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NIDL: A pilot study of contactless measurement of skin temperature for intelligent building
College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China; School of Electrical Engineering and Computer Science, Royal Institute of Technology (KTH), Stockholm, 10044, Sweden; Computer Vision Laboratory, Swiss Federal Institute of Technology (ETH), Zürich, 8092, Switzerland.
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics. School of Building Services Science and Engineering, Xi'an University of Architecture and Technology, Xi'an, 710055, China.
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
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2019 (English)In: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 198, p. 340-352Article in journal, News item (Refereed) [Artistic work] Published
Abstract [en]

Human thermal comfort measurement plays a critical role in giving feedback signals for building energy efficiency. A contactless measuring method based on subtleness magnification and deep learning (NIDL) was designed to achieve a comfortable, energy efficient built environment. The method relies on skin feature data, e.g., subtle motion and texture variation, and a 315-layer deep neural network for constructing the relationship between skin features and skin temperature. A physiological experiment was conducted for collecting feature data (1.44 million) and algorithm validation. The contactless measurement algorithm based on a partly-personalized saturation temperature model (NIPST) was used for algorithm performance comparisons. The results show that the mean error and median error of the NIDL are 0.476 °C and 0.343°C which is equivalent to accuracy improvements of 39.07 % and 38.76 %, respectively.

Place, publisher, year, edition, pages
netherlands: Elsevier, 2019. Vol. 198, p. 340-352
Keywords [en]
Contactless method, Thermal comfort measurement, Vision-based subtleness magnification, Deep learning, Intelligent building
National Category
Civil Engineering
Identifiers
URN: urn:nbn:se:umu:diva-159772DOI: 10.1016/j.enbuild.2019.06.007ISI: 000477091800027OAI: oai:DiVA.org:umu-159772DiVA, id: diva2:1320863
Available from: 2019-06-05 Created: 2019-06-05 Last updated: 2019-08-14Bibliographically approved

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Cheng, XiaogangYang, BinOlofsson, Thomas

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