Can Anthropometry be Used to Dictate Participant-Specific Thigh Marker Placements Which Minimize Error in Hip Joint Center Estimation?
Bibliographic record
Abstract
Specific participant characteristics may be leveraged to dictate marker placements which reduce soft tissue artifact; however, a better understanding of the relationships between participant characteristics and soft tissue artifact are first required. The purpose of this study was to assess the accuracy in which measures of whole-body and thigh anthropometry could predict mislocation error of the hip joint center, tracked using skin-mounted marker clusters. Fifty participants completed squatting and kneeling, while pelvis and lower limb motion were recorded. The effect of soft tissue artifact was estimated from 6 rigid thigh marker clusters by evaluating their ability to track the position of the hip joint center most like the pelvis cluster. Eighteen backward stepwise linear regressions were performed using 10 anthropometric measures as independent variables and the mean of the peak difference between the thigh and pelvis cluster-tracked hip joint centers. Fourteen models significantly predicted error with low to moderate fit (R = .38-.67), explaining 14% to 45% of variation. Partial correlations indicated that soft tissue artifact may increase with soft tissue volume and be altered by local soft tissue composition. However, it is not recommended that marker placement be adjusted based on anthropometry alone.
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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 | 0.009 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".