You can then use the transform tools to position, rotate and scale the object(s). If your environment model already includes your fixation objects, then you can skip this step, otherwise, go to “File- Add” and add objects that you would like to measure eye tracking data on. For more information on getting models out of Sketchfab see our blog post on this topic. You can find 3D models off of websites like Sketchfab, use 3D modeling software, or various other sources. We will start first by loading your environment and fixation objects into the configuration tool, Vizard’s “Inspector”.įirst, open up Inspector (in Vizard- under “Tools- Inspector”) and load your environment model by going to “File- Open”.
#VIVE EYE TRACKING INSTALL#
When you first install the VR Eye Tracking Analytics Lab, you will have a folder with some template scripts you can use to build an eye tracking experiment, as well as folders to store your resources, saved recordings and data files. Getting Started and Setting Up Your Scene Once set up, you can collect eye tracking data related to your 3D assets such as number of fixations (as well as average and total fixation time), pupil diameter size, time stamps, gaze intersect visualization and position data, 3D gaze path visualizations, and more! For more information on this or any WorldViz products contact a video guide of this tutorial click here This article will introduce you to the most important features of the WorldViz VR Eye Tracking Analytics Lab and show you how you can easily modify the included template to use your own 3D assets.
#VIVE EYE TRACKING PRO#
Tags: eye tracking, eye tracking experiment, eyetracking, vive pro eye. This is a 2020 ARVO Annual Meeting abstract.Posted July 13, 2020. While providing a powerful tool for many novel functionalities the eye tracking of the HTC Pro Eye’s accuracy and precision have to be taken into account in experiment planning, specifically when including tracking in the periphery, or head movements. Under head-movement one-way ANOVA revealed a significant difference in precision between phases with moving head, compared to phases of static head (F (1,18) = 253.03, p < 0.0001 mean moving: 6.21°, SD: 0.77 mean static 1.80°, SD: 0.42). Comparing accuracy and precision horizontally in a one-way ANOVA, revealed that accuracy differed between the centre and periphery (F (4,50) = 3.35, p = 0.02). In the head-free condition, phases with head movement were compared to phases of static head in accuracy and precision within each trial.Īverage accuracy of the eye-tracker in the head-fixed condition was 4.16°, SD: 3.23 while the precision had a mean of 2.17°, SD: 0.75. Precision was calculated as the RMS (Root Mean Square) of successive measurements. In the head-fixed condition accuracy was calculated as the mean offset between the target-eye vector and gaze-target vector. Eleven subjects were tested in the first condition ten subjects were tested in the second condition.
In both conditions the displayed targets were presented in a randomized order with 5 repetitions.
In the head-free condition targets were positioned in a world-fixed coordinate system, and subjects were motivated to move the gaze together with the head in a natural way towards an appearing target and fixate it. In the first condition, target position was fixed to headset, and subjects were asked to saccade to an appearing target, while keeping the head still. The current study evaluates accuracy and precision of gaze estimates over the whole visual field in head-restrained, as well as head-free conditions.Īccuracy and precision were tested at 25 sample positions spanning +- 25° horizontally and +- 25° vertically in two separate conditions: head-restrained, as well as head-free. But, many of those functions, such as gaze-contingent content presentation, require a specific level of eye tracking accuracy. Eye tracking opens up a variety of novel functions in virtual reality.
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