Towards the evaluation of force-sensing resistors for in situ measurement of interface pressure during leg compression therapy
Bibliographic record
Abstract
While compression therapy is the cornerstone in managing leg venous disorders, the applied pressure has to be in specific ranges in order to have an effective treatment. Medical bandages and compression stockings lack an embedded pressure measurement system. The listed class of compression for medical stockings is not utterly reliable, and achieving desired pressure profiles in medical bandages is dependent solely on the bandaging skills of clinicians. Moreover, after the recipients of compression products leave medical centers, there exists no way to continuously assess the changes in sub-bandage pressure that might occur due to movements and physiological changes of the lower extremities. Thus there is a need for a valid and reliable measurement system that can be integrated into compression products for continuous monitoring of the interface pressure. In the current study, force-sensing resistors (FSRs®), which are portable, thin, flexible, low-cost, and easy-to-use sensors, were investigated. FSRs, like many other flexible resistive sensors, are known for their qualitative rather than quantitative measurements. Therefore, they should be validated for clinical use. In this study, the FSRs were first calibrated on different surfaces, including human leg, and then evaluated in measuring interface pressures in situ. The preliminary investigations showed promising results.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".