⚡ Quick Summary
This study introduces a groundbreaking IoT-driven biosensor system for detecting driver fatigue using EEG signals. The innovative CNN-XGBoost model achieved an impressive accuracy of 99.80%, promising to enhance road safety and healthcare quality.
🔍 Key Details
- 📊 Dataset: Substantial driver fatigue dataset
- 🧩 Features used: EEG signals transformed into RGB scalograms
- ⚙️ Technology: CNN-XGBoost Evolutionary Learning
- 🏆 Performance: Accuracy of 99.80%
🔑 Key Takeaways
- 🚗 Drowsy driving accounts for 35 to 45% of all road accidents.
- 💡 IoMT leverages biosensors and machine intelligence to enhance driver safety.
- 🔍 EEG signals provide valuable insights for fatigue detection.
- 🤖 CNN-XGBoost model significantly improves fatigue identification accuracy.
- 🌈 EEG recordings were innovatively transformed into RGB scalograms for analysis.
- 📈 The model’s performance surpasses existing methods in fatigue detection.
- 🌍 Integration into IoT frameworks optimizes data processing and enhances system performance.
- 🔮 AIoT infrastructure established for critical driving conditions.
📚 Background
Drowsy driving is a critical issue, contributing to a significant percentage of traffic accidents. The integration of the Internet of Medical Things (IoMT) offers a promising solution to this problem by utilizing advanced technologies such as biosensors and machine learning to monitor driver fatigue effectively. This approach not only aims to reduce accidents but also enhances the overall quality of life in smart societies.
🗒️ Study
The study focused on developing a novel method for detecting driver fatigue through the analysis of electroencephalogram (EEG) signals. Researchers faced challenges such as inter-individual variability in EEG data and the difficulty of collecting sufficient data during fatigue. To overcome these obstacles, they proposed the CNN-XGBoost Evolutionary Learning method, which combines the strengths of convolutional neural networks and XGBoost for improved accuracy.
📈 Results
The CNN-XGBoost model demonstrated remarkable performance, achieving an accuracy of 99.80% on the driver fatigue dataset. This level of accuracy is a significant improvement over existing methods, showcasing the potential of this innovative approach in accurately identifying fatigue from EEG signals.
🌍 Impact and Implications
The implications of this research are profound. By integrating the CNN-XGBoost model into an IoT framework, we can significantly enhance the detection of driver fatigue, thereby reducing the risk of accidents and improving road safety. This technology not only has the potential to save lives but also to optimize healthcare quality by providing timely alerts to fatigued drivers.
🔮 Conclusion
This study highlights the transformative potential of combining machine learning with IoT technologies for detecting driver fatigue. The impressive accuracy of the CNN-XGBoost model paves the way for future advancements in road safety and healthcare. As we continue to explore the integration of AI and IoT in our daily lives, the future looks promising for enhancing safety and quality of life on the roads.
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Smart IoT-driven biosensors for EEG-based driving fatigue detection: A CNN-XGBoost model enhancing healthcare quality.
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.
Author: [‘Rezaee K’, ‘Nazerian A’, ‘Ghayoumi Zadeh H’, ‘Attar H’, ‘Khosravi M’, ‘Kanan M’]
Journal: Bioimpacts
Citation: Rezaee K, et al. Smart IoT-driven biosensors for EEG-based driving fatigue detection: A CNN-XGBoost model enhancing healthcare quality. Smart IoT-driven biosensors for EEG-based driving fatigue detection: A CNN-XGBoost model enhancing healthcare quality. 2025; 15:30586. doi: 10.34172/bi.30586