Could a Compliant Foam Anvil Characterize the Biofidelic Impact Response of Equestrian Helmets?
Bibliographic record
Abstract
The performance of equestrian helmets to protect against brain injuries caused by fall impacts against compliant surfaces such as turf has not been studied widely. We characterize the kinematic response of simulated fall impacts to turf through field tests on horse racetracks and laboratory experiments. The kinematic response characteristics and ground stiffness at different going ratings (GRs) (standard measurement of racetrack condition) were obtained from 1 m and 2 m drop tests of an instrumented hemispherical impactor onto a turf racetrack. The "Hard" rating resulted in higher peak linear accelerations and stiffness, and shorter impact durations than the "Soft" and "Heavy" ratings. Insignificant differences were found among the other GRs, but a strong overall relationship was evident between the "going rating" and the kinematic response. This relationship was used to propose a series of three synthetic foam anvils as turf surrogates in equestrian falls corresponding to ranges of GRs of (i) heavy-soft (H-S), (ii) good-firm (G-F), and (iii) firm-hard (F-H). Laboratory experiments consisted of a helmeted headform being dropped onto natural turf and the turf surrogate anvils using a monorail drop rig. These experiments revealed that the magnitudes and durations of the linear and rotational accelerations for helmeted impacts to turf/turf surrogates were similar to those in concussive sports falls and collisions. Since the compliance of an impacted surface influences the dynamic response of a jockey's head during a fall impact against the ground, it is important that this is considered during both accident reconstructions and helmet certification tests.
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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 | 0.000 | 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".