Khosro Rezaee
1* 
, Asmar Nazerian
2, Hossein Ghayoumi Zadeh
3, Hani Attar
4, Mohamadreza Khosravi
5,6,7, Mohammad Kanan
81 Department of Biomedical Engineering, Meybod University, Meybod, Iran
2 Department of Engineering, Islamic Azad University, Qods Branch, Tehran, Iran
3 Department of Electrical Engineering, Faculty of Engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran
4 Faculty of Engineering, Zarqa University, Zarqa, Jordan
5 Department of Medical Physics and Engineering, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
6 IT Services, Lidoma Sanat Mehregan Part Ltd., Shiraz 71581, Fars, Iran
7 SPUL4PH Lab, Weifang University of Science and Technology, Weifang 262700, China
8 College of Engineering, University of Business and Technology, Jeddah, Saudi Arabia
Abstract
Introduction: Drowsy driving is a significant contributor to accidents, accounting for 35 to 45% of all crashes. Implementation of an internet of things (IoT) system capable of alerting fatigued drivers has the potential to substantially reduce road fatalities and associated issues. Often referred to as the internet of medical things (IoMT), this system leverages a combination of biosensors, actuators, detectors, cloud-based and edge computing, machine intelligence, and communication networks to deliver reliable performance and enhance quality of life in smart societies.
Methods: Electroencephalogram (EEG) signals offer potential insights into fatigue detection. However, accurately identifying fatigue from brain signals is challenging due to inter-individual EEG variability and the difficulty of collecting sufficient data during periods of exhaustion. To address these challenges, a novel evolutionary optimization method combining convolutional neural networks (CNNs) and XGBoost, termed CNN-XGBoost Evolutionary Learning, was proposed to improve fatigue identification accuracy. The research explored various subbands of decomposed EEG data and introduced an innovative approach of transforming EEG recordings into RGB scalograms. These scalogram images were processed using a 2D Convolutional Neural Network (2DCNN) to extract essential features, which were subsequently fed into a dense layer for training.
Results: The resulting model achieved a noteworthy accuracy of 99.80% on a substantial driver fatigue dataset, surpassing existing methods.
Conclusion: By integrating this approach into an IoT framework, researchers effectively addressed previous challenges and established an artificial intelligence of things (AIoT) infrastructure for critical driving conditions. This IoT-based system optimizes data processing, reduces computational complexity, and enhances overall system performance, enabling accurate and timely detection of fatigue in extreme driving environments.