Impact performance of ice hockey helmets: head acceleration versus focal force dispersion
Bibliographic record
Abstract
Modern sport helmets certified to various international safety standards have virtually eliminated the incidence of cranial fracture and fatal brain injury in contact sports; however, the occurrence of diffuse brain injuries (mTBI) are still prevalent. Local contact mechanics between the colliding surface (helmet/head) need to be considered as global measures of acceleration and are insensitive to load distribution measures which are indicative of helmet performance. The purpose of this study was to demonstrate the ability to capture localized load distribution response between the helmet and headform and to examine factors that may influence these measures. Twenty-five flexible force sensors were arranged in a 5 × 5 array about three impact sites (front, side, rear) of a 575 mm EN960 headform. Test factors included helmet model (5), impact location (3) and temperature (21 °C, −25 °C) as well as repeated impacts (3). Testing procedure followed the CSA Z262.1-09 standard at the defined locations. Average error calculated during sensor calibration was 2.8 ± 1% with an R2 value of 0.987 ± 0.009. As expected, peak global force correlated well to peak acceleration ( R2 = 0.98) but weakly corresponded to peak focal force ( R2 = 0.22). Furthermore, both load distribution magnitudes and patterns were found to vary substantially with mixed effects between helmet models, impact locations, and temperatures. Given these findings, this novel approach may be used to quantify local contact mechanics between the colliding surface/helmet/head.
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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 | 0.000 |
| 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) | 0.001 | 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".