Measurements of change in thermal images due to applied pressure
Bibliographic record
Abstract
Thermal imaging is of value to medical professionals because of its low risk and non-invasive properties. While thermal imaging has been explored in the area of pressure ulcers, many relevant papers address existing pressure ulcers and few address the prevention of pressure ulcers. This paper aims to examine the potential of thermal imaging in the prevention of pressure ulcers by extracting temperature-based and region-based measurements from thermal images and quantifying thermal patterns. A subject was asked to press on a pressure sensor mat at two specified intensities, and a series of thermal images were taken before and after to track thermal behaviour. These images were subjected to standard image processing techniques before temperature specific contour and area measurements were extracted as well as region specific intensity and weighted centroid measurements. Results indicated that the contour and area measurements were able to capture the temperature pattern of the whole hand, while the intensity measurements were able to indicate region specific thermal patterns. These results suggest that the extraction of measurements from a series of thermal images can capture and quantify visually identifiable thermal patterns of the hand over time. These findings will be expanded upon in future work by further examining different measurements, sharper images, different equipment and the involvement of elderly patients. While future collection of patient data is expected to yield different thermal patterns, this paper has demonstrated recognition and quantification of a pattern, regardless of the pattern itself.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".