Clinical integration of markerless motion capture: A multicentre study of gait in knee osteoarthritis
Bibliographic record
Abstract
Markerless motion capture addresses key barriers limiting the clinical uptake of biomechanical assessments by enabling efficient data collection and standardized modeling, making it well-suited for multicentre research. This study assessed whether gait deviations associated with knee osteoarthritis (OA) could be consistently detected using markerless motion capture across three clinical centres in Canada. Gait data from 486 participants (351 with knee OA; 135 controls) were analyzed, with body segment kinematics estimated from video using Theia3D. Principal component analysis and linear models were used to evaluate joint kinematics and temporal-distance parameters across groups and sites. After pooling data across centres, individuals with knee OA exhibited characteristic gait deviations, including slower walking speed, reduced hip, knee, and ankle range of motion, and increased knee adduction, compared to controls. These deviations were observed consistently across all three centres. Inter-site differences in joint kinematics were minor (RMS < 3°), remained within reported inter-site error thresholds from marker-based systems, and did not obscure group-level effects. These findings demonstrate that clinically meaningful gait deviations can be reliably detected using markerless motion capture in varied clinical environments without extensive standardization. This work supports its use in multicentre studies and highlights its potential to enable large-scale biomechanical research, an essential step toward broader clinical integration of movement 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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".