Modeling and simulating the deformation of human skeletal muscle based on anatomy and physiology
Bibliographic record
Abstract
Abstract This paper describes the modeling and simulation of the deformation of human skeletal muscle at different structural levels based on sound scientific principles, experimental evidence, and state‐of‐art muscle anatomy and physiology. The equations of a continuum model of a muscle with realistic architecture, including internal arrangement of muscle fibers and passive structures, and deformation, including activation relations, was developed and solved with the finite element method. The continuum model is used as the basis of a strategy for controlling muscle deformation using activation relations. In order to demonstrate the functionality of the model, it was used to investigate force production and structural changes during contraction of the human tibialis anterior for maximally and submaximally activated muscle behavior. From a comparison with experimental data obtained from ultrasound imaging, we concluded that the modeling and simulation of the continuum based on physiologically meaningful parameters as described in the paper is both an excellent predictor of force production observations and of changes in internal geometry under various test conditions. It is therefore a valuable tool for controlling muscle deformation during movement. Copyright © 2005 John Wiley & Sons, Ltd.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".