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All Journal Civil Engineering Journal
Siti Fatimah Abdul Razak
Faculty of Information Science and Technology, Multimedia University, Jln Ayer Keroh Lama, Melaka, 75450
Civil Engineering, Building, Construction & Architecture

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Journal : Civil Engineering Journal

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Physiological-based Driver Monitoring Systems: A Scoping Review Siti Fatimah Abdul Razak; Sumendra Yogarayan; Azlan Abdul Aziz; Mohd Fikri Azli Abdullah; Noor Hisham Kamis
Civil Engineering Journal Vol 8, No 12 (2022): December
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2022-08-12-020

Abstract

A physiological-based driver monitoring system (DMS) has attracted research interest and has great potential for providing more accurate and reliable monitoring of the driver’s state during a driving experience. Many driving monitoring systems are driver behavior-based or vehicle-based. When these non-physiological based DMS are coupled with physiological-based data analysis from electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), and electromyography (EMG), the physical and emotional state of the driver may also be assessed. Drivers’ wellness can also be monitored, and hence, traffic collisions can be avoided. This paper highlights work that has been published in the past five years related to physiological-based DMS. Specifically, we focused on the physiological indicators applied in DMS design and development. Work utilizing key physiological indicators related to driver identification, driver alertness, driver drowsiness, driver fatigue, and drunk driver is identified and described based on the PRISMA Extension for Scoping Reviews (PRISMA-Sc) Framework. The relationship between selected papers is visualized using keyword co-occurrence. Findings were presented using a narrative review approach based on classifications of DMS. Finally, the challenges of physiological-based DMS are highlighted in the conclusion. Doi: 10.28991/CEJ-2022-08-12-020 Full Text: PDF
Comparative Study of Machine Learning Algorithms in Classifying HRV for the Driver’s Physiological Condition Siti Fatimah Abdul Razak; S. N. M. Sayed Ismail; Sumendra Yogarayan; Mohd Fikri Azli Abdullah; Noor Hisham Kamis; Azlan Abdul Aziz
Civil Engineering Journal Vol 9, No 9 (2023): September
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2023-09-09-013

Abstract

Heart Rate Variability (HRV) may be used as a psychological marker to assess drivers’ states from physiological signals such as an electrocardiogram (ECG), electroencephalogram (EEG), and photoplethysmography (PPG). This paper reviews HRV acquisition methods from drivers and machine learning approaches for driver cardiac health based on HRV classification. The study examines four publicly available ECG datasets and analyzes their HRV features, including time domain, frequency domain, short-term measures, and a combination of time and frequency domains. Eight machine learning classifiers, namely K-Nearest Neighbor, Decision Tree, Naive Bayes, Linear Discriminant Analysis, Support Vector Machine, Random Forest, Gradient Boost, and Adaboost, were used to determine whether the driver's state is normal or abnormal. The results show that K-Nearest Neighbor and Decision Tree classifiers had the highest accuracy at 92.86%. The study concludes by assessing the performance of machine learning algorithms in classifying HRV for the driver's physiological condition using the Man-Whitney U test in terms of accuracy and F1 score. We have statistical evidence to support that the prediction quality is different when HRV analysis applies these three sets: (i) time domain measures or frequency domain measures; (ii) frequency domain measures or short-term measures; and (iii) combining time and frequency domains or only frequency domains. Doi: 10.28991/CEJ-2023-09-09-013 Full Text: PDF
Co-Authors Azlan Abdul Aziz Mohd Fikri Azli Abdullah Noor Hisham Kamis S. N. M. Sayed Ismail Sumendra Yogarayan