Three-dimensional video analysis of helmet-to-ground impacts in North American youth football
Bibliographic record
Abstract
This study presents a video analysis of helmet-to-ground impacts in youth football (≤14 years). A total of 21 non-injurious helmet-to-ground impact cases were assessed from game video of two age divisions (9-12 years: n = 9; 13-14 years: n = 12) using a novel multi-camera videogrammetry approach. Descriptive parameters related to the game situation and impact mechanisms were documented. Motion analysis software was used to manually track and compute three-dimensional helmet kinematics and uncertainty of the motion tracking analysis was assessed. Overall, the impact cases primarily followed a body-to-body, body-to-ground, helmet-to-ground contact progression. Impact locations on the helmet were mostly distributed across the rear and side helmet shell. The resultant pre-impact velocities for these cases averaged 4.04 ± 1.24 m/s at an angle of -49.6° to the ground. The average resultant impact-induced change in helmet velocity was 3.32 ± 1.14 m/s; the time interval associated with the duration of helmet-to-ground contact was approximately 0.06 s. The average maximum uncertainty (±) error of the position coordinates from the helmet tracking was 1.5 ± 0.3 cm. In summary, this video-based methodology can effectively be used to quantify helmet impact velocities and locations in youth football games. To date, the acquisition of such information has largely been limited to professional football game footage. Therefore, the data reported here may help inform the development of more representative assessment methods for youth-specific helmet test standards.
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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 | 0.003 | 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".