Influence of Sex and Body Size on the Validity of the Microsoft Kinect for Frontal Plane Knee Kinematics During Landings
Bibliographic record
Abstract
Three-dimensional (3D) motion capture is inaccessible, and the Microsoft Kinect is an alternative to measure surrogates of knee valgus that may contribute to anterior cruciate ligament (ACL) injury risk. We evaluated the influence of sex and body size on the agreement between methods. A total of 40 (10 per sex and BMI group) participants were included. The Kinect and motion capture measured knee ankle separation ratio (KASR) and knee abduction angles (KAAs). Intraclass correlation coefficients (ICCs) evaluated agreement between methods. 2 (sex) by 2 (BMI) by 2 (method) ANOVA compared kinematics between groups. Agreement between methods was moderate-to-good for KASR (initial contact ICCs 0.667–0.86; peak flexion ICCs 0.766–0.882). Agreement for KAA was low-to-moderate (initial contact ICCs 0.128–0.575; peak flexion ICCs 0.315–0.760). There was a BMI-by-method interaction for KASR at initial contact (p < 0.01) and a main effect of method (p < 0.01). There were BMI-by-method interactions for KAA (initial contact p > 0.01; peak knee flexion p < 0.01). The high BMI group had greater KAAs than the low BMI group, but only using motion capture. The Kinect is an alternative for measuring KASR, but not KAA. The high BMI group had greater KAAs than the low BMI group, but only when measured with motion capture.
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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 | 0.017 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".