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Importance of subject-dependent classification and imbalanced distributions in driver sleepiness detection in realistic conditions
Univ Porto, Portugal.
Univ Porto, Portugal; INESC TEC, Portugal.
CardioID Technol Lda, Portugal; High Inst Engn Lisbon, Portugal; Inst Telecommun, Portugal.
Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Swedish Natl Rd and Transport Res Inst VTI, Linkoping, Sweden.
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
Institution of Engineering and Technology, 2019. Vol. 13, no 2, p. 347-355
Keywords [en]
medical signal processing; medical signal detection; cardiology; learning (artificial intelligence); electro-oculography; eye; electrocardiography; feature extraction; pattern classification; signal classification; subject-dependent classification; imbalanced distributions; driver sleepiness detection; realistic conditions; in-depth study; electrocardiogram; electrooculogram; driver sleepiness; fatigue; realistic driving conditions; sleepiness assessment; supervised machine learning methods; 25 eye-based extracted features; heart rate variability; subject dependency; sleepiness classification; imbalanced class distributions; worse performance; subject-independent classification; detection rate
National Category
Applied Psychology
Identifiers
URN: urn:nbn:se:liu:diva-154563DOI: 10.1049/iet-its.2018.5284ISI: 000457717800013Scopus ID: 2-s2.0-85061325453OAI: oai:DiVA.org:liu-154563DiVA, id: diva2:1290506
Note

Funding Agencies|European Union [688900]; Swedish Governmental Agency for Innovation Systems [2011-03994]; ERDF - European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme; National Funds through the Portuguese funding agency, FCT - Fundacao para a Ciencia e a Tecnologia [POCI-01-0145-FEDER-030707]

Available from: 2019-02-20 Created: 2019-02-20 Last updated: 2019-02-27Bibliographically approved

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