Obstacle Circumvention Strategies During Omnidirectional Treadmill Walking in Virtual Reality
Bibliographic record
Abstract
Obstacle circumvention is an important task for community ambulation that is challenging to replicate in research and clinical environments. Omnidirectional treadmills combined with virtual reality (ODT-VR) offer a promising solution, allowing users to change walking direction and speed while walking in large, simulated environments. However, the extent to which such a setup yields circumvention strategies representative of overground walking in the real world (OVG-RW) remains to be determined. This study examined obstacle circumvention strategies in ODT-VR versus OVG-RW and measured how they changed with practice. Fifteen healthy young individuals walked while avoiding an interferer, performing four consecutive blocks of trials per condition. Distance at onset trajectory deviation, minimum distance from the interferer, and walking speed were compared across conditions and blocks. In ODT-VR, larger clearances and slower walking speeds were observed. In contrast, onset distances and proportions of right-side circumvention were similar between conditions. Walking speed increased from the first to the second block exclusively. Results suggest the use of a cautious locomotor behavior while using the ODT-VR setup, with some key features of circumvention strategies being preserved. Although ODT-VR setups offer exciting prospects for research and clinical applications, consideration should be given to the generalizability of findings to the real world.
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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 | 0.002 |
| 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".