ENSEMBLE-BASED MACHINE LEARNING MODELS FOR VEHICLE DRIVERS’ FATIGUE STATE DETECTION UTILIZING EEG SIGNALS

Md Mahmudul Hasan, Norizam Sulaiman, Mirza Mahfuj Hossain, Md Nahidul Islam, Sayma Khandaker, Ashraful Islam

DOI Number
https://doi.org/10.2298/FUEE2404671H
First page
671
Last page
686

Abstract


Currently, there is a great extent of academic research focused on evaluating fatigue among drivers due to its growing recognition as a major contributor to vehicle tragedies. Combining advanced features and machine learning techniques, signals from the electroencephalogram (EEG) can be analyzed to efficiently detect fatigue in the shortest possible time. This study presents an innovative approach to detect driver fatigue states utilizing ensemble-based machine learning techniques from EEG signals. Two ensemble models (Ensemble-based RUSBoosted Decision Trees and Ensemble-based Random Subspace Discriminant) were applied and compared. The study utilized an online EEG dataset of 12 individuals, with data collected during normal and fatigued driving conditions and Fast Fourier Transform was applied for feature extraction. The Ensemble-based RUSBoosted Decision Trees model achieved superior performance with 98.53% classification accuracy, compared to 83.13% for the Random Subspace Discriminant model. Multiple performance metrics were used for evaluation model performance. Finally, the proposed Ensemble-based RUSBoosted Decision Trees model outperformed Ensemble-based Random Subspace Discriminant model and existing conventional methods for fatigue state detection. This research contributes to the development of more accurate and reliable fatigue detection systems, which could potentially improve road safety by identifying fatigued drivers in real-time.

Keywords

Fatigue state detection, EEG signal, Ensemble based classifier, RUSBoosted Decision Tree, Random Subspace Discriminant

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References


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