Comparison Of Kinovea And Force Platforms For Postural Stability Assessment In Older Adults
Bibliographic record
Abstract
Postural stability declines with advancing age due to degeneration of the musculoskeletal and sensory systems. Force platforms are currently the gold standard for evaluating postural stability, but are costly and must be carried out in person. Kinovea, a free for use sports analysis software, has been suggested as a telehealth solution. PURPOSE: To investigate the validation of Kinovea for postural stability assessment in older adults. METHODS: 10 older women (73.9 ± 5.7 years old) completed 30 seconds of quiet standing on force platforms with eyes opened (EO), eyes closed (EC) and on a foam pad (FP) with a camera placed sagittally. Movements at the hip and shoulder were analyzed frame-by-frame by two blinded raters using Kinovea. Correlations between Kinovea and force platforms were evaluated using Pearson’s Correlation Coefficient (r) and interrater reliability was evaluated using Intraclass Correlation Coefficients (ICC). RESULTS: Tracking of the shoulders had a strong, positive correlation during EC (r = .0756, p < .001) and FP (r = 0.776, p < .001). Shoulders during EO showed moderate correlation (r = 0.436, p = 0.016). Excellent interrater reliability (ICC from 0.957-0.982, p < .001) was found at shoulders in all conditions. Further, tracking at hips had a moderate correlation during EC (r = 0.641, p < .001) and on FP (r = 0.644, p < .001) while the relationship in EO was weak (r = 0.374, p = .042). There was a moderate interrater reliability in EO (0.611) and FP (0.565), while a good interrater reliability with EC (0.757). CONCLUSION: As the conditions become more difficult, the movements become bigger, and consequently easier to track. Validity between force platforms and Kinovea is strongest with shoulder displacements with excellent interrater reliability. Tracking of the shoulder using Kinovea appears a valid method to assess postural stability remotely.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".