Evaluation of Visual Acuity Measurement Based on the Mobile Virtual Reality Device

Abstract

Job-related vision standards have become an increasing concern in recent years. Mobile visual acuity measurements enable early detection and diagnosis of visual impairments and are being used around the world. However, the reliability of mobile visual acuity testing has not yet been fully demonstrated. A simple virtual reality (VR) system combining a mobile phone and a VR cardboard device has the potential as a reliable visual acuity evaluation system due to its fully controlled environment. Visual acuity measurements taken via this type of VR system were evaluated by comparing them with those obtained using the traditional Snellen chart. This study gathered data according to different parameters, including right or left eye, with or without corrective vision devices, and the learning effects of the system. The results showed that the VR system had an accuracy of up to 96.43% and 92.86% for the left and right eyes, respectively, for participants not using corrective devices. In the same group, the proposed system provided significant correlation results for Spearman’s r parameters for the left and right eyes (0.7342 and 0.8188, respectively), as compared to those obtained using a traditional approach. Therefore, despite some limitations, a mobile VR system has potential as a self-diagnostic tool for rapid, low-cost visual acuity measurements in a fully controlled environment as well as for providing historical vision data and tracing for the early detection of visual impairments or conditions.

A Visual Acuity Assessment System Based on Static Gesture Recognition and Naive Bayes Classifier

“…Despite the improved accuracy and the convenience of home testing, these methods essentially remain unchanged from the traditional approach; the reliance on manual interaction continues to be an obstacle for full automation and leads to low efficiency. • Virtual reality (VR) integration: VR technology presents a novel avenue for VA testing, with Shen's team demonstrating high accuracy rates using VR cardboard and smartphones (Shen et al, 2022). However, the need for specialized VR equipment and its associated high costs limit its accessibility, particularly for home users (An, 2022).…”