Augmenting Virtual Spatial UIs with Physics- and Direction-Based Visual Motion Cues to Non-Disruptively Mitigate Motion Sickness
Bibliographic record
Abstract
The use of Virtual Reality (VR) technology in moving platforms such as vehicles can be difficult due to significant issues around motion sickness, partly due to the physical motion being occluded in VR. The use of visual cues within VR can mitigate this motion sickness. However, these additional visual cues can disrupt users. This paper presents two studies conducted on a yaw-motion platform, investigating the effectiveness of our efforts to manipulate the visually perceived motion of spatial UIs within VR environments using novel physics-based cues, reducing motion sickness with less distraction on tasks. The first study validates our design’s effectiveness, while the second compares it with existing solutions (speed/direction-base cues) regarding motion sickness and distraction levels among VR users. Our findings show that our design can relieve rotational motion sickness while concurrently diminishing distraction. This study serves as a valuable starting point for research into non-disruptively interleaving motion cues with spatial UI components within VR environments to mitigate motion sickness, emphasizing the delicate equilibrium between motion sickness mitigation and preserving the user experience.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".