Siddiqui, Hafeez Ur Rehman and Akmal, Ambreen and Iqbal, Muhammad and Saleem, Adil Ali and Raza, Muhammad Amjad and Zafar, Kainat and Zaib, Aqsa and Dudley, Sandra and Arambarri, Jon and Kuc Castilla, Ángel Gabriel and Rustam, Furqan UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, jon.arambarri@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2024) Ultra-Wide Band Radar Empowered Driver Drowsiness Detection with Convolutional Spatial Feature Engineering and Artificial Intelligence. Sensors, 24 (12). p. 3754. ISSN 1424-8220
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Abstract
Driving while drowsy poses significant risks, including reduced cognitive function and the potential for accidents, which can lead to severe consequences such as trauma, economic losses, injuries, or death. The use of artificial intelligence can enable effective detection of driver drowsiness, helping to prevent accidents and enhance driver performance. This research aims to address the crucial need for real-time and accurate drowsiness detection to mitigate the impact of fatigue-related accidents. Leveraging ultra-wideband radar data collected over five minutes, the dataset was segmented into one-minute chunks and transformed into grayscale images. Spatial features are retrieved from the images using a two-dimensional Convolutional Neural Network. Following that, these features were used to train and test multiple machine learning classifiers. The ensemble classifier RF-XGB-SVM, which combines Random Forest, XGBoost, and Support Vector Machine using a hard voting criterion, performed admirably with an accuracy of 96.6%. Additionally, the proposed approach was validated with a robust k-fold score of 97% and a standard deviation of 0.018, demonstrating significant results. The dataset is augmented using Generative Adversarial Networks, resulting in improved accuracies for all models. Among them, the RF-XGB-SVM model outperformed the rest with an accuracy score of 99.58%.
| Item Type: | Article |
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| Uncontrolled Keywords: | drowsiness; ultra-wideband radar; convolutional neural network; spatial features; ensemble models |
| Subjects: | Subjects > Engineering |
| Divisions: | Europe University of Atlantic > Research > Scientific Production Fundación Universitaria Internacional de Colombia > Research > Scientific Production Ibero-american International University > Research > Scientific Production Universidad Internacional do Cuanza > Research > Scientific Production University of La Romana > Research > Scientific Production |
| Depositing User: | Sr Bibliotecario |
| Date Deposited: | 17 Jun 2024 12:28 |
| Last Modified: | 17 Jun 2024 12:28 |
| URI: | http://repositorio.funiber.org/id/eprint/12747 |
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