Between-Day Reliability of Kinematic Variables Using Markerless Motion Capture for Single-Leg Squat and Single-Leg Landing Tasks
Bibliographic record
Abstract
Background: Markerless motion capture has the potential to repeatedly collect biomechanical data during activities associated with injuries. Few studies have assessed the reliability of this technology during single-leg tasks. Purpose: The primary aim was to examine the between-day reliability of trunk and lower limb kinematics during single-leg squat and single-leg landing tasks using markerless motion capture. The secondary aim was to examine the between-day reliability of the same protocol using marker-based motion capture. Design: Reliability. Methods: Nineteen recreational athletes performed all tasks in two sessions, one week apart. Joint angles of trunk, hip, knee, and ankle were processed using Theia3D. A separate study (10 different participants) evaluated the reliability of marker-based motion capture. In both technologies, full curve analysis was examined using root mean square difference (RMSD) and discrete point analysis (initial contact and peak knee flexion) using intraclass correlation coefficient (ICC) and standard error of measurement (SEM). Statistical parametric mapping (SPM) was also used for full curve analysis in markerless motion capture. Results: For full curve analysis, markerless motion capture demonstrated low mean RMSD for all joints and planes in both SLS (3.6˚±1.3˚) and landing tasks (forward=3.2˚±1.3˚; medial=3.4˚±1.7˚). SPM showed statistical difference for bilateral hip flexion (full curve) and unilateral hip adduction, rotation, and knee flexion during a percentage of landing tasks. For discrete point analysis, ICC mean indicated moderate to good reliability (SLS= 0.77; forward landing= 0.83; medial landing= 0.80) with low mean SEM values (SLS=3.1°±1.3˚; forward landing=2.3˚±0.9°; medial landing=2.3˚±0.8˚). Marker-based motion capture showed slightly higher mean RMSD (SLS=4.2˚±1.8˚; forward landing=3.5˚±1.0˚; medial landing=3.3˚±0.9) and SEM values (SLS=4.1˚±2.2˚; forward landing=2.7˚±1.2°; medial landing=2.8˚±1.2˚). ICC mean showed good relative reliability (SLS=0.90; forward landing=0.88; medial landing=0.88). Hip flexion presented values >5° across tasks and technologies (RMSD and SEM= 5° to 8°). Conclusions: Markerless motion capture using Theia3D can reliably measure single-leg tasks with measurement errors comparable to marker-based motion capture. The low measurement error provides confidence for the regular monitoring of athletes during single-leg tasks. Level of evidence: 3.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".