Attention model of between and during tasks based on wearable activity tracking

Abstract

Nowadays, the amount of information and the number of devices that people face every day are growing rapidly. In this reason, people have to put more effort to the target information among a large amount of information provided by those devices. Also, it occurs attentional dispersion to people. Therefore, Pervasive Attentive Interface(PAI) has become important in order to manage user’s attention in multi-display environments. Compare to a attentive interface, it includes user's cognitive information on changing interface. Therefore, the first step to design PAI is identifying the user’s capable amount of attention. Therefore, this paper introduces the concept of attention modeling based on physical movements reflected on devices. This paper identified users' capable attention between and during task through the data gathered from diverse built-in sensors of devices, an eyewear device, and UI events. We did experiment with different sampling settings and modeling methods to find best set.