Citation

Tsai CY, Majumdar A, Wang Y, Hsu WH, Kang JH, Lee KY, Tseng CH, Kuan YC, Lee HC, Wu CJ, Houghton R, Cheong HI, Manole I, Lin YT, Joyce Li LY, Liu WT. Int. J. Occup. Safety Ergonomics 2022; ePub(ePub): ePub.

Copyright

(Copyright © 2022, Centralny Instytut Ochrony Pracy - Państwowy Instytut Badawczy, Publisher Informa - Taylor and Francis Group)

DOI

10.1080/10803548.2022.2135281

PMID

36281493

Abstract

PURPOSE: Current approaches via physiological features detecting aberrant driving behaviour (ADB), including speeding, abrupt steering, hard braking, and aggressive acceleration are developing. This study proposes using machine learning approaches incorporating heart rate variability (HRV) parameters to predict ADB occurrence.

METHODS: Naturalistic driving data of 10 highway bus drivers in Taiwan from their daily routes were collected for 4 consecutive days. Their driving behaviours and physiological data during a driving task were determined using a navigation mobile application and heartrate watch. Participants' self-reported data on sleep, driving-related experience, open-source data on weather and the traffic congestion level were obtained. Five machine learning models-logistic regression, random forest, naive Bayes, support vector machine, and gated recurrent unit (GRU)-were employed to predict ADBs.

RESULTS: Most drivers with ADB had low sleep efficiency (≤80%), with significantly higher scores in driver behaviour questionnaire subcategories of lapses and errors and in Karolinska Sleepiness Scale than those without ADBs. Moreover, HRV parameters were significantly different between baseline and pre-ADB event measurements. GRU had the highest accuracy: 81.16% to 84.22%.

CONCLUSIONS: Sleep deficit may be related to the increased fatigue level and ADB occurrence predicted from HRV-based models among bus drivers.

Language: en

Keywords

heart rate variability; aberrant driving behaviour; driver behaviour questionnaire; gated recurrent unit; Karolinska Sleepiness Scale