Citation

Abdul Razak, Siti Fatimah and Sayed Ismail, S. N. M. and Yogarayan, Sumendra and Abdullah, Mohd Fikri Azli and Kamis, Noor Hisham and Abd. Aziz, Azlan (2023) Comparative Study of Machine Learning Algorithms in Classifying HRV for the Driver’s Physiological Condition. Civil Engineering Journal, 9 (9). pp. 2272-2285. ISSN 2676-6957

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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.

Item Type:	Article
Uncontrolled Keywords:	Driver Monitoring System; ADAS; Machine Learning; Vehicle Safety
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering and Technology (FET) Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	31 Oct 2023 06:07
Last Modified:	31 Oct 2023 06:07
URII:	http://shdl.mmu.edu.my/id/eprint/11772

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