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A Contactless Measuring Method of Skin Temperature based on the Skin Sensitivity Index and Deep Learning
College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China; Computer Vision Laboratory (CVL), ETH Zürich, 8092 Zürich, 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.
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2019 (Engelska)Ingår i: Applied Sciences: APPS, ISSN 1454-5101, E-ISSN 1454-5101, Vol. 9, nr 7, artikel-id 1375Artikel i tidskrift (Refereegranskat) [Forskning på konstnärlig grund] Published
Abstract [en]

In human-centered intelligent building, real-time measurements of human thermal comfort play critical roles and supply feedback control signals for building heating, ventilation, and air conditioning (HVAC) systems. Due to the challenges of intra- and inter-individual differences and skin subtleness variations, there has not been any satisfactory solution for thermal comfort measurements until now. In this paper, a contactless measuring method based on a skin sensitivity index and deep learning (NISDL) was proposed to measure real-time skin temperature. A new evaluating index, named the skin sensitivity index (SSI), was defined to overcome individual differences and skin subtleness variations. To illustrate the effectiveness of SSI proposed, a two multi-layers deep learning framework (NISDL method I and II) was designed and the DenseNet201 was used for extracting features from skin images. The partly personal saturation temperature (NIPST) algorithm was use for algorithm comparisons. Another deep learning algorithm without SSI (DL) was also generated for algorithm comparisons. Finally, a total of 1.44 million image data was used for algorithm validation. The results show that 55.62% and 52.25% error values (NISDL method I, II) are scattered at (0 °C, 0.25 °C), and the same error intervals distribution of NIPST is 35.39%. 

Ort, förlag, år, upplaga, sidor
Switzerland: MDPI, 2019. Vol. 9, nr 7, artikel-id 1375
Nyckelord [en]
contactless measurements, skin sensitivity index, thermal comfort, subtleness magnification, deep learning, piecewise stationary time series
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URN: urn:nbn:se:umu:diva-159773DOI: 10.3390/app9071375ISI: 000466547500110Scopus ID: 2-s2.0-85064083775OAI: oai:DiVA.org:umu-159773DiVA, id: diva2:1320864
Tillgänglig från: 2019-06-05 Skapad: 2019-06-05 Senast uppdaterad: 2019-11-27Bibliografiskt granskad

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Cheng, XiaogangYang, BinOlofsson, Thomas

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