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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".