Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
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, Skolan för elektroteknik och datavetenskap (EECS), Medieteknik och interaktionsdesign, MID.ORCID-id: 0000-0002-8276-4471
Vise andre og tillknytning
2019 (engelsk)Inngår i: Applied Sciences, E-ISSN 2076-3417, Vol. 9, nr 7, artikkel-id 1375Artikkel i tidsskrift (Fagfellevurdert) 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%.

sted, utgiver, år, opplag, sider
MDPI , 2019. Vol. 9, nr 7, artikkel-id 1375
Emneord [en]
contactless measurements, skin sensitivity index, thermal comfort, subtleness magnification, deep learning, piecewise stationary time series
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-254118DOI: 10.3390/app9071375ISI: 000466547500110Scopus ID: 2-s2.0-85064083775OAI: oai:DiVA.org:kth-254118DiVA, id: diva2:1328944
Merknad

QC 20190624

Tilgjengelig fra: 2019-06-24 Laget: 2019-06-24 Sist oppdatert: 2019-06-24bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekstScopus

Personposter BETA

Isaksson, ErikHedman, AndersLi, Haibo

Søk i DiVA

Av forfatter/redaktør
Tan, KaigeIsaksson, ErikHedman, AndersLi, Haibo
Av organisasjonen
I samme tidsskrift
Applied Sciences

Søk utenfor DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric

doi
urn-nbn
Totalt: 98 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf