Correlating Skeletal Muscle Output Force and Intramuscular Pressure Via a Three-Dimensional Finite Element Muscle Model
Bibliographic record
Abstract
The inclusion of muscle pressure in muscle models may have important implications in biomechanics. This notion builds from the known correlation between muscle contractile force and internal pressure. However, this relation is often omitted in numerical models leveraged to study biomechanics. Thus, the purpose of this study was to develop and validate a method of modeling muscles, via finite elements, inclusive of the correlation between muscle contractile force and intramuscular pressure. A magnetic resonance imaging (MRI)-scanned tibialis anterior muscle was modeled via a simple, yet easily scalable, mixed shell and pressure finite element model. Then a validation study was conducted on intramuscular pressure, resulting from applied muscle contractile force, through leveraging special fluid elements type. The fluid-structure-based model and adopted methods exhibited muscle forces and intramuscular pressure that were highly linearly correlated. Indirect validation was achieved with a maximum discrepancy of 7.25%. Furthermore, force-length curves followed a trend similar to documented conventional muscle data, which added to the model's validity. Mesh, material properties, and tendon stiffness sensitivity studies supported the model's robustness. This study has introduced a novel three-dimensional finite element modeling method that respects the physiological force and intramuscular pressure relationship. Although similar models have been previously explored, their complex physiological representation and time-consuming solvers make their scalability and real-time implementation questionable. Thus, the developed model may address such limitations while improving the realism of volumetric finite element models inclusive of muscle contribution.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".