A Test Bed to Examine Helmet Fit and Retention and Biomechanical Measures of Head and Neck Injury in Simulated Impact
Bibliographic record
Abstract
Conventional wisdom and the language in international helmet testing and certification standards suggest that appropriate helmet fit and retention during an impact are important factors in protecting the helmet wearer from impact-induced injury. This manuscript aims to investigate impact-induced injury mechanisms in different helmet fit scenarios through analysis of simulated helmeted impacts with an anthropometric test device (ATD), an array of headform acceleration transducers and neck force/moment transducers, a dual high speed camera system, and helmet-fit force sensors developed in our research group based on Bragg gratings in optical fiber. To simulate impacts, an instrumented headform and flexible neck fall along a linear guide rail onto an anvil. The test bed allows simulation of head impact at speeds up to 8.3 m/s, onto impact surfaces that are both flat and angled. The headform is fit with a crash helmet and several fit scenarios can be simulated by making context specific adjustments to the helmet position index and/or helmet size. To quantify helmet retention, the movement of the helmet on the head is quantified using post-hoc image analysis. To quantify head and neck injury potential, biomechanical measures based on headform acceleration and neck force/moment are measured. These biomechanical measures, through comparison with established human tolerance curves, can estimate the risk of severe life threatening and/or mild diffuse brain injury and osteoligamentous neck injury. To our knowledge, the presented test-bed is the first developed specifically to assess biomechanical effects on head and neck injury relative to helmet fit and retention.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".