A novel stability and kinematics-driven trunk biomechanical model to estimate muscle and spinal forces
Bibliographic record
Abstract
An anatomically detailed eighteen-rotational-degrees-of-freedom model of the human spine using optimization constrained to equilibrium and stability requirements is developed and used to simulate several symmetric tasks in upright and flexed standing postures. Predictions of this stability and kinematics-driven (S+KD) model for trunk muscle forces and spine compressive/shear loads are compared to those of our existing kinematics-driven (KD) model where both translational and rotational degrees-of-freedom are included but redundancy is resolved using equilibrium conditions alone. Unlike the KD model, the S+KD model predicted abdominal co-contractions that, in agreement with electromyography data, increased as lifting height increased at a constant horizontal moment arm. The S+KD model, however, could not fully explain the CNS strategy in activating antagonistic muscles for most of the remaining tasks. Despite quite distinct activities in individual muscles, both models predicted L4-L5 intradiscal pressure that matched the in vivo data, the L4-S1 compression loads, and the sum of all trunk muscle forces. For modeling applications in ergonomics, where the compressive spine loads are of interest, the two models yielded <15% difference. In the field of rehabilitation, where detailed muscle forces are required, the S+KD model explained more properly the CNS strategy in activating the antagonistic muscles for some tasks.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".