Estimating human interest and attention via gaze analysis

In this paper we analyze joint attention between a robot that presents features of its surroundings and its human audience. In a statistical analysis of hand-coded video data, we find that the robot´s physical indications lead to a greater attentional coherence between robot and humans than do its verbal indications.We also find that aspects of how the tour group participants look at robot-indicated objects, including when they look and how long they look, can provide statistically significant correlations with their self-reported engagement scores of the presentations. Higher engagement would suggest a greater degree of interest in, and attention to, the material presented. These findings will seed future gaze tracking systems that will enable robots to estimate listeners´ state. By tracking audience gaze, our goal is to enable robots to cater the type of content and manner of its presentation to the preferences or educational goals of a particular crowd, e.g. in a tour guide, classroom or entertainment setting.