Bibliographic record
Abstract
Despite the continued progress in human factor (HF) considerations in virtual reality (VR), health and safety issues persist in VR applications, especially in fully immersive VR. This article reviews those issues and the recent developments to address them and discusses the adequacy of current HF considerations in VR. The four types of realities are first defined: reality, augmented reality, augmented virtual reality, and virtual reality (non-immersive, semi-immersive, and fully immersive). A brief review of the general VR applications (arts, community services, engineering, and others) is presented, followed by a more detailed review of engineering applications (industry, research, and education and training). Notable general HF issues in immersive VR are discussed, including cybersickness, hygiene, immersion injuries, and repetitive strain injuries. Manufacturer support in addressing various health and safety issues, such as in-use symptoms, post-use symptoms, repetitive strain injuries, and system alerts is highlighted. An in-depth review of the research issues related to VR is presented. Based on this study, it is concluded that significant efforts by researchers and manufacturers are still needed to address several HF issues in VR.
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 | 0.020 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".