Turning Strategies While Walking on an Omnidirectional Treadmill With Virtual Reality
Bibliographic record
Abstract
Omnidirectional treadmills with virtual reality (ODTVR) offer a promising alternative for training complex locomotor tasks that require changes in direction, but their effects on locomotion remain unclear. This study examined the impact of a motorized self-paced ODTVR setup on axial body segment coordination and spatiotemporal gait parameters during a turning while walking task. Kinematic data of twenty healthy young adults were collected as they walked and turned in different directions and under three conditions (ODTVR, as well as omnidirectional treadmill (ODT) without VR, and overground (OVG) without VR). Results revealed a similar sequence of segment reorientation across conditions, which was initiated with the head, followed by the thorax, pelvis and heading. However, earlier onsets (242-250 ms), and marginally smaller segment reorientation amplitudes (2-3 $^{\circ }\text {)}$ , were observed in the ODTVR vs. other conditions. Slower walking speeds and shorter step lengths were generally observed in the ODT vs. OVG condition, with further decreases in the ODTVR condition. Standardized questionnaires revealed that ODTVR walking was perceived as non-anxiogenic and easy to use, but that it induced a moderate sense of presence and elevated simulator sickness. Findings indicate that alterations in body segment coordination during ODTVR walking, while subtle, are primarily caused by VR, but walking speed and step length were affected by both the ODT and VR. While the differences induced by ODTVR walking should be taken into consideration, the present findings support its use for clinical and experimental purposes. They also highlight the importance of accounting for habituation and user experience in future applications.
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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 | 0.000 |
| 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".