Investigation and Analysis about Multimodal Fatigue Detection
Main Article Content
Keywords
multimodal, fatigue driving, fatigue detection, EEG multimodal, multimodal improvement
Abstract
Fatigue driving accounts for a significant proportion of traffic accidents. Multimodal fatigue detection offers a new effective way to detect fatigue. This article selected six articles by some criteria and reviewed them carefully. The improvements of the method include using lightweight YOLOv8 networks to process complex images; others process multimodal EEG signals via non-smooth non-negative matrix factorization (nsNMF) and Gramian angular field imaging. Attention-based networks like MMA-Net and TMU-Net are designed to fuse EEG, EDA, PPG, and EOG signals. Additionally, LSTM-based models analyze PPG and facial features, while enhanced MTCNN and PFLD algorithms improve detection accuracy and reduce individual variability. Based on the methods, the article summarizes some challenges in the multimodal fatigue detection and proposes the future of the multimodal fatigue detection. In general, the article provides a comprehensive review to guide further research about multimodal fatigue detection.
References
- Chang, W., Nie, W., Lv, R., Zheng, L., Lu, J. and Yan, G., (2024). Fatigue driving state detection based on spatial characteristics of EEG signals. Electronics, vol. 13, p. 3742.
- Cheng, W. X., Yan, G. H., Chang, W. W., Wu, B. J. and Huang, Y. N., (2025). Channel-weighted multimodal feature fusion for EEG-based fatigue driving detection Journal of Zhejiang University(Engineering Science), vol. 59, no. 9, pp. 1775-1783+1802.
- Ge, W., Ma, M. and Qu, L., (2025). Research on lightweight bimodal fatigue driving detection algorithm. Computer Simulation, vol. 42, no. 6, pp. 551-556.
- Guo, Y., Yang, K. and Wu, Y., (2025). A Multi-Modality Attention Network for Driver Fatigue Detection Based on Frontal EEG, EDA and PPG Signals. IEEE Journal of Biomedical and Health Informatics, vol. 29, no. 6, pp. 4009-4022.
- Huang, Z. C., Zhao, H. M., Chen, Y. X. and Tang, Y. S., (2021). Driver fatigue and dangerous driving detection system based on facial recognition. Mechanical & Electrical Engineering Technology, vol. 50, no. 12, pp. 143-146.
- Li, W., (2025). A high-precision fatigue driving monitoring system based on the improved MTCNN algorithm and PFLD algorithm. Intelligent Transportation Systems, vol. 9, no. 2, pp. 1-13.
- Wang, F., Guo, Q. and Fu, R., (2025). A new semi-dry electrode for driving fatigue detection on urban roads. Sensors and Actuators A: Physical, vol. 393, p. 116794.
- Xiang, H., Sun, L., Wang, Q., Liao, Z., Xie, J., Zhao, W., Qiu, J. and Zhao, H., (2025). Research on driver’s wrist motion pattern and fatigue characterization methods. International Journal of Occupational Safety and Ergonomics, pp. 1-15.
- Yan, K., Zhao, C., Shen, C., Wang, P. and Wang, G., (2022). Published. Driver Status Monitoring System with Feedback from Fatigue Detection and Lane Line Detection. 2022 IEEE 15th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC), Penang, Malaysia. IEEE, pp. 167-173.
- Yin, X. P., Zhao, X. Q., Wang, X. F., Ruan, Q. and Zhang, W., (2025). Improved YOLOv10 Algorithm for Driver Fatigue Detection Journal of Electronic Measurement and Instrumentation, pp. 1-13.
- Yu, L., Yang, X., Wei, H., Liu, J. and Li, B., (2024). Driver fatigue detection using PPG signal, facial features, head postures with an LSTM model. Heliyon, vol. 10, no. 21, p. e39479.
- Zhang, Y., Xu, X., Du, Y. and Zhang, N., (2025). TMU-Net: A Transformer-Based Multimodal Framework with Uncertainty Quantification for Driver Fatigue Detection. Sensors, vol. 25, no. 17, p. 5364.
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