Evaluation of cybersickness in a passive walking virtual reality cognitive exercise
Bibliographic record
Abstract
bWell is an interactive immersive research platform targeting cognitive assessment and remediation, developed at the National Research Council Canada. Following a common need from collaborators for scenes with user being passively displaced while seated, the present study evaluates the tolerability of a stroll scenario with imposed head movement while the participant is physically seated. Twenty-six healthy adults performed three exercises containing linear and sinusoidal walking vection with or without an attention task forcing yaw head movement. Results indicate that the system is generally well tolerated. There was a significant difference in reported cybersickness symptoms between the different exercises with a higher level of symptoms reported when angular acceleration was present. With regards to the severity of the symptoms, no obvious link has been observed. The progression of symptoms was not always linear and could be grouped in three different profiles: 1) constant, 2) progression followed by either a plateau or regression and 3) continuous progression. These findings extend the design possibilities and opportunity for bWell cognitive exercises to include more challenging motion patterns, including passive displacement and angular visual scanning with a more vulnerable population.
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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 | 0.001 |
| 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 | 0.000 | 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".