Observation of the human body thermoregulation and extraction of its vein signature using NIR and MWIR imaging
Bibliographic record
Abstract
The article aims first to present a new study on the thermal regulatory response of the human skin surface while exposed to a cold environment. Our work has shown that when a cold stress is applied to the left hand, thermal infrared imaging (MWIR spectral band: 3-5 μm) allows a clear observation of a temperature rise on the right hand. Moreover, a frequency analysis was also carried out upon selected vein pixels of the images monitored during the same cold stress experiment. The objective was to identify the specific frequencies that could be linked to some physiological mechanisms of the human body. This kind of study could be very useful for the characterization of possible thermo-physiological pathologies. Besides thermoregulation, we also present in this article some results on the extraction of the hand vein pattern. Firstly, we show some vein extraction results obtained after image processing of the thermal images recorded in the thermal band (MWIR), then we compare this vein pattern to the signature obtained with a camera operating in the NIR spectral band (0.85-1.7 μm). This method could be used as a complementary means for finger print signatures in biometrics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".