Muscle Activation Affects Kinematic Response and Injury Risk in Non-Traditional Oblique Impact Scenarios Assessed with a Head and Neck Finite Element Model
Bibliographic record
Abstract
<div>Detailed finite element human body models (HBMs), and neck models (NMs) in particular, have been used to assess response and injury risk with a focus on frontal, lateral, and rear impact conditions. Although HBMs have successfully predicted kinematics and the importance of active muscle in simple loading conditions, they have generally not been assessed for more complex loading conditions such as non-traditional oblique loading that may be encountered in future vehicles equipped with automated driving systems.</div> <div>In this study, a contemporary NM was assessed using oblique human volunteer sled test data. Average head and first thoracic vertebra kinematics were determined from the volunteer tests and applied as a boundary condition to the NM. An open-loop co-contraction muscle activation scheme with four activations times within reported human limits (50, 75, 100, no activation) was used to investigate the effect on response and potential for injury risk.</div> <div>The T1 and head kinematics from 45 oblique impact volunteer tests were analyzed in five groups according to the peak sled acceleration (4g to 11g), resulting in mean and standard deviation corridors. The NM ran stably to completion for all impact cases, demonstrating complex forward excursion of the head, and lateral bending and axial rotation of the neck under oblique loading. Objective evaluation of the predicted head kinematics over a range of impact severities demonstrated fair to good biofidelity (0.65 to 0.77 rating) for the 75 ms activation time and no tissue damage was identified, in agreement with the experimental tests. The model correlation was higher for the 50 ms activation time, suggesting that the volunteer muscle activation times were lower than the average for the population. The average 75 ms co-contraction activation has previously provided good results in frontal and lateral impacts and, in the current study, demonstrated applicability to more complex oblique impact scenarios that may be encountered in ADS-equipped vehicles.</div>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".