Discrete Viewpoint Control to Reduce Cybersickness in Virtual Environments
Bibliographic record
Abstract
Cybersickness in virtual reality (VR) is an on-going problem, despite recent advances in head-mounted displays (HMDs).In this thesis, we propose and evaluate a method for reducing the onset of cybersickness caused by illusions of self-motion (vection), when using stationary VR setups.Discrete viewpoint control techniques have been recently used by some VR developers and rely on reducing optic flow via inconsistent displacement.We propose two different techniques based on discrete movements in translational and rotational viewpoint movements.We ran two different user studies and measure participant cybersickness levels via the widely used Simulator Sickness Questionnaire (SSQ), as well as user reported levels of nausea, presence, and objective error rates.Overall, our results indicate that both viewpoints snapping and translation snapping significantly reduced SSQreported cybersickness levels by 40% for rotational viewpoint movement, and 50% for translational viewpoint movement.Both techniques resulted in a reduction in participant nausea levels, especially with longer VR exposure.Presence levels, error rate, and performance were not significantly different when using viewpoint snapping, or translation snapping as compared to a control condition with continuous viewpoint motion.This is a genuine pleasure to express my deepest thanks to my mentor and supervisor Dr. Robert Teather from the School of Information Technology.This thesis would not be possible without his spectacular and brilliant guides and his financial and spiritual help.I extremely grateful to have advisors like Dr. Teather who accept my supervision and really improve my research skills beyond my expectations and imagination.I appreciate his ability to understand me as an international
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".