TY - JOUR
T1 - Automatic estimation of clothing insulation rate and metabolic rate for dynamic thermal comfort assessment
AU - Liu, Jinsong
AU - Worre Foged, Isak
AU - Moeslund, Thomas B.
PY - 2022/8
Y1 - 2022/8
N2 - 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 (Icl) 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, Icl 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 Icl and M estimation module, proving the effectiveness and automation of the proposed approach.
AB - 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 (Icl) 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, Icl 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 Icl and M estimation module, proving the effectiveness and automation of the proposed approach.
KW - Clothing insulation rate
KW - Computer vision
KW - Metabolic rate
KW - Thermal camera
KW - Thermal comfort
UR - http://www.scopus.com/inward/record.url?scp=85101507299&partnerID=8YFLogxK
U2 - 10.1007/s10044-021-00961-5
DO - 10.1007/s10044-021-00961-5
M3 - Journal article
SN - 1433-7541
VL - 25
SP - 619
EP - 634
JO - Pattern Analysis and Applications
JF - Pattern Analysis and Applications
IS - 3
ER -