Repeatability of Knee Kinematic Gait Outcomes Using a Novel Hospital Hallway Setup of a Markerless Motion Capture System
Bibliographic record
Abstract
Instrumented gait analysis has traditionally been isolated to laboratory, marker-based optoelectronic motion capture systems, which limits clinical uptake. Markerless motion capture (MMC) systems driven by trained machine learning algorithms offer high-throughput solutions for translational clinical opportunities. The aim of this study was to examine the day-to-day repeatability of discrete knee kinematic gait metrics in a healthy population using an MMC system uniquely installed in a hospital hallway. Twenty healthy adults (13 females, 7 males) participated in 3 overground hallway gait sessions, on average 11 days apart, using a novel MMC system setup. Intraclass correlation coefficients, standard errors of measurement, and minimal detectable changes were examined for each gait outcome. Results indicated good-to-excellent repeatability, with most (7/8) outcomes having intraclass correlation coefficient values over .86. Standard error of measurement values for all kinematic outcomes were less than 2.0°, and minimal detectable change values were less than 4.7°. Our novel setup of a hospital hallway MMC system produced highly repeatable gait kinematic metrics in a population of healthy adults. Repeatability errors from this study can be used as a healthy reference for future applications of this system.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".