A fuzzy logics clustering approach to computing human attention allocation using eyegaze movement cue

Human's attention is an important element in human–machine interface design due to a close relationship between operator's attention and operator's work performance. However, understanding of operator's attention allocation while he or she is performing a task remains a challenging task because attention is generally unobservable, immeasurable, and uncertain. In our previous study, we demonstrated the effectiveness of using operator's eye movement information to understand attention allocation, which has made attention observable. The present paper describes our study which addressed immeasurability and uncertainty of attention. Specifically, we used eye fixation's duration to indicate operator's attention and developed a new computational model for the attention and its allocation using fuzzy logics clustering techniques. Along with the development of this model, we also developed an experiment to verify the effectiveness of the model. The result of the experiment shows that the model is promising.