No abstract

Virtual reality as a research environment has seen a boost in its popularity during the last decades. Not only the usage fields for this technology have broadened, but also a research niche has appeared as the hardware improved and became more affordable. Experiments in vision research are constructed upon the basis of accurately displaying stimuli with a specific position and size. For classical screen setups, viewing distance and pixel position on the screen define the perceived position for subjects in a relatively precise fashion. However, projection fidelity in HMDs strongly depends on eye and face physiological parameters. This study introduces an inexpensive method to measure the perceived field of view and its dependence upon the eye position and the interpupillary distance, using a super wide angle camera. Measurements of multiple consumer VR headsets show that manufacturers’ claims regarding field of view of their HMDs are mostly unrealistic. Additionally, we performed a “Goldmann” perimetry test in VR to obtain subjective results as a validation of the objective camera measurements. Based on this novel data, the applicability of these devices to test humans’ field of view was evaluated.

Purpose The aim of the current study was to develop and validate an automated contrast sensitivity (CS) test using a live- detection of optokinetic nystagmus (OKN) and an adaptive psychometric procedure. In addition, the study sought to replicate the known effect of defocus on CS for the OKN-based measurements in emmetropic participants. Methods Fifteen participants viewed a horizontally moving grating while their eyes were tracked with an infra-red (IR) eye-tracker. To simulate the clinical conditions of the CS measurements, the participants were stimulated monocularly as the left eye was occluded by an IR filter. The horizontal eye position was continuously analyzed for OKN responses, and the stimulus contrast was changed by an adaptive psychometric method depending on the outcome. Furthermore, the newly proposed OKN live-detection was verified against an offline analysis and an expert-observer judgement. The OKN-based CS was measured for six spatial frequencies at normal vision and three levels of defocus using spherical convex lenses. Results The newly proposed OKN live-detection method showed a sufficient detection performance for implementation of adaptive procedures, and the detection rate is similar or better compared to offline detection methods. Spatial frequency and defocus had a significant effect on the OKN-based CS ( P < 0.0001 for both). Conclusions The current study presents a novel method to measure motion CS in an automated way, combining the real-time detection of OKN and an adaptive psychometric procedure. Furthermore, the known effect of defocus on CS was successfully replicated with the newly developed tool. Translational Relevance OKN-based CS is a novel approach to assess spatial vision, which is sensitive to subtle effects of defocus, allowing use with nonverbal patients and infants. Furthermore, the newly developed tool may improve the performance of such measurements.

No abstract