Towards benchmarking VR sickness: A novel methodological framework for assessing contributing factors and mitigation strategies through rapid VR sickness induction and recovery
Bibliographic record
Abstract
Virtual Reality (VR) sickness remains a significant challenge in the widespread adoption of VR technologies. The absence of a standardized benchmark system hinders progress in understanding and effectively countering VR sickness. This paper proposes an initial step towards a benchmark system, utilizing a novel methodological framework to serve as a common platform for evaluating contributing VR sickness factors and mitigation strategies. Our benchmark, grounded in established theories and leveraging existing research, features both small and large environments. In two research studies, we validated our system by demonstrating its capability to (1) quickly, reliably, and controllably induce VR sickness in both environments, followed by a rapid decline post-stimulus, facilitating cost and time-effective within-subject studies and increased statistical power, (2) integrate and evaluate established VR sickness mitigation methods — static and dynamic field of view reduction, blur, and virtual nose — demonstrating their effectiveness in reducing symptoms in the benchmark and their direct comparison within a standardized setting. Our proposed benchmark also enables broader, more comparative research into different technical, setup, and participant variables influencing VR sickness and overall user experience, ultimately paving the way for building a comprehensive database to identify the most effective strategies for specific VR applications. • Novel methodological framework to standardize and benchmark VR sickness assessment. • Reliable and quick VR sickness induction with rapid recovery post-stimulus. • Minimal carry-over effects for cost- and time-effective within-subject studies. • Systematic comparison of static/dynamic FOV reduction, blur, virtual nose techniques. • Paving the way for building a comprehensive VR sickness database.
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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 | 0.057 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".