Prediction of Oblique Saccade Trajectories Using Learned Velocity Profile Parameter Mappings

This manuscript proposes and validates two techniques for predicting the trajectory of oblique saccades using a Gaussian velocity profile model. Profile parameters and event duration are estimated at the onset of each saccade using support vector machine regression models. The proposed techniques ar...

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Bibliographic Details
Published in2020 10th Annual Computing and Communication Workshop and Conference (CCWC) pp. 0018 - 0024
Main Authors Griffith, Henry, Aziz, Samantha, Komogortsev, Oleg
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.01.2020
Subjects
curve fitting
Eye movement prediction
foveated rendering
gaze-contingent rendering
saccades
virtual reality
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Summary:This manuscript proposes and validates two techniques for predicting the trajectory of oblique saccades using a Gaussian velocity profile model. Profile parameters and event duration are estimated at the onset of each saccade using support vector machine regression models. The proposed techniques are evaluated using a set of 47,652 saccades with a mean amplitude of 12.25 degrees of the visual angle gathered from 322 subjects during a random saccade task. Numerous performance metrics are evaluated for predictions made at various fractions of the saccade duration. An average landing point estimation error of less than three degrees of the visual angle is obtained for predictions formed at 30% of the saccade duration.
DOI:10.1109/CCWC47524.2020.9031274