Evaluating compliance with HeatSuite for monitoring in situ physiological and perceptual responses and personal environmental exposure
Bibliographic record
Abstract
Extreme heat events pose a significant health threat to vulnerable populations such as the elderly and those living with disease. Recent extreme heat events highlight that heat-related mortality often occurs indoors, urging a need to better understand how at-risk populations physiologically and behaviorally respond in their natural environment. However, a low-cost and scalable all-in-one solution to comprehensively monitor individuals during periods of extreme heat does not presently exist. We developed HeatSuite, a fully data-governed multimodal sensor platform, that can monitor the local environmental conditions, and physiological and behavioural responses, of free-living individuals. Compliance to the platform was assessed over 28 days among 21 older individuals living in low-income housing (70 ± 7 y, body mass index: 28.7 ± 6.3). Moderate (>77%) to near optimal (94%) compliance was observed among the physiological and perceptual metrics obtained. In conclusion, HeatSuite is an effective and comprehensive solution for at-home monitoring of at-risk populations.
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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 | 0.000 |
| 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) | 0.000 | 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".