författare)
- Mälardalens högskola,Inbyggda system,Mälardalens Högskola,Malardalen Univ, Sweden
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- Ahmed, Mobyen Uddin, 1976- (författare)
- Mälardalens högskola,Inbyggda system,Mälardalens Högskola,Malardalen Univ, Sweden
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- Ahlström, Christer, 1977- (författare)
- Linköpings universitet,Statens väg- och transportforskningsinstitut,Trafikanttillstånd, TIL,The Swedish National Road and Transport Research Institute (VTI), Linköping, SE, Sweden,Fysiologisk mätteknik,Tekniska fakulteten,Swedish Natl Rd and Transport Res Inst VTI, SE-58195 Linkoping, Sweden
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- Begum, Shahina, 1977- (författare)
- Mälardalens högskola,Inbyggda system,Mälardalens Högskola,Malardalen Univ, Sweden
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(creator_code:org_t)
- Elsevier Ltd, 2019
- 2019
- Engelska.
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Ingår i: Expert systems with applications. - : Elsevier Ltd. - 0957-4174 .- 1873-6793. ; 115, s. 121-135
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Transportteknik och logistik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Transport Systems and Logistics (hsv//eng)
Nyckelord
- Contextual information
- Driver sleepiness
- Electroencephalography
- Electrooculography
- Machine learning
- Accidents
- Case based reasoning
- Decision trees
- Electrophysiology
- Fisher information matrix
- Learning systems
- Nearest neighbor search
- Support vector machines
- 10-fold cross-validation
- Binary classification
- Classification accuracy
- Individual Differences
- Multi-class classification
- Physiological features
- Classification (of information)
- 914 Road: ITS och vehicle technology
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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