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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 universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för tillämpad fysik och elektronik. School of Building Services Science and Engineering, Xi'an University of Architecture and Technology, Xi'an, 710055, China.
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för tillämpad fysik och elektronik.
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2019 (Engelska)Ingår i: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 198, s. 340-352Artikel i tidskrift (Refereegranskat) [Forskning på konstnärlig grund] 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.

Ort, förlag, år, upplaga, sidor
Elsevier, 2019. Vol. 198, s. 340-352
Nyckelord [en]
Contactless method, Thermal comfort measurement, Vision-based subtleness magnification, Deep learning, Intelligent building
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Identifikatorer
URN: urn:nbn:se:umu:diva-159772DOI: 10.1016/j.enbuild.2019.06.007ISI: 000477091800027Scopus ID: 2-s2.0-85067305627OAI: oai:DiVA.org:umu-159772DiVA, id: diva2:1320863
Tillgänglig från: 2019-06-05 Skapad: 2019-06-05 Senast uppdaterad: 2019-09-06Bibliografiskt granskad

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Cheng, XiaogangYang, BinOlofsson, Thomas

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