Posturographic measures in healthy young adults during quiet sitting in comparison with quiet standing
Bibliographic record
Abstract
Measures of postural steadiness - known as posturography - are commonly used for balance assessment during quiet standing. Although quiet sitting balance may be studied via posturography as well, this has not been done to date. As such, the purpose of this study was to characterize the posturography during quiet sitting in comparison with quiet standing and to provide a benchmark for future studies investigating differences in balance regulation and execution. Twelve young and healthy people agreed to quietly sit and stand on a force platform with their eyes open and closed. For each condition, one trial of 2 min was executed and the anterior-posterior, medial-lateral, and resultant distance fluctuations of the body's center of pressure (COP) were calculated. Finally, time-domain, frequency-domain, and stabilogram diffusion function (SDF) measures were identified and compared for all COP time series. The results consistently indicate that, for quiet sitting, the body sway size and velocity were smaller and the power-weighted average frequency larger than for quiet standing. Moreover, the SDF analysis revealed that quiet sitting shows fewer drifts over short time intervals, but also fewer controlled adjustments in the longer term to bring the system back to equilibrium. The observed differences can be partially explained by biomechanical and dynamic differences of the body portions that are in motion during quiet sitting and standing. The SDF analysis suggests, however, that also the balance control strategies are not identical. These findings may be especially useful for the assessment of sitting balance and the development of novel balance rehabilitation techniques and assistive devices.
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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 | 0.001 | 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) | 0.001 | 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".