Cluster Analysis of Center-of-Pressure Measures
Bibliographic record
Abstract
Postural stability analysis is useful in a variety of applications, such as assessing the risk of falls for older adults or investigating neuromuscular disorders.Variations in the center-of-pressure (COP) are often used to assess postural stability.The COP is a point where the vertical reaction forces of the ground act.It represents the weighted average of all pressures over the body in contact with the ground.Static posturography, which analyses COP variations during a fixed standing posture, is a non-invasive assessment technique, used to quantify postural stability.The objective of this study is to investigate the performance of six conventional COP measures used to characterize postural stability.Six conventional measures are investigated: i) average radial displacement, ii) average velocity, iii) area of the 95% confidence ellipse, iv) standard deviation of COP in the x-direction (medio-lateral), v) standard deviation of COP in the y-direction (antero-posterior), and vi) standard deviation of radial displacement.These COP measures are compared during four different stability conditions: i) feet together, eyes open, ii) feet together, eyes closed, iii) single leg, eyes open, and iv) single leg, eyes closed.Performance is quantified by cluster analysis using the silhouette coefficient, which provides a measure of how well clustered the four stability conditions are for a given stability measure.A good stability measure should have high repeatability for a given stability condition (low intra-cluster distances) and be able to discern between different stability conditions (high inter-cluster distances).Results from eight subjects suggest that out of the six COP measures examined, average velocity is the best measure to assess postural stability.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".