A Force-Sensing Insole to Quantify Impact Loading to the Foot
Bibliographic record
Abstract
Lower leg injuries commonly occur in frontal automobile collisions, and are associated with high disability rates. Accurate methods to predict these injuries must be developed to facilitate the testing and improvement of vehicle safety systems. Anthropomorphic test devices (ATDs) are often used to assess injury risk by mimicking the behavior of the human body in a crash while recording data from sensors at discrete locations, which are then compared to established safety limits developed by cadaveric testing. Due to the difference in compliance of cadaveric and ATD legs, the force dissipating characteristics of footwear, and the lack of direct measurement of injury risk to the foot and ankle, a novel instrumented insole was developed that could be applied equally to all specimens both during injury limit generation and during safety evaluation tests. An array of piezoresistive sensors were calibrated over a range of speeds using a pneumatic impacting apparatus, and then applied to the insole of a boot. The boot was subsequently tested and compared to loads measured using ankle and toe load cells in an ATD, and found to have an average error of 10%. The sensors also provided useful information regarding the force distribution across the sole of the foot during an impact, which may be used to develop regional injury criteria. This work has furthered the understanding of lower leg injury prediction and developed a tool that may be useful in developing accurate injury criteria in the future for the foot and lower leg.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".