A hybrid deep learning model for detecting driver fatigue using electroencephalogram signals
Road accidents caused by driver fatigue are a significant public safety concern, and detecting driver fatigue is crucial for preventing such incidents. Existing methods for detecting driver fatigue are limited in their effectiveness, and there is a need for more accurate and reliable methods. This s...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit UTHM
2024
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/43675/ http://umpir.ump.edu.my/id/eprint/43675/1/A%20Hybrid%20Deep%20Learning%20Model%20for%20Detecting%20Driver%20Fatigue%20Using%20Electroencephalogram%20Signals.pdf |
| Summary: | Road accidents caused by driver fatigue are a significant public safety concern, and detecting driver fatigue is crucial for preventing such incidents. Existing methods for detecting driver fatigue are limited in their effectiveness, and there is a need for more accurate and reliable methods. This study presents a solution to the problem of accurately identifying driver fatigue using electroencephalogram (EEG) signals. The approach involves the development of a hybrid deep learning model that incorporates both a deep belief network (DBN) and a recurrent neural network (RNN). We trained and evaluated our model on a dataset of EEG signals collected from drivers in normal and fatigued states. The effectiveness of the hybrid model in accurately categorizing driver fatigue was evaluated in comparison to two other classifiers. The results of the study indicate that the hybrid model outperformed the other classifiers in terms of accuracy, sensitivity, specificity, precision, and F1 score, suggesting its superior performance. We observed that the model’s accuracy and loss remained consistent even when the number of epochs was low, indicating that the model effectively learned to classify EEG signals and did not overfit the training data. Further evaluation of the hybrid model with varying numbers of epochs revealed that the optimal number for the model was 50. Additionally, analysis of the loss function during training demonstrated that the model effectively learned to classify EEG signals without overfitting the training data. The proposed hybrid model achieved an overall accuracy of 99.98%, with perfect sensitivity (100%) and high specificity (99.95%), precision (99.95%), recall (100%), and F1 score (99.98%). These results indicate that the proposed hybrid deep learning model outperformed the individual DBN and RNN models in classifying EEG signals. Our study’s results demonstrate the potential of the proposed hybrid model to accurately detect driver fatigue, which could contribute to the development of more effective and reliable methods for preventing road accidents caused by driver fatigue. |
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