What Not to Wear: Examining the Usability of Markerless Motion Capture for Pediatric Populations
Bibliographic record
Abstract
Markerless motion capture may improve the accessibility and participants experience of three-dimensional gait analysis in children. This study explores aspects of usability of markerless motion capture that have not been previously studied specifically, the consequences of street clothing on lower-limb kinematics, participant and caregiver perceptions, and assessment durations. Thirty typically developing children completed two 3D gait analysis protocols. They wore either a) "Conventional" clothing required for marker-based methods and had markers placed, or b) their "Street" clothes without markers. Markerless gait kinematics were measured using Theia3D. Root-mean-square-deviations (RMSD) and an outlier analysis were used to determine differences between experimental conditions. Differences in participant perceptions were assessed using custom surveys, and differences in testing duration were assessed using the Wilcoxon Signed Rank test. Median RMSDs were < 4° and maximal RMSDs were 3.6°-16.0° and with no consistent pattern across joints and planes of motion, suggesting minimal differences between clothing conditions. Individuals with larger deviations generally wore baggy or loose clothing. When asked which condition they would prefer to repeat, 12% (n = 3/25) indicated the Conventional condition, 68% (n = 17/25) mentioned the Street condition and 16% (n = 4/25) would repeat both. Further, caregivers more frequently reported atypical gait in the Conventional condition. The Street condition took less time with a median (25th-75th percentile) difference of 11 (9-13) minutes (p < 0.001). Street clothing with minimal restrictions can be used without compromising lower-limb kinematics, while improving participant experience and reducing assessment duration. These findings may contribute to improved access to 3D gait analysis.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".