Single-pixel infrared imaging thermometry maps human inner canthi temperature
Bibliographic record
Abstract
Efficiently and accurately mapping the temperature of human inner canthi is crucial for disease diagnostics and monitoring. The specific anatomical location of the inner canthi precludes temperature screening methods that are invasive, require tissue contact, and/or demand active illumination. Camera-based thermography, although capable of passive and non-contact temperature mapping, suffers from low efficiency in pixel allocation to the inner canthi as well as from measurement inaccuracies due to background blending and moderate pixel sensitivity. In response to these challenges, we develop single-pixel infrared imaging thermometry (SPIRIT). We design diagonally aggregated two-dimensional transmissive encoding masks using a cyclic S-matrix, which supports compressed data acquisition in a single scan and high image quality through non-iterative reconstruction. SPIRIT maps the temperature distribution of human inner canthi with a resolution of 0.3 °C, which enables human temperature mapping via single-pixel imaging. Using SPIRIT, we reveal sub-degree temperature differences induced by daily physical activities and the glasses-wearing habit. These findings shed light on SPIRIT's contribution to improving evaluation criteria for public health, including COVID-19 febrile screening.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".