Integration of Muscle Pre-tension and Activation to Evaluate Neck Muscle Strain Injury Risk during Simulated Rear Impacts Using a Finite Element Neck Model
Bibliographic record
Abstract
Prevention of rear-impact neck injuries remains challenging for safety designers due to a lack of understanding of the tissue-level response and injury risk. Soft tissue injuries have been inferred from clinical, cadaveric, and numerical studies; however, there is a paucity of data for neck muscle injury, commonly reported as muscle pain. The goal of this study was to investigate the effect of muscle pre-tension and activation on muscle strain and injury risk resulting from low-severity rear impacts using a detailed finite element head and neck model (HNM). The HNM was extracted from the GHBMC average stature male model and re-postured to match a volunteer study, with measured T1 kinematics applied as boundary conditions to the HNM. Three cases were simulated for three impact severities: the baseline repostured HNM, the HNM including muscle pre-tension, and the HNM with muscle pre-tension and muscle activation. The head kinematics, vertebral kinematics, muscle strains, and three neck injury criteria were calculated to assess injury risk. The kinematic response of the neck model demonstrated an S-shaped pattern, followed by extension in the rear impact cases. The maximum kinetics, kinematics, and muscle strains occurred later in the impact during the extension phase. The distribution and magnitude of muscle strain depended on muscle pre-tension and activation, and the largest predicted strains occurred at locations associated with muscle injury reported in the literature. The HNM with muscle pre-tension and muscle activation provides a tool to assess rear impact response and could inform injury mitigation strategies in the future.
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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 | 0.001 | 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".