Assessing bicycle helmet protection for head and neck in E-scooter falls
Bibliographic record
Abstract
Objectives The rapid increase in E-scooter usage has led to more scooter-related head and neck injuries. Yet, experimental data on head impacts and helmet effectiveness during crashes are scarce. The objectives of this study are to experimentally evaluate bicycle helmets in E-scooter falls, assessing head kinematics, impact conditions, and injury risks in two crash scenarios with and without helmets.Methods Six E-scooter forward falls, induced by a curb collision at 20 km/h, were simulated in sled tests using a Hybrid III 50th anthropomorphic test device with and without a helmet. The curb was positioned either perpendicularly or at a 55° angle to the E-scooter’s trajectory. Head velocity, head acceleration, neck load, chest acceleration, and chest deflection were measured.Results The average normal and tangential head velocities at impact were 5.9 m/s and 3.7 m/s, respectively. In configurations without helmet, both head accelerations and neck loads exceeded some injury thresholds, indicating a risk of severe injury. Using a helmet significantly reduced peak head linear (143 g vs. 571 g) and rotational (9.8 krad/s2 vs. 23.1 krad/s2) accelerations, and Head Injury Criterion (HIC) (792 vs. 5868). However, it did not significantly affect peak head rotational velocity (44.5 rad/s vs. 41.5 rad/s), neck load (in flexion-compression) nor Neck Injury Criterion (NIJ) (1.2 vs. 1.0).Conclusion The bicycle helmet significantly reduced most head injury metrics. Yet, the risk of severe head and neck injuries remains high. These results offer valuable data for evaluating head protection and developing and validating numerical crash test reconstructions for further investigations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".