Estimating an operator's cognitive state in real time: A user modeling approach

Abstract

This paper presents a Cognitive Task Load (CTL) model designed to keep track of an operator's mental workload, both quantitatively (amount of workload) and qualitatively (cognitive state). Every second, the CTL-model updates a diagnosis of the operator's cognitive state; by integrating this model in a (semi-)autonomous robot, the robot's level of automation and user interface can be attuned to the operator's state. The CTL-model's predictions were tested in an Urban Search And Rescue (USAR) setting. The test showed insufficient workload variations to validate the model. This indicates that participants should be subjected to more “high-pressure” conditions in future trials. These results also suggest that in a realistic environment, an operator's mental workload is affected by high-level coping strategies.