Abstract
The primary goal of this work is to classify Human Cognitive State (HCS) in human-machine cooperative control systems. A total of 10 young and fit volunteers were used as the experimental subjects. A set of 9 process control task conditions were programmed on an automation-enhanced Cabin Air Management System (aCAMS) originally developed to simulate with high-fidelity the life support system in aerospace applications. The psychophysiological and performance data of the subjects were recorded while they performed process control operations in collaboration with computer-based automatic control systems. The fuzzy C-Means (FCM) algorithm was used to classify the momentary HCSs into three categories: “Good”, “Average” and “Risky” with certain degree of membership. The classification results indicated that the FCM-based classifier can achieve accurate HCS classification if the influential features are properly selected. The method proposed has potential to be applied to design adaptive task (or functional) allocation strategy in adaptive/intelligent human-machine control systems.
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