Physiological signal-based drowsiness detection using machine learning: Singular and hybrid signal approaches.

Journal: Journal of safety research
Published Date: Dec 13, 2021

Abstract

INTRODUCTION: Drowsiness is one of the main contributors to road-related crashes and fatalities worldwide. To address this pressing global issue, researchers are continuing to develop driver drowsiness detection systems that use a variety of measures. However, most research on drowsiness detection uses approaches based on a singular metric and, as a result, fail to attain satisfactory reliability and validity to be implemented in vehicles.

Authors

  • Md Mahmudul Hasan
    Queensland University of Technology (QUT), Centre for Accident Research and Road Safety Queensland (CARRS-Q), Australia; Queensland University of Technology (QUT), Institute of Health and Biomedical Innovation (IHBI), Australia. Electronic address: mahmudul.hasan.eee.kuet@gmail.com.
  • Christopher N Watling
    Queensland University of Technology (QUT), Centre for Accident Research and Road Safety Queensland (CARRS-Q), Australia; Queensland University of Technology (QUT), Institute of Health and Biomedical Innovation (IHBI), Australia.
  • GrĂ©goire S Larue
    Queensland University of Technology (QUT), Centre for Accident Research and Road Safety Queensland (CARRS-Q), Australia; Queensland University of Technology (QUT), Institute of Health and Biomedical Innovation (IHBI), Australia.

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