Abstract
Driver fatigue is a significant danger to road safety, resulting in numerous accidents and fatalities on a global scale. To tackle this problem, researchers have been exploring innovative techniques for identifying and preventing driver drowsiness during real-world driving situations. This study proposes a novel approach for detecting driver fatigue by merging EEG (Electroencephalogram) data from sensors positioned behind the ear with computer vision-based analysis of facial characteristics. Behind-the-ear (BTE) EEG provides a more practical and user-friendly alternative than traditional scalp EEG methods. In addition to Ear-EEG signals, computer vision technology enhances fatigue detection accuracy by examining drivers’ facial images while driving. The study introduces a custom-designed wearable device for gathering EEG data from four sensor electrodes behind the ear. Continuous wavelet transform (CWT) converts these EEG signals into scalograms. These scalograms and facial images captured by a camera focused on key facial areas such as the left eye, right eye, mouth, and entire face serve as inputs for a deep learning model developed for identifying driver fatigue. Subsequently, a comparative assessment is conducted to gauge the performance of the proposed system when using only Ear-EEG signals, only camera images, or a combination of both data sources. The test results validate the practicality and effectiveness of the proposed system in identifying driver fatigue. Additionally, a companion smartphone application has been developed to simplify and promptly monitor and alert drivers when they exhibit drowsiness while driving in traffic.
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References
Nguyen, H.T., Mai, N.D., Lee, B.G., Chung, W.Y.: Behind-the-ear EEG-based wearable driver drowsiness detection system using embedded tiny neural networks. IEEE Sens. J. (2023). https://doi.org/10.1109/JSEN.2023.3307766
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Xiang, J., Gengming, Z.: Joint face detection and facial expression recognition with MTCNN. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE). IEEE (2017)
Sinha, S., et al.: Spectral decomposition of seismic data with continuous wavelet transform. Geophysics 70(6), P19–P25 (2005)
Acknowledgements
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2019R1A2C1089139).
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Mai, ND., Nguyen, HT., Chung, WY. (2024). Ear-EEG Based-Driver Fatigue Detection System Augmented by Computer Vision. In: Choi, B.J., Singh, D., Tiwary, U.S., Chung, WY. (eds) Intelligent Human Computer Interaction. IHCI 2023. Lecture Notes in Computer Science, vol 14531. Springer, Cham. https://doi.org/10.1007/978-3-031-53827-8_10
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DOI: https://doi.org/10.1007/978-3-031-53827-8_10
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