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dc.contributor.author
Cheng, Xiaogang
dc.contributor.author
Yang, Bin
dc.contributor.author
Tan, Kaige
dc.contributor.author
Isaksson, Erik
dc.contributor.author
Li, Liren
dc.contributor.author
Hedman, Anders
dc.contributor.author
Olofsson, Thomas
dc.contributor.author
Lin, Haibo
dc.date.accessioned
2019-04-23T09:07:16Z
dc.date.available
2019-04-19T01:42:56Z
dc.date.available
2019-04-23T09:07:16Z
dc.date.issued
2019-04
dc.identifier.issn
2076-3417
dc.identifier.other
10.3390/app9071375
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/338791
dc.identifier.doi
10.3929/ethz-b-000338791
dc.description.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%.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
MDPI
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
contactless measurements
en_US
dc.subject
skin sensitivity index
en_US
dc.subject
thermal comfort
en_US
dc.subject
subtleness magnification
en_US
dc.subject
deep learning
en_US
dc.subject
piecewise stationary time series
en_US
dc.title
A contactless measuring method of skin temperature based on the skin sensitivity index and deep learning
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2019-04-01
ethz.journal.title
Applied Sciences
ethz.journal.volume
9
en_US
ethz.journal.issue
7
en_US
ethz.pages.start
1375
en_US
ethz.size
14 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Basel
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2019-04-19T01:42:57Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2019-04-23T09:07:34Z
ethz.rosetta.lastUpdated
2019-04-23T09:07:34Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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