Enhancing driver fatigue detection accuracy in on-road driving systems using an LSTM-DNN hybrid model with modified Z-Score and morlet wavelet - Rafiuddin, Abdubrani; Mahfuzah, Mustafa; Zarith Liyana, Zahari (Springer Nature, 2024)
Driver fatigue is a significant safety concern in transportation systems, with the potential to cause accidents. Detecting and addressing driver fatigue in real time is crucial for improving road safety. This research paper introduces an innovative method for detecting driver fatigue using electroencephalogram (EEG) signals, enhanced by the Morlet mother wavelet and modified z-score feature. The Morlet wavelet is adapted to capture both temporal and frequency information from EEG signals associated with driver fatigue, while the modified z-score feature measures abnormal EEG activity. Three deep learning models, Long Short-Term Memory (LSTM), Deep Neural Network (DNN), and LSTM-DNN, are employed to classify the data. The LSTM model captures long-term dependencies, the DNN model learns complex relationships, and the hybrid LSTM-DNN model combines their strengths to improve classification accuracy. The proposed approach demonstrates its effectiveness through comprehensive experiments, achieving high accuracy, specificity, sensitivity, F1-score, and recall in driver fatigue detection. The LSTM-DNN hybrid model showed exceptional performance, achieving an accuracy of 99.99% in classifying EEG signals. This showcases its remarkable precision in accurately categorizing the signals. Additionally, the LSTM-DNN model exhibited a specificity of 99.98% and a sensitivity of 100.00%, indicating its capability to classify driver fatigue states accurately. Furthermore, the F1-score and recall for the LSTM-DNN model were 99.99% and 100.00%, respectively.