Comparing cybersickness in virtual reality and mixed reality head-mounted displays
Bibliographic record
Abstract
Introduction: Defence Research and Development Canada is developing guidance on the use of Mixed Reality head-mounted displays for naval operations in the Royal Canadian Navy. Virtual reality head-mounted displays display graphics to the user in 3D and completely occlude the user’s view of the real world. Mixed Reality head-mounted displays overlay and integrate graphics onto the real world allowing the user to perceive the real world and rich 3D graphic elements simultaneously. Nausea and other debilitating symptoms caused by the use of head-mounted displays, known as ‘cybersickness’, is well documented during Virtual reality head-mounted display exposure and can be quite severe. However, it is not yet clear from the literature on Mixed Reality head-mounted displays whether CS differs in Virtual reality vs. Mixed Reality head-mounted displays. The objective of this study was to determine the impact of MR HMDs on CS. Method: This was done by modulating the quantity of graphics in two Mixed Reality conditions and one Virtual reality condition. Only foreground objects were graphically rendered in the first Mixed Reality condition (called ‘Mixed Reality’ condition), while the entire scene was graphically rendered in the second Mixed Reality condition (called ‘Mixed Reality +’ condition). The Virtual reality condition simulated the Mixed Reality + condition but was displayed in a Virtual reality head-mounted display. Participants observed the virtually rendered scene in one of the three conditions and reported their CS with the simulator sickness questionnaire six times throughout the 30-min experiment. We hypothesized that CS severity would increase as quantity of graphics in the display increased. Results and Discussion: Findings indicated that CS was significantly greater in the ‘Mixed Reality +’ condition compared to the ‘Mixed Reality’ and ‘Virtual reality’ conditions, providing partial evidence for our main hypothesis. Moreover, CS increased significantly and meaningfully after 25 min in the ‘Mixed Reality +’ condition. These findings indicate safe use of Mixed Reality head-mounted displays by the RCN for shore-based applications provided quantity of graphics is limited.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".