Novel Insole Material for Weight-Bearing Monitoring Medical Device to Be Used in Fractured Bone Clinic
Bibliographic record
Abstract
Partial Weight-Bearing (PWB) regime is a part of a recreation process for patients with lower limb fractures/strains/sprains. In order to avoid a frequent foot overloading and achieve better patient compliance with requirement of PWB, the application of Weight-Bearing Monitoring System (WBMS) is highly attractive. However, it is challenging to find an affordable material for protective insole cover that is also a good shock-absorber. In this regard, a new formulation for protective covering material that preserves and isolates the sensors of WBMS device is suggested in this thesis. Twenty one samples of renewably sourced Polyurethane Foam (PUF) composed of poly (trimethylene ether) glycol (PO3G) and unmodified castor oil (CO) were synthesized and evaluated according to predetermined criteria. Response surface methodology of Box – Behnken design was applied to study the effect of the following parameters (polyols ratio, isocyanate index (II), and blowing agent ratio) on the properties (hardness, density) of PUFs. Results showed that CO/PO3G/TDI PUFs with hardness Shore A 17-22 and density of 0.19-0.25 g/cm3 demonstrate the required characteristics and can potentially be used as a durable and functional insole material. Phase separation studies have found the presence of well-segregated structure in PUFs having polyols ratio CO:PO3G 1:3 and low II, which further explains their extraordinary elastic properties (400% elongation). Analysis of cushioning performance of PUF signified that 5 samples have Cushioning Energy (CE) higher than 70 N·mm and Cushioning Factor (CF) in the range of 4-8, hence are recommended for application in WBMS due to superior weight-bearing and pressure-distributing properties. Moreover, the developed formulation undergoes anaerobic soil bacterial degradation and can be categorized as “green” bio-based material.
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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 | 0.001 | 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) | 0.003 | 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".