Markerless Vs. Marker-based Kinematics During 45-degree Cutting In Pediatric Patients
Bibliographic record
Abstract
PURPOSE: Sports biomechanical analysis has traditionally been performed using marker-based motion analysis. Markerless motion capture systems are now available, but need to be validated for clinical and research use. This study compares kinematics from the Theia3D markerless system vs. the Vicon marker-based system for cutting in pediatric patients. METHODS: 10 pediatric athletes (6 female; age range 10-18 years) with lower extremity injuries (5 ACL, 1 MCL, 4 lower limb pain, 1 unspecified knee injury) underwent sports biomechanical testing with data being captured concurrently by a traditional marker-based motion capture system (Nexus2, Vicon, Oxford, UK) and a markerless system (Theia3D, Ontario, Canada and Qualysis, Goteborg, Sweden). Participants performed a 45° cut by running straight forward, planting their left foot on a force plate, and cutting to the right along a guideline on the floor. Kinematic data from one successful trial per patient was compared between the markerless and marker-based systems using root mean square difference (RMSD), mean difference, and RMSD after subtracting the mean difference (RMSDoffset). RESULTS: Kinematics showed similar patterns between the markerless and marker-based systems. While average RMSD of the raw data was >5° for most joint angles, all RMSDoffset were < 7° except for trunk and hip rotation (9.7° and 7.5°, respectively) (Table). Systematic differences between the two systems >10° were observed in trunk tilt and ankle inversion/eversion with differences >5° in ankle rotation, knee rotation, and knee flexion/extension. CONCLUSIONS: The markerless system produced kinematic patterns similar to marker-based motion analysis during cutting, but the differences were greater than observed during gait1 and not always systematic. Additional validation of accuracy and reliability is recommended before markerless motion capture is used for clinical biomechanical sports assessment. 1 Wren 2023. Gait Pos 104, 9-14
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".