Ebrahim Babaei
University of Melbourne, Melbourne, VIC, Australia
Automatically detecting attentional states is a prerequisite for designing interventions to manage attention — knowledge workers' most critical resource. As a first step towards this goal, it is necessary to understand how different attentional states are made discernible through visible cues in knowledge workers. In this paper, we demonstrate the important facial cues to detect attentional states by evaluating a data set of 15 participants that we tracked over a whole workday, which included their challenge and engagement levels. Our evaluation shows that gaze, pitch, and lips part action units are indicators of engaged work; while pitch, gaze movements, gaze angle, and upper-lid raiser action units are indicators of challenging work. These findings reveal a significant relationship between facial cues and both engagement and challenge levels experienced by our tracked participants. Our work contributes to the design of future studies to detect attentional states based on facial cues.
https://doi.org/10.1145/3313831.3376566
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2020.acm.org/)