Multimodal Mitigations for Cybersickness in Motion Base Simulators
Bibliographic record
Abstract
1. Abstract Background Virtual reality (VR) technologies that integrate with motion-base simulators (MBS) have the potential to accelerate personnel training and enhance workplace safety. Motion sickness on an MBS is a widespread problem with vast individual differences that are likely related to idiosyncrasies in estimates of head, body, and vehicle motions. When combined with head-mounted VR, we term the emergent symptoms ‘cybersickness’. Methods We conducted two experiments that evaluated cybersickness mitigations in an MBS. In Experiment 1 (N = 8), we tested the effectiveness of a light-touch body harness attached to a mobile-elevated work platform (MEWP) simulator during two nauseogenic VR tasks. In Experiment 2 (N = 14, 7 of whom completed Experiment 1), we tested the effectiveness of a dynamic field-of-view (dFOV) modifier that adaptively restricted the FOV for vehicle rotations in the same VR tasks. We gathered subjective sickness data and qualitative evaluations of the mitigations after the fact. Results We observed a reduced level of sickness in both Experiment 1 and 2 when mitigations were applied. In Experiment 1, the use of a harness led to a mild decrease in total cybersickness of between 3-11%, which was only significant for the nausea dimension. In Experiment 2, the use of dFOV imparted a large benefit to comfort, up to a 45% improvement. Both mitigations primarily improved comfort in a bumpy trench traversal task. Conclusions Cybersickness mitigations can help to deliver VR training for longer, and to more users. The type of content undertaken should be considered when employing new mitigations.
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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 | 0.001 | 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) | 0.004 | 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".