Importance of passive muscle, skin, and adipose tissue mechanical properties on head and neck response in rear impacts assessed with a finite element model
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to improve head-neck kinematic predictions of a contemporary finite element (FE) head-neck model, assessed in rear impact scenarios (3-10 g), by including an accurate representation of the skin, adipose tissue, and passive muscle mechanical properties. The soft tissues of the neck have a substantial contribution to kinematic response, with the contribution being inversely proportional to the impact severity. Thus accurate representation of these passive tissues is critical for the assessment of kinematic response and the potential for crash induced injuries. Contemporary Human Body Models (HBMs) often incorporate overly stiff mechanical properties of passive tissues for numerical stability, which can affect the predicted kinematic response of the head and neck. METHODS: Soft tissue material properties including non-linearity, compression-tension asymmetry, and viscoelasticity were implemented in constitutive models for the skin, adipose, and passive muscle tissues, based on experimental data in the literature. A quasi-linear viscoelastic formulation was proposed for the skin, while a phenomenological hyper-viscoelastic model was used for the passive muscle and adipose tissues. A head-neck model extracted from a contemporary FE HBM was updated to include the new tissue models and assessed using head rotation angle for rear impact scenarios (3 g, 7 g, and 10 g peak accelerations), and compared to postmortem human surrogate (PMHS) data for 7 g impacts. RESULTS: The head rotation angle increased with the new material models for all three rear impact cases: (3 g: +43%, 7 g: +52%, 10 g: +71%), relative to the original model. The increase in head rotation was primarily attributed to the improved skin model, with the passive muscle being a secondary contributor to the increase in response. A 52% increase in head rotation for the 7 g impact improved the model response with respect to PMHS data, placing it closer to the experimental average, compared to the original model. CONCLUSIONS: The improved skin, adipose tissue, and passive muscle material model properties, based on published experimental data, increased the neck compliance in rear impact, with improved correspondence to published PMHS test data for medium severity impacts. Future studies will investigate the coupled effect of passive and active muscle tissue for low severity impacts.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".