# How far are we from quantifying visual attention in mobile HCI?

## TL;DR

This paper investigates the feasibility of quantifying visual attention in mobile HCI using machine learning and device cameras, highlighting challenges and future research directions for developing attentive user interfaces.

## Contribution

It provides a fundamental analysis of the challenges in sensing visual attention on mobile devices and discusses how eye contact detection can enable new attentive interface applications.

## Key findings

- Identifies key challenges like face visibility and gaze estimation accuracy.
- Highlights the importance of robust head pose estimation.
- Proposes future research directions for mobile attention quantification.

## Abstract

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.