Development of virtual reality environments for evaluation of visual disability Alexander Lam, Elaine To, Christopher Leung Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong Abstract Code: P-FS-118 Purpose Examples captured from the three VR environments are shown in Figures 1-3. Tasks (1) and (2) were performed in simulated daylight and nightlight conditions. Obstacles (including pedestrians and vehicles) are randomly assigned in different locations to minimize learning effect (Figure 4). Parameters such as the time required to complete a task (an example is shown in Figure 5), the number of collisions with the VR objects in the VR environments, or the angle of collision are recorded to calculate a performance score. The performance score can then be used to quantify the visual performance for assessment, grading and monitoring of visual disability of a person. While the impact of visual field defects on vision-related quality of life in glaucoma patients has been investigated with patient-reported outcomes, the strength of association is weak [1]. This is likely related to a wide individual variability in subjective perception about the quality of life [2]. Developing a clinical test that can integrate different components of vision for objective measurement of visual disability experienced by patients relevant to their quality of life is an unmet need in clinical management of glaucoma. We have developed a virtual reality (VR) platform to simulate daily tasks and measure the visual disability. Figure 4. Modification of the VR environments in daytime (left) and night time (right) to minimize learning effect Methods We developed three VR environments using Unity (Unity Technologies, CA) and HTC Vive (HTC Corporation, Taiwan) simulating daily tasks including (1) navigating a city area; (2) walking up and down 2 flights of stairs; and (3) locating an object of interest from a shelf for measurement of visual disability. These environments are programmed so that the motion sensors in the VR headset can detect the subject’s head orientation and translate into corresponding viewing direction in the VR environment. In task (1), subjects navigate a city area loaded with obstacles, pedestrians and vehicles. Walking speed (ranged from 0.25m/s to 1m/s) can be adjusted by a remote controller. In task (2), subjects are asked to walk up and down two flights of stairs, each with 15 steps (3m in width, 30cm in length and 15cm in height). In task (3), subject are asked to locate 10 different objects from a 6-level supermarket rack, 3.3m in width and height. Figure 5. Association between the time required to complete a task and the average visual field mean deviation Results Figure 1. Navigating a city area Conclusions Generating VR environments for clinical testing of visual performance will provide a new paradigm to quantify and monitor visual disability in patients with glaucoma and other ocular diseases, which can empower clinicians to better understand from a patient’s perspective how visual impairment impacts the activities of daily living. Figure 2. Walking up and down the stairs References 1. Qiu M, Wang SY, Singh K, Lin SC. Association between visual field defects and quality of life in the United States. Ophthalmology. 2014;121:733-40. 2. Medeiros FA, Gracitelli CP, Boer ER, Weinreb RN, Zangwill LM, Rosen PN. Longitudinal changes in quality of life and rates of progressive visual field loss in glaucoma patients. Ophthalmology. 2015;122:293-301.  Figure 3. Identifying and selecting objects from a supermarket rack Disclosure Stock Shareholder: ACE VR Ltd. (HK) Correspondence: Alexander Lam Email: alexander@cuhk.edu.hk Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong 4/F, Hong Kong Eye Hospital, 147K Argyle Street, Kowloon, Hong Kong