How far are we from quantifying visual attention in mobile HCI?
This addresses the problem of measuring user attention on mobile devices for HCI researchers, but it is incremental as it builds on existing eye contact detection methods.
The paper investigated the feasibility of quantifying visual attention in mobile HCI using device-integrated cameras and machine learning, identifying core challenges such as face visibility and gaze estimation, and proposed future research directions for attentive user interfaces.
With an ever-increasing number of mobile devices competing for our attention, quantifying when, how often, or for how long users visually attend to their devices has emerged as a core challenge in mobile human-computer interaction. Encouraged by recent advances in automatic eye contact detection using machine learning and device-integrated cameras, we provide a fundamental investigation into the feasibility of quantifying visual attention during everyday mobile interactions. We identify core challenges and sources of errors associated with sensing attention on mobile devices in the wild, including the impact of face and eye visibility, the importance of robust head pose estimation, and the need for accurate gaze estimation. Based on this analysis, we propose future research directions and discuss how eye contact detection represents the foundation for exciting new applications towards next-generation pervasive attentive user interfaces.