Evaluation of the Skeleton Avatar Technique for Assessment of Mobility and Balance Among Older Adults
Bibliographic record
Abstract
Background: Mobility and balance is essential for older adults' well-being and independence and the ability to maintain physically active. Early identification of functional impairment may enable early risk-of-fall assessments and preventive measures. There is a need to find new solutions to assess functional ability in easy, efficient, and accurate ways, which can be clinically used frequently and repetitively. Therefore, we need to understand how functional tests and expert assessments (EAs) correlate with new techniques. Objective: To explore whether the skeleton avatar technique (SAT) can predict the results of functional tests (FTs) of mobility and balance: Timed Up and Go (TUG), the 30-s chair stand test (30sCST), the 4-stage balance test (4SBT), and EA scoring of movement quality. Methods: Fifty-four older adults (+65 years) were recruited through pensioners' associations. The test procedure contained three standardized FTs: TUG, 30sCST, and 4SBT. The test performances were recorded using a three-dimensional SAT camera. EA scoring was performed based on the video recordings of the 30sCST. Functional ability scores were aggregated from balance and mobility scores. Probability theory-based statistical analyses were used on the data to aggregate sets of individual variables into scores, with correlation analysis used to assess the dependency between variables and between scores. Machine learning techniques were used to assess the appropriateness of easily observable variables/scores as predictors of the other variables included. Results: The results indicate that SAT data of the fourth 4SBT stage could be used to predict the aggregated results of all stages of 4SBT (with 7.82% mean absolute error), the results of the 30sCST (11.0%), the TUG test (8.03%), and the EA of the sit-to-stand movement (8.79%). There is a moderate (significant) correlation between the 30sCST and the 4SBT (0.31, p = 0.03), but not between the EA and the 30sCST. Conclusion: SAT can predict the results of the 4SBT, the 30sCST (moderate accuracy), and the TUG test and might add important qualitative information to the assessment of movement performance in active older adults. SAT might in the future provide the means for a simple, easy, and accessible assessment of functional ability among older adults.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".