Circumvention of Pedestrians While Walking in Virtual and Physical Environments
Bibliographic record
Abstract
Virtual environments (VEs) are increasingly used in the context of scientific inquiries and rehabilitation for tasks that are otherwise difficult to control or perform safely in physical environments (PEs), such as avoiding other pedestrians during locomotion. The usefulness of VEs, however, remains constrained by the extent to which they can elicit natural responses. The objectives of the study were to examine circumvention strategies in response to pedestrians approaching from different directions in the VE versus PE and to determine the effects of repeated practice on the circumvention strategies. Twelve participants were assessed over five blocks of eight trials that consisted of walking toward a target while circumventing pedestrians approaching from different directions (0°, ± 30° right or left or none) in the VE and the PE. Similar onset distances of circumvention strategy and preferred side of circumvention were observed between the two environments. Participants, however, maintained enlarged minimum distances from the interferer (13%) and walked slower (11.5%) in the VE. Repeated practice resulted in walking speed increments of 7.4% over the entire session that were similar in the VE versus PE. While the changes observed in VE may reflect the use of more cautious circumvention strategies, the similarities in strategies between the two environments and the advantages of VEs (e.g., controlled exposure, reproduction of ecologically valid conditions, and safety) suggest that virtual reality is a valuable tool to study visually guided locomotor tasks, such as pedestrian circumvention, and shows great potential for assessment and intervention in physical rehabilitation.
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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 | 0.001 |
| 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".