Uncertainty-Aware Scarf Plots

TL;DR

This paper introduces uncertainty-aware scarf plots for eye-tracking data analysis, accounting for positional, depth, and annotation uncertainties to improve interpretability in AR scenarios.

Contribution

The paper presents novel visualization techniques that incorporate multiple sources of uncertainty in eye-tracking data analysis, enhancing accuracy and insight.

Findings

Uncertainty-aware scarf plots improve understanding of gaze data.

The approach effectively visualizes positional and depth uncertainties.

Enhanced analysis in augmented reality scenarios.

Abstract

Multiple challenges emerge when analyzing eye-tracking data with areas of interest (AOIs) because recordings are subject to different sources of uncertainties. Previous work often presents gaze data without considering those inaccuracies in the data. To address this issue, we developed uncertainty-aware scarf plot visualizations that aim to make analysts aware of uncertainties with respect to the position-based mapping of gaze to AOIs and depth dependency in 3D scenes. Additionally, we also consider uncertainties in automatic AOI annotation. We showcase our approach in comparison to standard scarf plots in an augmented reality scenario.