Development and Calibration of an Eye-Tracking Fixation Identification Algorithm for Immersive Virtual Reality

Abstract

Fixation identification is an essential task in the extraction of relevant information from gaze patterns; various algorithms are used in the identification process. However, the thresholds used in the algorithms greatly affect their sensitivity. Moreover, the application of these algorithm to eye-tracking technologies integrated into head-mounted displays, where the subject’s head position is unrestricted, is still an open issue. Therefore, the adaptation of eye-tracking algorithms and their thresholds to immersive virtual reality frameworks needs to be validated. This study presents the development of a dispersion-threshold identification algorithm applied to data obtained from an eye-tracking system integrated into a head-mounted display. Rules-based criteria are proposed to calibrate the thresholds of the algorithm through different features, such as number of fixations and the percentage of points which belong to a fixation. The results show that distance-dispersion thresholds between 1–1.6° and time windows between 0.25–0.4 s are the acceptable range parameters, with 1° and 0.25 s being the optimum. The work presents a calibrated algorithm to be applied in future experiments with eye-tracking integrated into head-mounted displays and guidelines for calibrating fixation identification algorithms

ARETT: Augmented Reality Eye Tracking Toolkit for Head Mounted Displays

“…Three methods from the literature for offline fixation detection are implemented. This includes I-VT using a velocity threshold similar to Reference [ 31 ], I-DT for VR as described by Llanes-Jurado et al [ 32 ] using a dispersion threshold, and I-AOI proposed by Salvucci and Goldberg [ 33 ] based on detected areas of interest. Our implementation of I-VT follows the description by Olsen [ 31 ].…”

“…We calculate a velocity for each gaze point over a specified duration and categorize the points by comparing the velocities to a specified threshold. I-DT follows the implementation by Llanes-Jurado et al [ 32 ]. It computes the angular dispersion distance over a window of a specific size in terms of its duration.…”

An Immersive Virtual Reality Game for Predicting Risk Taking through the Use of Implicit Measures

“…ET data was processed in order to obtain a classification between fixations and saccades, using the dispersion threshold (DT) algorithm with 1 • as a dispersion threshold and 0.25 s as a time window threshold [83]. A complete set of features was obtained from the classification between fixation and saccade.…”

Recognition of Customers’ Impulsivity from Behavioral Patterns in Virtual Reality

“…Eye-tracking (ET) features extracted using simple kinematic definitions and data points were classified into fixation and saccades using the algorithm presented in [71]. Moreover, the following signals were defined.…”