Influence of bone-conducted vibration on simulator sickness in virtual reality
Bibliographic record
Abstract
Use of virtual reality (VR) technology is often accompanied by a series of unwanted symptoms, including nausea and headache, which are characterised as 'simulator sickness'. Sensory mismatch has been thought to lie at the heart of the problem and recent studies have shown that reducing cue mismatch in VR can have a therapeutic effect. Specifically, electrical stimulation of vestibular afferent nerves (galvanic vestibular stimulation; GVS) can reduce simulator sickness in VR. However, GVS poses a risk to certain populations and can also result in negative symptoms in normal, healthy individuals. Here, we tested whether noisy vestibular stimulation through bone-vibration can also reduce symptoms of simulator sickness. We carried out two experiments in which participants performed a spatial navigation task in VR and completed the Simulator Sickness Questionnaire over a series of trials. Experiment 1 was conducted using a high-end projection-based VR display, whereas Experiment 2 involved the use of a consumer head mounted display. During each trial, vestibular stimulation was either: 1) absent; 2) coupled with large angular accelerations of the projection camera; or 3) applied randomly throughout each trial. In half of the trials, participants actively navigated using a motion controller, and in the other half they were moved passively through the environment along pre-recorded motion trajectories. In both experiments we obtained lower simulator sickness scores when vestibular stimulation was coupled with angular accelerations of the camera. This effect was obtained for both active and passive movement control conditions, which did not differ. The results suggest that noisy vestibular stimulation can reduce simulator sickness, and that this effect appears to generalize across VR conditions. We propose further examination of this stimulation technique.
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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 | 0.005 |
| 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 | 0.000 | 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".