Photogrammetry: An accurate and cost-effective three-dimensional ice hockey helmet fit acquisition method
Bibliographic record
Abstract
Ice hockey helmets must pass standardized impact tests to be certified for sale. However, these tests are performed with the helmet attached to a surrogate headform. Human head shapes are not uniform, and very few standards exist for helmet fitting for the common user. The goal of this study was to develop an accurate and cost-effective three-dimensional acquisition protocol to assess the geometric fit of human subject heads to a variety of ice hockey helmets. The study had three main objectives: First, a photogrammetry-based three-dimensional acquisition system was developed. Second, the researchers populated a database of both male human heads and ice hockey helmets by scanning five different helmet models from various manufacturers. Finally, the system accuracy and error were calculated using root mean squared errors between the dimensional difference curves of repeated scans. Errors were calculated by repeating the entire protocol error with 20 comparisons (root mean squared error = 2.83 mm), the alignment error with 5 comparisons (root mean squared error = 1.14 mm), and scaling error with 4 comparisons (root mean squared error = 1.84 mm). Suggestions are provided in the section “Discussion” on how to create a system that is more time efficient with higher resolution renders and lower error. A method that quantifies three-dimensional fit is the first step toward studying the relationship between helmet fit and user-specific helmet protection.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".