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Real-time and contactless measurements of thermal discomfort based on human poses for energy efficient control of buildings
Xian Univ Architecture & Technol, Sch Bldg Serv Sci & Engn, Xian 710055, Shaanxi, Peoples R China.;Umea Univ, Dept Appl Phys & Elect, S-90187 Umea, Sweden..
KTH, School of Electrical Engineering and Computer Science (EECS). Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing 210003, Jiangsu, Peoples R China.;Swiss Fed Inst Technol, Comp Vis Lab, CH-8092 Zurich, Switzerland..
Swiss Fed Inst Technol, Comp Vis Lab, CH-8092 Zurich, Switzerland..
Umea Univ, Dept Appl Phys & Elect, S-90187 Umea, Sweden..
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2019 (English)In: Building and Environment, ISSN 0360-1323, E-ISSN 1873-684X, Vol. 162, article id UNSP 106284Article in journal (Refereed) Published
Abstract [en]

Individual thermal discomfort perception gives important feedback signals for energy efficient control of building heating, ventilation and air conditioning systems. However, there is few effective method to measure thermal discomfort status of occupants in a real-time and contactless way. A novel method based on contactless measurements of human thermal discomfort status was presented. Images of occupant poses, which are related to thermoregulation mechanisms, were captured by a digital camera and the corresponding 2D coordinates were obtained. These poses were converted into skeletal configurations. An algorithm was developed to recognize different poses related to thermal discomfort, such as hugging oneself or wiping sweat off the brow. The algorithm could recognize twelve thermal discomfort related human poses. These poses were derived from a questionnaire survey of 369 human subjects. Some other human subjects participated in the validation experiments of the proposed method. All twelve thermal discomfort related poses can be recognized effectively.

Place, publisher, year, edition, pages
PERGAMON-ELSEVIER SCIENCE LTD , 2019. Vol. 162, article id UNSP 106284
Keywords [en]
Contactless measurement, Thermal discomfort, Human pose, Machine learning
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-261035DOI: 10.1016/j.buildenv.2019.106284ISI: 000484514400018Scopus ID: 2-s2.0-85070109030OAI: oai:DiVA.org:kth-261035DiVA, id: diva2:1356740
Note

QC 20191002

Available from: 2019-10-02 Created: 2019-10-02 Last updated: 2019-10-04Bibliographically approved

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Cheng, XiaogangLi, Haibo

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