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Workshop: Gaze meets ML

Time-to-Saccade metrics for real-world evaluation

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

Keywords: [ eye-tracking ] [ time-to-saccade ] [ gaze ] [ metrics ]


In this paper, we explore multiple metrics for the evaluation of time-to-saccade problems. We define a new sampling strategy that takes the sequential nature of gaze data and time-to-saccade problems into account to avoid samples of the same event into different datasets. This also allows us to define novel error metrics, evaluating predicted durations utilizing classical eye-movement classifiers. Furthermore, we define metrics to evaluate the consistency of a predictor and the (modification) of the error over time. We evaluate our method using state-of-the-art methods along with an average baseline on three different datasets.

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