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Automatic estimation of clothing insulation rate and metabolic rate for dynamic thermal comfort assessment.
- Source :
-
Pattern Analysis & Applications . Aug2022, Vol. 25 Issue 3, p619-634. 16p. - Publication Year :
- 2022
-
Abstract
- Existing heating, ventilation, and air-conditioning systems have difficulties in considering occupants' dynamic thermal needs, thus resulting in overheating or overcooling with huge energy waste. This situation emphasizes the importance of occupant-oriented microclimate control where dynamic individual thermal comfort assessment is the key. Therefore, in this paper, a vision-based approach to estimate individual clothing insulation rate ( I cl ) and metabolic rate (M), the two critical factors to assess personal thermal comfort level, is proposed. Specifically, with a thermal camera as the input source, a convolutional neural network (CNN) is implemented to recognize an occupant's clothes type and activity type simultaneously. The clothes type then helps to differentiate the skin region from the clothing-covered region, allowing to calculate the skin temperature and the clothes temperature. With the two recognized types and the two computed temperatures, I cl and M can be estimated effectively. In the experimental phase, a novel thermal dataset is introduced, which allows evaluations of the CNN-based recognizer module, the skin and clothes temperatures acquisition module, as well as the I cl and M estimation module, proving the effectiveness and automation of the proposed approach. [ABSTRACT FROM AUTHOR]
- Subjects :
- *SKIN temperature
*THERMAL comfort
*CONVOLUTIONAL neural networks
Subjects
Details
- Language :
- English
- ISSN :
- 14337541
- Volume :
- 25
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- Pattern Analysis & Applications
- Publication Type :
- Academic Journal
- Accession number :
- 157956927
- Full Text :
- https://doi.org/10.1007/s10044-021-00961-5