Cognitive Ability Assessment and Enhancement
Human cognitive/ mental state plays an essential role in daily performance. How to objectively and non-intrusively assess human state is always an interesting topic.
Fatigue assessment
Li, F., Chen, C. H., Xu, G., Khoo, L. P., & Liu, Y. (2019). Proactive mental fatigue detection of traffic control operators using bagged trees and gaze-bin analysis. Advanced Engineering Informatics, 42, 100987.
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Most existing eye movement-based fatigue detectors utilize statistical analysis of fixations, saccades, and blinks as inputs. Nevertheless, these parameters require a long recording time and heavily depend on eye trackers. In an effort to facilitate the 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 could 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.
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Vigilance assessment
Li, F., Lee, C. H., Chen, C. H., & Khoo, L. P. (2019). Hybrid data-driven vigilance model in traffic control center using eye-tracking data and context data. Advanced Engineering Informatics, 42, 100940.
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Vigilance decrement of traffic controllers would greatly threaten public safety. Hence, extensive studies have been conducted to establish the physiological data-based vigilance model for objectively monitoring or detecting vigilance decrement. Nevertheless, most of them use intrusive devices to collect physiological data and failed to consider context information. Consequently, these models can be used in a laboratory environment but cannot adapt to the dynamic working conditions of traffic controllers. The goal of this research is to develop an adaptive vigilance model for monitoring vigilance objectively and non-intrusively. In recent years, with advanced information and communication technology, a massive amount of data can be collected from connected daily-use items. Hence, we proposed a hybrid data-driven approach based on connected objects for establishing a vigilance model in the traffic control center and provide an elaborated case study to illustrate the method. Specifically, eye movements are selected as the primary inputs of the proposed vigilance model; the technique of the Bagged tree is adapted to generate the vigilance model. The results of the case study indicated that (1) eye metrics would be correlated with the vigilance performance subjected to the mental fatigue levels, (2) the bagged trees with the fusion features as inputs achieved a relatively stable performance under the condition of data loss, (3) the proposed method could achieve better performance than the other classic machine learning methods.