Contributions of Vision in Human Postural Control: A Virtual Reality-based Study
Bibliographic record
Abstract
During human standing, it has been previously observed that information about the position and frequency of visual surround motion improves balance by reducing sway responses to external disturbances. However, experimental limitations only allowed for independent investigation of such parameters while being incapable of providing a fully immersive experience of a real environment. The aim of this study is to investigate the effect of visual information on dynamic body sway in the human upright stance by presenting perturbations through a virtual reality (VR) system. Moreover, we designed a new perturbation signal based on trapezoidal velocity (TrapV) pulses enabling us to simultaneously examine the effects of amplitude and velocity on balance control. The experiments included four different peak-to-peak amplitudes (1-10 degrees), and three velocities (2-10 degree/sec). The body angle, ankle torques and shank angles were measured and analyzed in response to each perturbation. The results reveal that stimuli with higher amplitudes evoked larger responses, while they were initially increased and reached a peak, then decreased by increasing the motion velocity of visual surround.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.002 | 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".