Phenomenological Muscle Constitutive Model With Actin–Titin Binding for Simulating Active Stretching
Bibliographic record
Abstract
The force produced by a muscle depends on its contractile history, yet human movement simulations typically employ muscle models that define the force-length relationship from measurements of fiber force during isometric contractions. In these muscle models, the total force-length curve can have a negative slope at fiber lengths greater than the fiber length at which peak isometric force is produced. This region of negative stiffness can cause numerical instability in simulations. Experiments have found that the steady-state force in a muscle fiber following active stretching is greater than the force produced during a purely isometric contraction. This behavior is called residual force enhancement. We present a constitutive model that exhibits force enhancement, implemented as a hyperelastic material in the febio finite element software. There is no consensus on the mechanisms responsible for force enhancement; we adopt the assumption that the passive fiber force depends on the sarcomere length at the instant that the muscle is activated above a threshold. We demonstrate the numerical stability of our model using an eigenvalue analysis and by simulating a muscle whose fibers are of different lengths. We then use a three-dimensional muscle geometry to verify the effect of force enhancement on the development of stress and the distribution of fiber lengths. Our proposed muscle material model is one of the few models available that exhibits force enhancement and is suitable for simulations of active lengthening. We provide our implementation in febio so that others can reproduce and extend our results.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 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".