Shear capable soft sensor technology for the application of pressure ulcer detection in diabetics
Bibliographic record
Abstract
Diabetic pressure ulcers (DFU) are one of the most common complications related to diabetes, a disease that has become a global epidemic affecting many countries, especially modern and rich ones. The financial cost of treating DFUs this disease is monumental, costing $550 million yearly in Canada alone to treat. the DFUs being one of the costliest outcomes of diabetes, can lead to a sedimentary lifestyle for individuals that could benefit from physical activity to combat their diabetes. There is a need to develop technology that can sense and monitor the condition of feet at this crucial crossroad. This thesis builds on a capacitive sensor developed in our lab that can measure normal and shear stress simultaneously, made from soft, comfortable, and affordable materials which could be implemented into an insole or modified shoe device. The sensor was characterized using modified protocols of existing methodology to establish sensitivity, repeatability, and proper calibration. Overall, the sensor can measure stresses in the prescribed ranges for normal (0-1000 kPa) and shear (0-200 kPa) and is responsive, in the lab as well as real-life testing, to the different time regimes it is being designed for (standing and walking). We show that the sensor is suited well for measuring displacement change in the foot to capture anatomy change in the foot and swelling. While the force characterization has been described, there is still a good amount of work to establish this sensing parameter to coupe with hysteresis and creep (in the worst case 24% of the full scale) present in the deformation of the materials being used. There is a tradeoff to contend with that combines the comfort and softness of the sensor to its ability to withstand high forces and how the modeling of these deformations is accurate and relevant to our clinical considerations. This technology could be a game-changer for the common diabetic and here we lay out the framework to make soft normal and shear stress in-shoe sensors a reality.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".