Spatial Efficacy of Respiration Monitoring using Doppler Radars for Personalized Thermal Comfort Assessment
Bibliographic record
Abstract
Spatial Efficacy of Respiration Monitoring using Doppler Radars for Personalized Thermal Comfort Assessment Wooyoung Jung and Farrokh Jazizadeh Pages 1034-1041 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: Recent research efforts have shown that human thermophysiological features could play a crucial role in inferring occupants thermal comfort, which is required for comfort-aware heating, ventilation, and air conditioning (HVAC) operation. Our previous studies have demonstrated that variations of respiration, a representative human thermophysiological feature, can be non-intrusively quantified by a Doppler radar sensor (DRS). However, in pursuit of enabling human-aware rooms in buildings, in this study we have explored the impact of distance and position of the respiration monitoring system to investigate the potential of DRS systems as a ubiquitous apparatus in the real-world scenarios. Through experimental studies, respiration characteristics were evaluated in different locations and angles relative to the location of the measurement device. The measurements were carried out using a DRS system and a respiratory belt for ground truth data collection. The noise artifacts were reduced by applying the Savitzky-Golay method and Hann window, and respiration was identified by selecting the frequency component with the maximum amplitude in the typical breathing frequency range (0.1 to 0.5 Hz). Our analyses demonstrated that the signal from a cost-effective DRS technology without the use of external amplifiers could cover a range, within 1.0m longitudinally and 0.5m laterally, which is sufficient for an individual sensing given a normal office environment. It was also observed that the use of an external amplifier extends the range of the DRS sensing but at the same time accentuates the noise. Therefore, advanced noise removal methods are needed to increase the range of robust sensing. This study contributes to DRS deployment strategies for realization of comfort-aware systems. Keywords: Comfort-aware HVAC operation; Doppler radar; Respiration monitoring DOI: https://doi.org/10.22260/ISARC2019/0138 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".