Abstract—In this paper, we propose a system for understanding attention behavior in daily living using an egocentric vision. As the visual attention models, several studies have been made on bottom-up attention and top-down attention. However, in the human brain, there are functions for specifically responding to faces and body parts. Therefore, we define a “category-specific attention model” for the specific response function and integrate the three model types of top-down attention, bottom-up attention, and category-specific attention to generate an attention map. And we extracted area of the higher saliency on the attention map. In the extracted area, we were verified whether divided attention is working or not from two approaches. The first approach detected the object by object recognition on what is seen. The second approach focused on the movement of the hand holding the food. The movement of the hand is generated using the movement orientation histogram calculated from the optical flow. In concrete terms, we evaluated the divided attention by the following 4 tasks; (i) a serial task of cutting only. (ii) as a parallel task, a task of cutting and task of checking whether the deep pan is boiling over or not. (iii) a serial task and cognitive load task. (iv) a parallel task and cognitive load task. On this occasion, there was a significant difference in both the movement orientation histogram of the hand and number of the deep pan confirmations based on the existence of a cognitive load issue, and it was confirmed that this could be used to distinguish between states when cognitive function is decreasing and the normal state.