Differences in virtual and physical head orientation predict sickness during head-mounted display based virtual reality
Bibliographic record
Abstract
When we rotate our heads during head-mounted display (HMD) based virtual reality (VR), our virtual head tends to trail its true orientation (due to display lag). However, the exact differences in our virtual and physical head pose (DVP) vary throughout the movement. We recently proposed that large amplitude, time varying patterns of DVP were the primary trigger for cybersickness in active HMD VR. This study tests the DVP hypothesis by measuring the sickness, and estimating the DVP, produced by head rotations under different levels of imposed display lag (from 0 to 200 ms). On each trial, users made continuous, oscillatory head movements in either yaw, pitch or roll while seated inside a large virtual room. After, we used the level of imposed display lag for the condition, and the user’s own tracked head-motion data, to estimate their DVP time series data for each trial. Irrespective of the axis or the speed of the head movement, we found that DVP reliably predicted our participants experiences of cybersickness. Significant positive linear relationships were found between the severity of their sickness and the mean, peak and standard deviation of this DVP data. Thus, our DVP hypothesis appears to offer significant advantages over existing (general) theories of motion sickness in terms of understanding user experiences in HMD VR. Instead of merely speculating about the presence, or degree, of sensory conflict in a particular simulation, DVP can be used to estimate the conflict produced by the active HMD VR. Importantly, this DVP is an objective measure of the stimulation (not an internal model of the user’s sensory processing). Compared to its many competitors, DVP also appears to provide a simpler operational definition of the provocative stimulation for cybersickness (since it is focussed only on movements of the head; not the body or limbs).
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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 | 0.008 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".