Fatigue State Detection Through Multiple Machine Learning Classifiers Using EEG Signal
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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Arqii Publication
2023
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/40473/1/AMS-480_Published_3_December_2023.pdf http://umpir.ump.edu.my/id/eprint/40473/ https://arqiipubl.com/ojs/index.php/AMS_Journal/article/view/480 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|