Fuzzy clustering of operator functional states in human-machine systems

The primary objective of this paper is to classify the operator functional states (OFS) in the control of human-machine system. In a lab environment, 11 subjects were employed to simulate a set of process control tasks in an automated cabin air management system (aCAMS), recording psychophysiological signal and performance appearance. The fuzzy C means classification algorithm is used to classify operator functional state, which is divided into three categories, corresponding to the status of “good”, “Medium” and “bad”, and finally gives the exact classification of results, and given their respective categories the degree of membership. By selecting the appropriate input, fuzzy C means classification accuracy can be achieved within an acceptable scope. The final classification based on the results is used to adjust control strategies, achieving intelligent human-machine interaction.