Infrared thermal imaging as a physiological access pathway: a study of the baseline characteristics of facial skin temperatures
Bibliographic record
Abstract
In this study we examine the baseline characteristics of facial skin temperature, as measured by dynamic infrared thermal imaging, to gauge its potential as a physiological access pathway for non-verbal individuals with severe motor impairments. Frontal facial recordings were obtained from 12 asymptomatic adults in a resting state with a high-end infrared thermal imaging system. From the infrared thermal recordings, mean skin temperature time series were generated for regions of interest encompassing the nasal, periorbital and supraorbital areas. A 90% bandwidth for all regions of interest was found to be in the 1 Hz range. Over 70% of the time series were identified as nonstationary (p<0.05), with the nonstationary mean as the greatest contributing source. Correlation coefficients between regions were significant (p<0.05) and ranged from values of 0.30 (between periorbital and supraorbital regions) to 0.75 (between contralateral supraorbital regions). Using information measures, we concluded that the greatest degree of information existed in the nasal and periorbital regions. Mutual information existed across all regions but was especially prominent between the nasal and periorbital regions. Results from this study provide insight into appropriate analysis methods and potential discriminating features for the application of facial skin temperature as a physiological access pathway.
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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 | 0.003 |
| 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 | 0.000 |
| 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".