A complete, non-lumped, and verifiable set of upper body segment parameters for three-dimensional dynamic modeling
Bibliographic record
Abstract
Dynamic models of the human trunk have been extensively used to investigate the biomechanics of lower back pain and postural instability in different populations. Despite their diverse applications, previous models rely on intrinsic upper body segment parameters (UBSP), e.g., each segment's mass-inertia characteristics. However, a comprehensive UBSP set allowing state-of-the-art, three-dimensional (3D) dynamic modeling does not exist to date. Therefore, our objective was to establish a UBSP set of all vertebral trunk segments that is accurate and complete. Based on high-resolution, transverse color images, anatomical structures of the Male Visible Human (MVH) were digitally reconstructed via commercial software. Subsequently, we identified the 3D spinal joint and 3D center of mass coordinates, the mass, and the moment of inertia tensor for 24 vertebral trunk segments and 4 upper limb segments (two segments per arm). Since the MVH images are public domain, the parameters are uniquely verifiable and expandable to also include lower limb parameters. To demonstrate the UBSP set's practicality, the parameters were finally implemented in a previously proposed inverse dynamics model of the upper body. Our findings reveal that an accurate and complete UBSP set has been obtained that will be beneficial to (1) systemize thinking in postural control studies; (2) quantify the effect of impact forces on the head and trunk (e.g., during whiplash); (3) suggest population-specific experiments based on theoretical insights into trunk dynamics (e.g., regarding lower back pain); or (4) assess the feasibility of new surgical techniques (e.g., spinal fusion) and neuroprostheses (e.g., after spinal cord injury).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".