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Volume 9
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10.3390/app9071375
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Article

A Contactless Measuring Method of Skin Temperature based on the Skin Sensitivity Index and Deep Learning

by
Xiaogang Cheng
1,2,
Bin Yang
3,4,*,
Kaige Tan
5,
Erik Isaksson
5,
Liren Li
6,
Anders Hedman
5,
Thomas Olofsson
4 and
Haibo Li
1,5
1
College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2
Computer Vision Laboratory (CVL), ETH Zürich, 8092 Zürich, Switzerland
3
School of Building Services Science and Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
4
Department of Applied Physics and Electronics, Umeå University, 90187 Umeå, Sweden
5
KTH Royal Institute of Technology, 10044 Stockholm, Sweden
6
School of computer science and technology, Nanjing Tech University, Nanjing 211816, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(7), 1375; https://doi.org/10.3390/app9071375
Submission received: 16 December 2018 / Revised: 27 February 2019 / Accepted: 15 March 2019 / Published: 1 April 2019
(This article belongs to the Special Issue Indoor Air Quality)
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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 °C, 0.25 °C), and the same error intervals distribution of NIPST is 35.39%.
Keywords: contactless measurements; skin sensitivity index; thermal comfort; subtleness magnification; deep learning; piecewise stationary time series contactless measurements; skin sensitivity index; thermal comfort; subtleness magnification; deep learning; piecewise stationary time series

Share and Cite

MDPI and ACS Style

Cheng, X.; Yang, B.; Tan, K.; Isaksson, E.; Li, L.; Hedman, A.; Olofsson, T.; Li, H. A Contactless Measuring Method of Skin Temperature based on the Skin Sensitivity Index and Deep Learning. Appl. Sci. 2019, 9, 1375. https://doi.org/10.3390/app9071375

AMA Style

Cheng X, Yang B, Tan K, Isaksson E, Li L, Hedman A, Olofsson T, Li H. A Contactless Measuring Method of Skin Temperature based on the Skin Sensitivity Index and Deep Learning. Applied Sciences. 2019; 9(7):1375. https://doi.org/10.3390/app9071375

Chicago/Turabian Style

Cheng, Xiaogang, Bin Yang, Kaige Tan, Erik Isaksson, Liren Li, Anders Hedman, Thomas Olofsson, and Haibo Li. 2019. "A Contactless Measuring Method of Skin Temperature based on the Skin Sensitivity Index and Deep Learning" Applied Sciences 9, no. 7: 1375. https://doi.org/10.3390/app9071375

APA Style

Cheng, X., Yang, B., Tan, K., Isaksson, E., Li, L., Hedman, A., Olofsson, T., & Li, H. (2019). A Contactless Measuring Method of Skin Temperature based on the Skin Sensitivity Index and Deep Learning. Applied Sciences, 9(7), 1375. https://doi.org/10.3390/app9071375

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