Proactive mental fatigue detection of traffic control operators using bagged trees and gaze-bin analysis

Most of existing eye movement-based fatigue detectors utilize statistical analysis of fixations, saccades, and blinks as inputs. Nevertheless, these parameters require long recording time and heavily depend on eye trackers. In an effort to facilitate proactive detection of mental fatigue, we introduced a complemental fatigue indicator, named gaze-bin analysis, which simply presents the eye-tracking data with histograms. A method which engaged the gaze-bin analysis as inputs of semisupervised bagged trees was developed. A case study in a vessel traffic service center demonstrated that this approach can alleviate the burden of manual labeling as well as improve the performance of fatigue detection model. In addition, the results show that the approach can achieve an excellent accuracy of 89%, which outperformed other methods. In general, this study provided a complemental indicator for detecting mental fatigue as well as enabled the application of a low sampling rate eye tracker in the traffic control center.