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

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