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A Contactless Measuring Method of Skin Temperature based on the Skin Sensitivity Index and Deep Learning
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..
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.
KTH, School of Electrical Engineering and Computer Science (EECS), Media Technology and Interaction Design, MID.ORCID iD: 0000-0002-8276-4471
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2019 (English)In: Applied Sciences, E-ISSN 2076-3417, Vol. 9, no 7, article id 1375Article in journal (Refereed) Published
Abstract [en]

Featured Application The NISDL method proposed in this paper can be used for real time contactless measuring of human skin temperature, which reflects human body thermal comfort status and can be used for control HVAC devices. Abstract 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 degrees C, 0.25 degrees C), and the same error intervals distribution of NIPST is 35.39%.

Place, publisher, year, edition, pages
MDPI , 2019. Vol. 9, no 7, article id 1375
Keywords [en]
contactless measurements, skin sensitivity index, thermal comfort, subtleness magnification, deep learning, piecewise stationary time series
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-254118DOI: 10.3390/app9071375ISI: 000466547500110Scopus ID: 2-s2.0-85064083775OAI: oai:DiVA.org:kth-254118DiVA, id: diva2:1328944
Note

QC 20190624

Available from: 2019-06-24 Created: 2019-06-24 Last updated: 2019-06-24Bibliographically approved

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Isaksson, ErikHedman, AndersLi, Haibo

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Tan, KaigeIsaksson, ErikHedman, AndersLi, Haibo
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