A Study of Thermistor Performance within a Textile Structure
Bibliographic record
Abstract
Textiles provide an ideal structure for embedding sensors for medical devices. Skin temperature measurement is one area in which a sensor textile could be particularly beneficial; pathological skin is normally very sensitive, making the comfort of anything placed on that skin paramount. Skin temperature is an important parameter to measure for a number of medical applications, including for the early detection of diabetic foot ulcer formation. To this end an electronic temperature-sensor yarn was developed by embedding a commercially available thermistor chip into the fibres of a yarn, which can be used to produce a textile or a garment. As part of this process a resin was used to encapsulate the thermistor. This protects the thermistor from mechanical and chemical stresses, and also allows the sensing yarn to be washed. Building off preliminary work, the behaviour and performance of an encapsulated thermistor has been characterised to determine the effect of encapsulation on the step response time and absolute temperature measurements. Over the temperature range of interest only a minimal effect was observed, with step response times varying between 0.01-0.35 s. A general solution is presented for the heat transfer coefficient compared to size of the micro-pod formed by the encapsulation of the thermistor. Finally, a prototype temperature-sensing sock was produced using a network of sensing yarns as a demonstrator of a system that could warn of impending ulcer formation in diabetic patients.
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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 | 0.002 |
| 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 | 0.001 | 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".