NIDL: A pilot study of contactless measurement of skin temperature for intelligent building

被引：52

作者：

Cheng, Xiaogang ^{[1
,4
,5
]}

Yang, Bin ^{[2
,3
]}

Hedman, Anders ^{[4
]}

Olofsson, Thomas ^{[3
]}

Li, Haibo ^{[1
,4
]}

Van Gool, Luc ^{[5
]}

机构：

[1] Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing 210003, Jiangsu, Peoples R China

[2] Xian Univ Architecture & Technol, Sch Bldg Serv Sci & Engn, Xian 710055, Shaanxi, Peoples R China

[3] Umea Univ, Dept Appl Phys & Elect, S-90187 Umea, Sweden

[4] Royal Inst Technol KTH, Sch Elect Engn & Comp Sci, S-10044 Stockholm, Sweden

[5] Swiss Fed Inst Technol, Comp Vis Lab, CH-8092 Zurich, Switzerland

来源：

ENERGY AND BUILDINGS | 2019年 / 198卷

基金：

中国国家自然科学基金;

关键词：

Contactless method; Thermal comfort measurement; Vision-based subtleness magnification; Deep learning; Intelligent building; THERMAL COMFORT; PREDICTION; PREFERENCE; MODEL; SENSATION;

D O I：

10.1016/j.enbuild.2019.06.007

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

Human thermal comfort measurement plays a critical role in giving feedback signals for building energy efficiency. A contactless measuring method based on subtleness magnification and deep learning (NIDL) was designed to achieve a comfortable, energy efficient built environment. The method relies on skin feature data, e.g., subtle motion and texture variation, and a 315-layer deep neural network for constructing the relationship between skin features and skin temperature. A physiological experiment was conducted for collecting feature data (1.44 million) and algorithm validation. The contactless measurement algorithm based on a partly-personalized saturation temperature model (NIPST) was used for algorithm performance comparisons. The results show that the mean error and median error of the NIDL are 0.476 degrees C and 0.343 degrees C which is equivalent to accuracy improvements of 39.07% and 38.76%, respectively. (C) 2019 Elsevier B.V. All rights reserved.

引用

页码：340 / 352

页数：13

共 40 条

[21] An online learning approach for quantifying personalized thermal comfort via adaptive stochastic modeling [J].

Ghahramani, Ali ;

Tang, Chao ;

Becerik-Gerber, Burcin .

BUILDING AND ENVIRONMENT, 2015, 92 :86-96

[22] Personal comfort models: Predicting individuals' thermal preference using occupant heating and cooling behavior and machine learning [J].

Kim, Joyce ;

Zhou, Yuxun ;

Schiavon, Stefano ;

Raftery, Paul ;

Brager, Gail .

BUILDING AND ENVIRONMENT, 2018, 129 :96-106

[23] Thermal sensation: a mathematical model based on neurophysiology [J].

Kingma, B. R. M. ;

Schellen, L. ;

Frijns, A. J. H. ;

Lichtenbelt, W. D. van Marken .

INDOOR AIR, 2012, 22 (03) :253-262

[24]

LeCun Y, 2015, NATURE, V521, P436, DOI [10.1038/nature14539, DOI 10.1038/NATURE14539]

[25] Inference of thermal preference profiles for personalized thermal environments with actual building occupants [J].

Lee, Seungjae ;

Karava, Panagiota ;

Tzempelikos, Athanasios ;

Bilionis, Ilias .

BUILDING AND ENVIRONMENT, 2019, 148 :714-729

[26] Evaluation of calculation methods of mean skin temperature for use in thermal comfort study [J].

Liu, Weiwei ;

Lian, Zhiwei ;

Deng, Qihong ;

Liu, Yuanmou .

BUILDING AND ENVIRONMENT, 2011, 46 (02) :478-488

[27] Prediction of the thermal comfort indices using improved support vector machine classifiers and nonlinear kernel functions [J].

Megri, Ahmed Cherif ;

El Naqa, Issam .

INDOOR AND BUILT ENVIRONMENT, 2016, 25 (01) :6-16

[28] Real-time human skin temperature analysis using thermal image recognition for thermal comfort assessment [J].

Metzmacher, Henning ;

Woelki, Daniel ;

Schmidt, Carolin ;

Frisch, Jerome ;

van Treeck, Christoph .

ENERGY AND BUILDINGS, 2018, 158 :1063-1078

[29]

National Academies of Sciences Engineering and Medicine, 2003, Information Technology for Counterterrorism: Immediate Actions and Future Possibilities, P3, DOI [10.17226/10640, DOI 10.17226/10640]

[30] Using machine learning techniques for occupancy-prediction-based cooling control in office buildings [J].

Peng, Yuzhen ;

Rysanek, Adam ;

Nagy, Zoltan ;

Schluter, Arno .

APPLIED ENERGY, 2018, 211 :1343-1358

← 1 2 3 4 →