TY  - JOUR
PY  - 2022//
TI  - Machine learning model for aberrant driving behaviour prediction using heart rate variability: a pilot study involving highway bus drivers
JO  - International journal of occupational safety and ergonomics
A1  - Tsai, Cheng-Yu
A1  - Majumdar, Arnab
A1  - Wang, Yija
A1  - Hsu, Wen-Hua
A1  - Kang, Jiunn-Horng
A1  - Lee, Kang-Yun
A1  - Tseng, Chien-Hua
A1  - Kuan, Yi-Chun
A1  - Lee, Hsin-Chien
A1  - Wu, Cheng-Jung
A1  - Houghton, Robert
A1  - Cheong, He-In
A1  - Manole, Iulia
A1  - Lin, Yin-Tzu
A1  - Joyce Li, Lok-Yee
A1  - Liu, Wen-Te
SP  - ePub
EP  - ePub
VL  - ePub
IS  - ePub
N2  - 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. <br><br>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. <br><br>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%. <br><br>CONCLUSIONS: Sleep deficit may be related to the increased fatigue level and ADB occurrence predicted from HRV-based models among bus drivers.<p />  <p>Language: en</p>
LA  - en
SN  - 1080-3548
UR  - http://dx.doi.org/10.1080/10803548.2022.2135281
ID  - ref1
ER  -