Designing and Constructing a Smart Armor for Protecting Motorcyclists’ Head and Neck in the Accident Time
Bibliographic record
Abstract
Background: In general, riding motorized two-wheeled vehicles carries a higher risk of being involved in a fatal accident than any other mode of transport. In some countries, the use of protective helmets while riding motorcycles is a legal requirement. That is, a helmet can be a lifesaver in an accident and can protect against severe head, brain and facial injuries, particularly integral helmets with full facial protection. The present paper introduces a newly invention of smart armor to protect motorcyclist's head and neck in an accident time and to minimize injuries.Materials & Designs: The smart armor for protecting motorcyclist's head and neck was designed innovatively. The system was constructed from boxes settled on the motorcycle, including a power source, an incident detection sensor, a transmitter circuit and a transmitter circuit antenna. In addition, the helmet parts including a mechanical operator's box, mechanical force interface cables, helmet frame, balance surface, fixed neck guard and moving neck guard were constructed. The constructed system was formally registered by the Iranian Patent Organization in 2010.Findings & Tests: The smart armor showed useful capabilities and was successfully tested by several motor cyclists in simulated accidents. The different mode of collision with obstacle ahead, back hit, and change angle more than 45 degrees from vertical mode of motorcycles were successfully tested. Conclusions: The designed smart armor presented in this article, showed a suitable performance to protect motorcyclist's head and neck simultaneously. Therefore, it can be used as a suitable protective device for motorcycle riders.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".