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
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 teacher head, 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".