A new ECG data processing approach to developing an accurate driving fatigue detection framework with heart rate variability analysis and ensemble learning

Halomoan, Junartho and Ramli, Kalamullah and Sudiana, Dodi and Gunawan, Teddy Surya and Salman, Muhammad (2023) A new ECG data processing approach to developing an accurate driving fatigue detection framework with heart rate variability analysis and ensemble learning. Information, 14 (4). pp. 1-25. ISSN 2078-2489

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Abstract

More than 1.3 million people are killed in traffic accidents annually. Road traffic accidents are mostly caused by human error. Therefore, an accurate driving fatigue detection system is required for drivers. Most driving fatigue detection studies concentrated on improving feature engineering and classification methods. We propose a novel driving fatigue detection framework concentrating on the development of the preprocessing, feature extraction, and classification stages to improve the classification accuracy of fatigue states. The proposed driving fatigue detection framework measures fatigue using a two-electrode ECG. The resampling method and heart rate variability analysis were used to extract features from the ECG data, and an ensemble learning model was utilized to classify fatigue states. To achieve the best model performance, 40 possible scenarios were applied: a combination of 5 resampling scenarios, 2 feature extraction scenarios, and 4 classification model scenarios. It was discovered that the combination of a resampling method with a window duration of 300 s and an overlap of 270 s, 54 extracted features, and AdaBoost yielded an optimum accuracy of 98.82% for the training dataset and 81.82% for the testing dataset. Furthermore, the preprocessing resampling method had the greatest impact on the model’s performance; it is a new approach presented in this study.

Item Type:	Article (Journal)
Uncontrolled Keywords:	fatigue detection; resampling; electrocardiogram; fatigue driving; heart rate variability analysis
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User:	Prof. Dr. Teddy Surya Gunawan
Date Deposited:	27 Jun 2023 15:08
Last Modified:	27 Jun 2023 15:10
URI:	http://irep.iium.edu.my/id/eprint/105242

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