Biomechanical Assessment of Cycling Helmets: the Influence of Headform and Impact Velocity based on Cycling Collisions associated with Injury Claims
Bibliographic record
Abstract
The goal of my thesis was to fill some of the gaps in knowledge about cyclist/motor vehicle collisions and testing guidelines for cycling helmets. Cycling collisions with motor vehicles represent a problem for the Canadian health care system as they can cause severe injuries, especially to the head. Our current knowledge of the factors involved in cycling collisions in Southern Ontario is limited due to current injury reporting techniques. Furthermore, the effectiveness of cycling helmets for mitigating injury during high energy impacts is unknown as testing guidelines are designed for lower energy impacts, such as a sideways fall from a bicycle. Accordingly, my thesis was designed with two studies to address these limitations. The first study was novel as it was the first to characterize cycling collisions in Southern Ontario that resulted in injury claims and determine if relationships existed between injury circumstances (e.g. helmet use, impact surface) and injury outcomes. Data was collected from a unique database at a professional forensic engineering company. Using a subset of this data, a head impact velocity was determined to represent higher energy impacts of cyclist/motor vehicle collisions. The second study compared peak dynamic headform responses between three headforms (two biofidelic and the magnesium headform currently used in testing standards) and also assessed the mitigating capacity of three brands of cycling helmets when subjected to impact velocities of standard testing scenarios as well as higher energy impact velocities (determined in study one). It determined that the Hybrid III headform may be an appropriate tool for helmet testing. Furthermore, the helmets tested mitigated injury below injury thresholds at impact velocities used in current testing standards but not at an impact velocity representative of a higher energy scenario such as a cyclist/motor vehicle collision, as determined in study one. Injury risk reduction was affected by helmet brand with more expensive helmets not necessarily producing better results. These findings indicate the need for more work in the area of improving and understanding the biofidelity of our testing regimes. Finally, helmet manufacturers should be urged to be more transparent to consumers about the relative mitigating capacity of their helmet brands perhaps by creating a rating system for helmet safety.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".