Effects of Elevated Height in Virtual Reality on Postural Control in the Semi-Tandem Stance
Bibliographic record
Abstract
Standing at elevated heights can elicit postural adjustments often characterized by reduced center of pressure (COP) magnitude, higher frequencies, and increased irregularity. While often attributed to postural stiffening, such changes may also reflect a shift toward more automatic control. However, most height-related studies use a feet-parallel stance, which may not be generalized to more constrained foot positions like semi-tandem, which are often encountered in real-world height situations. This study investigated the effects of acute virtual height exposure on postural control in a semi-tandem stance. Twenty young adults stood on a force platform under three conditions: (1) no VR, (2) VR sidewalk at ground level, and (3) VR elevated plank. Each participant completed six 60-second trials per condition. Repeated-measures ANOVAs revealed that wearing the VR headset alone affected postural control, with further changes under height exposure. Height was associated with increased COP frequency and irregularity, suggesting more automatic control. However, due to biomechanical constraints, the semi-tandem stance may have influenced postural strategies, resulting in increased COP variability. These findings support the use of VR to study postural threat and highlight the role of foot position in postural adaptations.
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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 | 0.001 |
| 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) | 0.002 | 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".