Entropic Characterization of Multiple Physiological Responses with Statistical Process Control Charts
Bibliographic record
Abstract
Numerous studies have been conducted in the past to measure and characterize human stress response using single physiological indicators.The proposed study presents a unique thermodynamic concept to provide a quantitative measure of stress response by combining multiple physiological responses using Maxwell relations.It combines five measurable peripheral physiological signals such as blood pressure, heart rate, finger skin temperature, electromyogram, and electrodermal response to provide a quantitative measure of entropy change, which is used as a key performance indicator (KPI).The data obtained from a NASA human engineering pilot study involving seven subjects are used to demonstrate this methodology.The five physiological signals are combined into two entropy change metrics.The entropy change as a KPI is represented on the statistical process control charts (SPC) with mean, upper control limit (UCL), and lower control limit (LCL) values.Both visual and single factor ANOVA tests show a significant statistical difference in individual physiological entropy change.In summary, the entropy-change shows great potential to be used as a KPI for monitoring physiological stress level and health status in various healthcare applications.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".