Regression-Based Continuous Driving Fatigue Estimation: Toward Practical Implementation

Mental fatigue in drivers is one of the leading causes that give rise to traffic accidents. Electroencephalography (EEG)-based driving fatigue studies showed promising performance in fatigue monitoring. However, complex methodologies are not suitable for practical implementation. In our simulation-based setup that retained the constraints of real driving, we took a step closer to fatigue estimation in a practical scenario. We adopted a preprocessing pipeline with low computational complexity, which can be easily and practically implemented in real time. Moreover, regression-based continuous fatigue estimation was achieved using power spectral features in conjunction with time as the fatigue label. We sought to compare three regression models and three time windows to demonstrate their effects on the performance of fatigue estimation. Dynamic time warping was proposed as a new measure for evaluating the performance of fatigue estimation. The results derived from the validation of the proposed framework on 19 subjects showed that our proposed framework was promising toward practical implementation. Fatigue estimation by the support vector regression with radial basis function kernel and 5-s window length achieved the best performance. We also provided a comprehensive analysis on the spatial distribution of channels and frequency bands mostly contributing to fatigue estimation, which can inform the feature and channel reduction for real-time fatigue monitoring in practical driving. After reducing the number of electrodes by 75%, the proposed framework retained comparable performance in fatigue estimation. This paper demonstrates the feasibility and adaptability of our proposed framework in practical implementation of mental fatigue estimation.