Virtual reality body motion induced navigational controllers and their effects on simulator sickness and pathfinding
Bibliographic record
Abstract
Virtual reality (VR) navigation is usually constrained by plausible simulator sickness (SS) and intuitive user interaction. The paper reports on the use of four different degrees of body motion induced navigational VR controllers, a TiltChair, omni-directional treadmill, a manual wheelchair joystick (VRNChair), and a joystick in relation to a participant's SS occurrence and a controller's intuitive utilization. Twenty young adult participants utilized all controllers to navigate through the same VR task environment in separate sessions. Throughout the sessions, SS occurrence was measured from a severity score by a standard SS questionnaire and from body sway by a center of pressure path length with eyes opened and closed. SS occurrence did not significantly differ among the controllers. However, time spent in VR significantly contributed to SS occurrence; hence, a few breaks to minimize SS should be interjected throughout a VR task. For all task trials, we recorded the participant's travel trajectories to investigate each controller's intuitive utilization from a computed traversed distance. Shorter traversed distances indicated that participants intuitively utilized the TiltChair with a slower speed; while longer traversed distances indicated participants struggled to utilize the omni-directional treadmill with a unnaturalistic stimulation of gait. Therefore, VR navigation should use technologies best suited for the intended age group that minimizes SS, and produces intuitive interactions for the participants.
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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 | 0.004 |
| 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".