Metrics for Time-to-Event Prediction of Gaze Events

Tim Rolff, Niklas Stein, Markus Lappe, Frank Steinicke, Simone Frintrop

Keywords: time-to-saccade, gaze, eye-tracking, metrics

TL;DR: We propose new metrics for time-to-saccade prediction

Abstract: In this paper, we explore metrics for the evaluation of time-to-saccade problems. We define a new sampling strategy that takes the temporal nature of gaze data and time-to-saccade problems into account, avoiding samples of the same event in different datasets. This allows us to define novel error metrics for a more intuitive evaluation of predicted durations. The metrics are defined to evaluate the consistency of a predictor and the evaluation of the error over time. We evaluate our method using a state-of-the-art method for time-to-saccade prediction along with an average baseline on three different datasets.

Submission Type: Extended Abstract

Travel Award - Academic Status: Ph.D. Student

Travel Award - Institution And Country: N/A

Travel Award - Low To Lower-middle Income Countries: No, my institution does not qualify.