Accuracy of a Wearable Sensor for Measures of Head Kinematics and Calculation of Brain Tissue Strain
Bibliographic record
Abstract
Wearable kinematic sensors can be used to study head injury biomechanics based on kinematics and, more recently, based on tissue strain metrics using kinematics-driven brain models. These sensors require in-situ calibration and there is currently no data conveying wearable ability to estimate tissue strain. We simulated head impact (n = 871) to a 50th percentile Hybrid III (H-III) head wearing a hockey helmet instrumented with wearable GForceTracker (GFT) sensors measuring linear acceleration and angular velocity. A GFT was also fixed within the H-III head to establish a lower boundary on systematic errors. We quantified GFT errors relative to H-III measures based on peak kinematics and cumulative strain damage measure (CSDM). The smallest mean errors were 12% (peak resultant linear acceleration) and 15% (peak resultant angular velocity) for the GFT within the H-III. Errors for GFTs on the helmet were on average 54% (peak resultant linear acceleration) and 21% (peak resultant angular velocity). On average, the GFT inside the helmet overestimated CSDM by 0.15.
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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 | 0.002 | 0.010 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".