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Drivers' Sleepiness Classification using Machine Learning with Physiological and Contextual data
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-7305-7169
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-3802-4721
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1212-7637
(English)In: First International Conference on Advances in Signal Processing and Artificial Intelligence ASPAI' 2019Conference paper, Published paper (Refereed)
Abstract [en]

Analysing physiological parameters together with contextual information of car drivers to identify drivers’ sleepiness is a challenging issue. Machine learning algorithms show high potential in data analysis and classification tasks in many domains. This paper presents a use case of machine learning approach for drivers’ sleepiness classification. The classifications are conducted based on drivers’ physiological parameters and contextual information. The sleepiness classification shows receiver operating characteristic (ROC) curves for KNN, SVM and RF were 0.98 on 10-fold cross-validation and 0.93 for leave-one-out (LOO) for all classifiers.

Keywords [en]
Sleepiness, Machine learning, Electroencephalography, Contextual information
National Category
Engineering and Technology Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-42235OAI: oai:DiVA.org:mdh-42235DiVA, id: diva2:1274252
Conference
First International Conference on Advances in Signal Processing and Artificial Intelligence ASPAI' 2019, 20 Mar 2019, Barcelona, Spain
Projects
VDM - Vehicle Driver MonitoringAvailable from: 2018-12-28 Created: 2018-12-28 Last updated: 2018-12-28

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No full text in DiVA

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Barua, ShaibalAhmed, Mobyen UddinBegum, Shahina

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf