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BinoVFAR: An Efficient Binocular Visual Field Assessment Method using Augmented Reality Glasses
Conference proceeding

BinoVFAR: An Efficient Binocular Visual Field Assessment Method using Augmented Reality Glasses

Md Baharul Islam and Arezoo Sadeghzadeh
Proceedings of the 23rd Symposium on Virtual and Augmented Reality, pp.92-100
ACM Other Conferences
SVR'21: Symposium on Virtual and Augmented Reality
10-18-2021

Abstract

Computing methodologies Computing methodologies -- Computer graphics Computing methodologies -- Computer graphics -- Graphics systems and interfaces Computing methodologies -- Computer graphics -- Graphics systems and interfaces -- Virtual reality Human-centered computing Human-centered computing -- Human computer interaction (HCI) Human-centered computing -- Human computer interaction (HCI) -- Interaction paradigms Human-centered computing -- Human computer interaction (HCI) -- Interaction paradigms -- Mixed -- augmented reality Human-centered computing -- Human computer interaction (HCI) -- Interaction paradigms -- Virtual reality
Virtual Reality (VR)-based Visual Field Assessment (VFA) methods completely isolate the users from the real world, which results in nausea, eye strain, and lack of concentration and patience for the time-consuming test. In this paper, a robust binocular visual field assessment method based on novel Augmented Reality (AR) glasses is presented, namely, BinoVFAR that can simultaneously find the VF of both eyes. In this method, 60 stimuli in an arrangement of 6 rows and 10 columns randomly appear on a white background on the display of the AR glasses. These stimuli are displayed for 2 seconds that continuously change the intensities from light gray to black. Wearing the AR glasses and focusing on the central fixation point, the users are asked to click the clicker by seen a stimulus. The visible stimuli’s intensities and positions are recorded in a 6 × 10 matrix based on the users’ responses. A bi-cubic interpolation is applied to compute the binocular visual field map (as a 600 × 1000 matrix). A set of experiments (with an average accuracy of 99.93%), including repeatability and reproducibility tests (with an average Intra-class correlation coefficient (ICC) of 99.72%), are conducted to evaluate the BinoVFAR method.
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