Accuracy and Variability of a Commercial Markerless Motion Capture System Compared to a Pressure Mat for Weight Distribution in Standing: Cross-Sectional Observational Study
Bibliographic record
Abstract
BACKGROUND: Commercial markerless motion capture (MMC) systems show promise for use in rehabilitation and have been validated for the assessment of various parameters. However, no prior studies have evaluated MMC systems to detect stance asymmetry. OBJECTIVE: The objective of this study was to assess the accuracy and variability of the Jintronix Weight Shift Tool MMC system to estimate the percentage of weight borne on each foot. METHODS: Twelve healthy younger adults, 12 healthy older adults, and 12 people living with stroke were recruited for this cross-sectional study. The percentage of weight borne on each foot was simultaneously recorded by the Weight Shift Tool and a validated pressure mat during 2 series (raising the arm to capture the recording and without arm raise) with left lean, right lean, and equal stance. Agreement between the Weight Shift Tool and the pressure mat was assessed using Bland-Altman analyses. RESULTS: Bias was greatest for older adults for all stances except for right lean without arm raise. Variability was greatest for people living with stroke for all stances except for left lean with arm raise. On average, the limits of agreement were narrower during equal stance. Although bias between the Weight Shift Tool and the pressure mat was small to moderate (0.0%-11.7%), the limits of agreement were wide (12.8%-33.6% above and below the bias). CONCLUSIONS: The Weight Shift Tool is not clinically acceptable for the estimation of the percentage of weight on each foot due to high variability. Investigation of other MMC systems is required to confirm the validity of MMC for clinical assessment.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".