Foot ulcer prevention using biomechanical modelling
Bibliographic record
Abstract
Foot ulcers are a common complication of diabetes and are the consequence of trauma to the feet and a reduced ability to perceive pain in persons with diabetes. Ulcers appear internally when pressures applied on the foot create high-internal strains below bony structures. It is therefore important to monitor tissue strains in persons with diabetes. We propose to use a biomechanical model of the foot coupled with a pressure sensor to estimate the strains within the foot and to determine whether they can cause ulcer formation. Our biomechanical foot model is composed of a finite element mesh representing the soft tissues, separated into four Neo-Hookean materials with different elasticity: plantar skin, non-plantar skin, fat and muscles. Rigid body models of the bones are integrated within the mesh to rigidify the foot. Thirty-three joints connect those bones around cylindrical or spherical pivots. Cables are included to represent the main ligaments in order to stabilise the foot. This model simulates a realistic behaviour when the sole is subjected to pressures measured with a sensor during bipedal standing. Surface strains around 5% are measured below the heel and metatarsal heads, while internal strains are close to 70%. This strain estimation, when coupled to a pressure sensor, could consequently be used in a patient alert system to prevent ulcer formation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".