A comparison of five methods to normalize joint moments during running
Bibliographic record
Abstract
BACKGROUND: Net joint moments (NJM) are typically normalized for a (combination of) physical body characteristics such as mass, height, and limb length using ratio scaling to account for differences in body characteristics between individuals. Four assumptions must be met when normalizing NJM data this way to ensure valid conclusions. First, the relationship between the non-normalized NJM and participant characteristic should be linear. Second, the regression line between NJM and the characteristic(s) used should pass through the origin. Third, scaling should not significantly perturb the statistical distribution of the data. Fourth, normalizing a NJM should eliminate its correlation with the characteristic(s) normalized for. RESEARCH QUESTION: . METHODS: Standard inverse dynamics analyses were conducted, and ratios were computed between the sagittal-plane hip, knee and ankle NJM's and the participant's mass, height, leg length, mass × height, and mass × leg length. RESULTS: The most important finding of this study was that none of the scaling variables fulfilled all assumptions across all joints. However, scaling by mass, mass*height and mass*leg length satisfied the assumptions for the knee joint moment and log-transformed hip joint moment, suggesting these methods generally performed best. SIGNIFICANCE: Our findings suggests that scaling by mass, mass*height and mass*leg length may be considered to normalize joint moments during running. Nevertheless, we urge researchers to check the statistical assumptions to ensure valid conclusions. We provide supplementary code to check the statistical assumptions, and discuss consequences of inappropriate scaling.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".