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Abstract
Fatigued drivers can often cause long-distance accidents worldwide. Fatigue states are the primary cause of highway accidents. This study is conducted to provide a comprehensive and reliable fatigue state detection system to avoid accidents and make a good decision. Three machine learning algorithms were applied to seventy-six subjects' electroencephalogram (EEG) readings to test their performance. A preprocessing stage extracts relevant information before applying machine learning algorithms to the signal. Three analytical methods were employed in this study, specifically the Decision Tree, the K-Nearest Neighbors and the Random Forest. The study revealed that employing all the classifiers resulted in a satisfactory accuracy rate compared to existing state-of-the-art methods for detecting fatigue states. The classification accuracy using Decision Tree for four classes and two classes were achieved at 88.61% and 88.21% respectively, which can make this EEG-based technology a practical and dependable solution for real-time applications.
| Item Type: | Article |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Decision Tree, EEG Signal, Fatigue detection, K-Nearest Neighbor, Machine Learning, Random Forest |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Faculty of Electrical & Electronic Engineering Institute of Postgraduate Studies |
| Depositing User: | Ir. Dr. Norizam Sulaiman |
| Date Deposited: | 22 Feb 2024 06:56 |
| Last Modified: | 22 Feb 2024 06:56 |
| URI: | http://umpir.ump.edu.my/id/eprint/40473 |
| Download Statistic: | View Download Statistics |
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