Estimation of the visual contribution to standing balance using virtual reality
Bibliographic record
Abstract
Abstract Sensory perturbations are a valuable tool to assess sensory integration mechanisms underlying balance. Implemented as systems-identification approaches, they can be used to quantitatively assess balance deficits and separate underlying causes. However, the experiments require controlled perturbations and sophisticated modeling and optimization techniques. Here we propose and validate a virtual reality implementation of moving visual scene experiments together with model-based interpretations of the results. The approach simplifies the experimental implementation and offers a platform to implement standardized analysis routines. Sway of fourteen healthy young subjects wearing a virtual reality head-mounted display was measured. Subjects viewed a virtual room or a screen inside the room, which were both moved during a series of sinusoidal or pseudo-random room or screen tilt sequences recorded on two days. In a between-subject comparison of 10x6 min long pseudo-random sequences, each applied at 5 amplitudes, our results showed no difference to a real-world moving screen experiment from the literature. We used the independent-channel model to interpret our data, which provides a direct estimate of the visual contribution to balance, together with parameters characterizing the dynamics of the feedback system. Reliability estimates of single subject parameters from six repetitions of a 6x20-s pseudo-random sequence showed strong to excellent test-retest agreement of all estimated parameters within the three repetitions of the second day and within the last of the three measurements between the days (Intraclass correlation; ICC >0.9). ICCs including the first two stimulus repetitions were consistently smaller for all parameters but the visual contribution and the feedback time delay. Sway responses strongly depended on the visual scene, where the high-contrast, abstract screen evoked larger sway as compared to the photo-realistic room. In conclusion, our proposed virtual reality approach allows researchers to reliably assess balance control dynamics including the visual contribution to balance with minimal implementation effort.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".