In-Fiber Bragg Grating Impact Force Transducer for Studying Head–Helmet Mechanical Interaction in Head Impact
Bibliographic record
Abstract
In this paper, we present the first Bragg-grating-based transducer system for application to trauma biomechanics, more specifically head-helmet contact force measurements secondary to headform impact. The transducer comprises an aluminum superstructure designed to withstand typical impact forces in helmeted impact and to have resonances that allow the overall sensor system to capture all relevant spectral components of force transients in impact. Structural finite-element models and strain-optic relationships are used to predict transducer sensitivity to impact force as well as mechanical resonances. The model predictions are verified through experimental calibration, and calibration results are, on average, within 10% of model predictions of force sensitivity. The model predicted first mechanical resonance is 72 kHz. The impact force transducer is also validated for helmeted impacts using an industry standard impact experiment and test headform. Results indicate excellent repeatability: maximum standard deviation of force measurements of 0.4% of the net force applied to the impacted headform and average error in the time duration of the force transients of only 4%. Transient impact forces measured with the Bragg grating transducer are in agreement with magnitudes inferred from work of previous researchers. The presented transducer can be applied with both helmet test and anthropomorphic headforms to measure distributions of transient forces and therefore hypotheses related to helmet performance and head injury.
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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) | 0.001 | 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 | 0.001 |
| 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".