Predicting the kinematic response of a helmeted headform during oblique impacts
Bibliographic record
Abstract
A total of 160 oblique impact tests were performed to study the relationship between the kinematic response of a helmeted headform and the impact severity caused by a change in speed (Group 1) and anvil angle (Group 2). In this study, the kinematic response of a helmeted headform was evaluated by measuring its linear acceleration, rotational acceleration, and rotational velocity. In Group 1, a football helmet was tested at 45° anvil angle at four different impact speeds ranging from 4.5 to 7.4 m/s on five impact locations. The results showed that, for all cases, the relationship between the impact speed and helmeted headform kinematic response was linear, with an average R2 value of 0.98. At each impact location, a prediction line was generated using the data points for the lowest and highest speeds. For the speeds of 5.5 and 6.5 m/s, the prediction of the helmeted headform kinematic response was validated with an average error of 4.7%. In Group 2, the helmeted headform was tested at 5.5 m/s impact speed at six different anvil angles between 15° and 55°, and the response was fitted with a second-degree polynomial (curve) with an average R2 value of 0.96. The kinematic response of the higher and lower impact speeds was obtained experimentally for one angle, and the fitted curve for 5.5 m/s was offset to pass through the obtained kinematic response. The predicted helmeted headform kinematic response was experimentally validated, and the average error was found to be 8.3%. The results showed that it is possible to predict the kinematic response of a helmeted headform by interpolating or extrapolating the data without having to perform an additional impact test. An analysis of other research works also showed similar predictable behaviour for headform equipped with other helmet models. Therefore, the number of tests during the process of evaluating helmet performance can be reduced.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".