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Importance of subject-dependent classification and imbalanced distributions in driver sleepiness detection in realistic conditions
University of Porto.
University of Porto.
High Institute of Engineering of Lisbon.
Swedish National Road and Transport Research Institute, Traffic and road users, Human Factors in the Transport System.ORCID iD: 0000-0003-4134-0303
2019 (English)In: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 13, no 2, p. 347-355Article in journal (Refereed) Published
Abstract [en]

The first in-depth study on the use of electrocardiogram and electrooculogram for subject-dependent classification in driver sleepiness/fatigue under realistic driving conditions is presented in this work. Since acquisitions in simulated environments may be misleading for sleepiness assessment, performing studies on road are required. For that purpose, the authors present a database resulting from a field driving study performed in the SleepEye project. Based on previous research, supervised machine learning methods are implemented and applied to 16 heart- and 25 eye-based extracted features, mostly related to heart rate variability and blink events, respectively, in order to study the influence of subject dependency in sleepiness classification, using different classifiers and dealing with imbalanced class distributions. Results showed a significantly worse performance in subject-independent classification: a decrease of similar to 40 and 20% in the detection rate of the 'sleepy' class for two and three classes, respectively. Since physiological signals are the ones that present the most individual characteristics, a subject-independent classification can be even harder to perform. Transfer learning techniques and methods for imbalanced distributions are promising approaches and need further investigation.

Place, publisher, year, edition, pages
INST ENGINEERING TECHNOLOGY-IET , 2019. Vol. 13, no 2, p. 347-355
Keywords [en]
Fatigue (human), Driver, Driver, Classification, Electrocardiography, Eye movement, In situ, Driving (veh)
National Category
Medical Laboratory and Measurements Technologies
Research subject
80 Road: Traffic safety and accidents, 84 Road: Road users
Identifiers
URN: urn:nbn:se:vti:diva-13596DOI: 10.1049/iet-its.2018.5284ISI: 000457717800013Scopus ID: 2-s2.0-85061325453OAI: oai:DiVA.org:vti-13596DiVA, id: diva2:1316365
Available from: 2019-05-17 Created: 2019-05-17 Last updated: 2019-06-27Bibliographically approved

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Ahlström, Christer

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Output format
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  • asciidoc
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