Investigating the dynamics of thermal perception, physiological responses, and task performance in office environments
Bibliographic record
Abstract
Understanding occupant thermal comfort in office environments can be achieved by examining the interplay between environmental data, physiological measurements, and subjective feedback. The increasing adoption of wearable sensing technologies enables inferring thermal comfort based on physiological data, potentially transforming HVAC system design to better respond to occupant needs. However, there is a research gap regarding accurate methods to infer occupants’ perceptions of comfort. Furthermore, thermal comfort comparisons across different genders and locations are understudied. This study presents findings from an experimental investigation focused on the relationship between physiological signals, thermal comfort and task performance. Three key physiological measures—electroencephalography (EEG), Heart Rate Variability (HRV), and skin temperature (ST)—were captured from 52 participants exposed to three distinct thermal conditions (slightly cool, neutral, and slightly warm) in a controlled office setting. The analysis compared thermal comfort perception, physiological measurements, and task performance across male and female participants and two locations: Cairo, characterized by a hot desert climate (BWh, ASHRAE climate zone 1B), and Montreal, characterized by a cold temperate climate (Dfb, ASHRAE climate zone 6A). Data from 156 tests were statistically analyzed, revealing gender differences in skin temperature responses across thermal conditions. Additionally, participants in Cairo exhibited heart rates approximately 15% higher under slightly warm conditions than those in Montreal, indicating location-based physiological variations. Moreover, task performance was about 20% more sensitive to thermal conditions for males than females. These findings provide valuable insights into the relationship between physiological responses, thermal comfort perceptions, and occupant performance in office environments, supporting the development of more responsive thermal comfort models.
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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 | 0.002 |
| 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 | 0.001 | 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".