| Summary: | It is reported that about 20% of accidents at nuclear power plants in Korea and abroad are caused by human error. One of the main factors contributing to human error is fatigue, so it is necessary to prevent human errors that may occur when the task is performed in an improper state by grasping the status of the operator in advance. In this study, we propose a method of evaluating operator's fitness-for-duty (FFD) using various parameters including eye movement data, subjective fatigue ratings, and operator's performance. Parameters for evaluating FFD were selected through a literature survey. We performed experiments that test subjects who felt various levels of fatigue monitor information of indicators and diagnose a system malfunction. In order to find meaningful characteristics in measured data consisting of various parameters, hierarchical clustering analysis, an unsupervised machine-learning technique, is used. The characteristics of each cluster were analyzed; fitness-for-duty of each cluster was evaluated. The appropriateness of the number of clusters obtained through clustering analysis was evaluated using both the Elbow and Silhouette methods. Finally, it was statistically shown that the suggested methodology for evaluating FFD does not generate additional fatigue in subjects. Relevance to industry: The methodology for evaluating an operator's fitness for duty in advance is proposed, and it can prevent human errors that might be caused by inappropriate condition in nuclear industries. Keywords: Fatigue, Eye tracking, Fitness for duty (FFD), Clustering analysis
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