Luminance and thresholding limitations of virtual reality headsets for visual field testing
Bibliographic record
Abstract
PURPOSE: To investigate the luminance capacity and achievable threshold levels of commercially employed virtual reality (VR) devices for visual field testing. METHODS: This two-part study included (1) a literature review of VR headsets used for perimetry with luminance data extracted from technical specifications in publications and manufacturers; and (2) empirical evaluation of three most employed VR headsets in the literature using a custom virtual testing environment. RESULTS: Three most employed VR devices for visual field testing were Pico Neo, Oculus Quest, and HTC Vive. The maximum reported luminance was 250 cd/m2 for the HTC Vive Pro. Information on luminance measurement was not consistently available, reporting only handheld luminance meters. Empirical measurements show that handheld luminance meters significantly overestimate luminance compared to standard spectroradiometers. Measured luminance varies significantly across aperture size and decreases for peripheral stimuli up to 30 degrees peripherally. Assuming conventional background of 10 cd/m2, the best performance with lowest possible thresholding was with HTC Vive at 16dB, corresponding to luminance of 80 cd/m2 centrally. Oculus Quest 2 and Pico Neo 3 had minimum threshold of 20dB. CONCLUSION: Commercially available VR devices do not meet luminance requirements or threshold sensitivities for visual field testing. Current VR technology is not designed-nor has the capacity-to threshold at mid-to-low dB ranges, which limits accuracy in diagnosing and monitoring defects seen in glaucoma. Translational Relevance: This study highlights the technical limitations of current commercially available VR devices for visual field testing and significant variables in evaluating luminance performance in these devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".