Fusion of pupil and gaze-based features to estimate cognitive workload of control room operators

Process industries are highly hazardous, and these hazards often lead to accidents. Over 70% of these accidents are attributed to human errors. With the advancements in technology and changing role of operators to the one involving an emphasis on cognitive aspects, most of these errors occur due to limitations in cognitive performance. One of the major constructs to understand cognitive performance is the cognitive workload. An increase in cognitive workload often leads to degradation in performance. Eye tracking has been used in several domains to assess cognitive workload. In this work, we propose a methodology to assess cognitive workload of control room operators during tasks that involve tackling process abnormalities. The methodology employs the fusion of metrics obtained from pupil and gaze data. Our results reveal that fusion of metrics provides better accuracies of classifying cognitive workload at three levels—low, medium and high workload.