Investigating open and closed dielectric elastomer structures for the development of a soft flexible and stretchable pressure sensor array for pressure injury prevention
Bibliographic record
Abstract
A pressure injury is a complex chronic wound that forms when the delivery of oxygen and nutrients to soft tissue regions is compromised due to prolonged pressure, commonly over bony prominences, which results in local ischemia, cell death and potentially fatal infections. Its early diagnosis and prediction are challenging, despite technological advancements. It remains one of the most burdensome, costly and fatal secondary medical conditions, which affects millions of people annually. Here, we present a soft, flexible and stretchable pressure sensor array made out of silicone elastomer material, carbon black particles and stretchable, conductive, silver-plated fabric. Its working principle is based on capacitive sensing, where electrodes form an array of parallel plate-like capacitors that enable the detection of pressure due to the deformation of the dielectric layer. We explored a variety of different dielectric architectures consisting of pillar structures of various shapes that make it compressible and potentially increase sensitivity. The sensor array is designed to be shape-conformable, scalable in size and resolution, and able to detect and measure pressure within the desired pressure range for pressure injuries (0-200 mmHg) over short (≤15 minutes) and long periods (≥8 hours) with consistent accuracy and low repeatability error.
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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 | 0.001 |
| 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".