Optimal Estimation of Anthropometric Parameters for Quantifying Multisegment Trunk Kinetics
Bibliographic record
Abstract
Kinetics assessment of the human head-arms-trunk (HAT) complex via a multisegment model is a useful tool for objective clinical evaluation of several pathological conditions. Inaccuracies in body segment parameters (BSPs) are a major source of uncertainty in the estimation of the joint moments associated with the multisegment HAT. Given the large intersubject variability, there is currently no comprehensive database for the estimation of BSPs for the HAT. We propose a nonlinear, multistep, optimization-based, noninvasive method for estimating individual-specific BSPs and calculating joint moments in a multisegment HAT model. Eleven nondisabled individuals participated in a trunk-bending experiment and their body motion was recorded using cameras and a force plate. A seven-segment model of the HAT was reconstructed for each participant. An initial guess of the BSPs was obtained by individual-specific scaling of the BSPs calculated from the male visible human (MVH) images. The intersegmental moments were calculated using both bottom-up and top-down inverse dynamics approaches. Our proposed method adjusted the scaled BSPs and center of pressure (COP) offsets to estimate optimal individual-specific BSPs that minimize the difference between the moments obtained by top-down and bottom-up inverse dynamics approaches. Our results indicate that the proposed method reduced the error in the net joint moment estimation (defined as the difference between the net joint moment calculated via bottom-up and top-down approaches) by 79.3% (median among participants). Our proposed method enables an optimized estimation of individual-specific BSPs and, consequently, a less erroneous assessment of the three-dimensional (3D) kinetics of a multisegment HAT model.
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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 | 0.001 | 0.001 |
| 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".