Development of a Novel Customized Insole for Effective Pressure Offloading in Diabetic Patients
Bibliographic record
Abstract
The number of people with diabetes is rising day-by-day, which also raises the incidence of diabetic ulcers, sensation loss in the foot’s plantar area, and in extreme instances, amputations. Using customized shoes, unloading orthoses, insoles, and other strategies may help control these issues to some degree. In this work, a novel modular diabetic insole was designed and fabricated to effectively offload the abnormal or peak plantar pressures in diabetic patients. The pressure values in the plantar region were quantified using an in-house-developed plantar pressure-measuring insole consisting of force sensitive resistor (FSR) sensors. The effectiveness of the modular diabetic insole was tested qualitatively and quantitatively. The qualitative performance of the insole was reported using Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST 2.0) in terms of subjective parameters like comfort, ease of use, effectiveness, etc. and calculated as 4.7 ± 0.18. Thereafter, the wearable pressure-measuring insole was used to investigate the feasibility of modular insole for the plantar pressure offloading during standing and walking conditions. It was observed that the maximum average zonal pressure (AZP) was reduced by up to 99% from 121.30 ± 3.72 kPa to 0.22 ± 0.18 kPa for the standing condition whereas it was reduced to 6.76 ± 2.03 kPa from 197.71 ± 3.21 kPa with a percentage value of 96% for the walking condition. In conclusion, the findings of this work validate the effectiveness of the modular diabetic insole as an intervention tool for diabetic foot ulcer prevention.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".