Multinomial logistic regression model by stepwise method for predicting subjective drowsiness using performance and behavioral measures

Atsuo Murata, Yukio Ohta, Makoto Moriwaka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The aim of this study was to explore the effectiveness of physiological and behavioral evaluation measures for predicting a drivers’ subjective drowsiness based on a multinomial logistic regression model. The participants were required to steer a steering wheel and keep their vehicle to the centerline as much as they could, and to maintain the distance between their own car and a preceding car properly as much as possible using a brake or an accelerator. A number of measures were recorded during a simulated driving task, and the participants were required to report subjective drowsiness once every minute. EEG (electroencephalography), heart rate variability (RRV3), and blink frequency were the physiological measures recorded. Meanwhile, behavioral measures included neck bending angle (horizontal and vertical), back pressure, foot pressure, and tracking error in a driving simulator task. Drowsy states were predicted via a multinomial logistic regression model. Physiological and behavioral measures were independent variables in the regression model and equated to the dependent variable: subjective evaluation of drowsiness. The stepwise method was adopted for the estimation of parameters of multinomial logistic regression model. The interval used for attaining the highest prediction accuracy was a 100 s interval between 20 and 120 s before the prediction. This approach clarified that the parameters finally appeared in the multinomial logistic regression model were different among participants, which indicated that the optimal structure of the model for predicting subjective drowsiness should be different among participants.

Original languageEnglish
Title of host publicationAdvances in Physical Ergonomics and Human Factors - Proceedings of the AHFE 2016 International Conference on Physical Ergonomics and Human Factors
PublisherSpringer Verlag
Pages665-674
Number of pages10
Volume489
ISBN (Print)9783319416939
DOIs
Publication statusPublished - 2016
EventInternational Conference on Physical Ergonomics and Human Factors, AHFE 2016 - Walt Disney World, United States
Duration: Jul 27 2016Jul 31 2016

Publication series

NameAdvances in Intelligent Systems and Computing
Volume489
ISSN (Print)21945357

Other

OtherInternational Conference on Physical Ergonomics and Human Factors, AHFE 2016
CountryUnited States
CityWalt Disney World
Period7/27/167/31/16

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Keywords

  • Behavioral measure
  • Drowsiness
  • Multinomial regression model
  • Physiological measure
  • Stepwise method

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

Cite this

Murata, A., Ohta, Y., & Moriwaka, M. (2016). Multinomial logistic regression model by stepwise method for predicting subjective drowsiness using performance and behavioral measures. In Advances in Physical Ergonomics and Human Factors - Proceedings of the AHFE 2016 International Conference on Physical Ergonomics and Human Factors (Vol. 489, pp. 665-674). (Advances in Intelligent Systems and Computing; Vol. 489). Springer Verlag. https://doi.org/10.1007/978-3-319-41694-6_64