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
收藏
页码：340 / 352
页数：13
相关论文
共 40 条
[11]   Machine learning method for real-time non-invasive prediction of individual thermal preference in transient conditions [J].
Cosma, Andrei Claudiu ;
Simha, Rahul .
BUILDING AND ENVIRONMENT, 2019, 148 :372-383
[12]   Machine learning approaches to predict thermal demands using skin temperatures: Steady-state conditions [J].
Dai, Changzhi ;
Zhang, Hui ;
Arens, Edward ;
Lian, Zhiwei .
BUILDING AND ENVIRONMENT, 2017, 114 :1-10
[13]   A personalized measure of thermal comfort for building controls [J].
Daum, David ;
Haldi, Frederic ;
Morel, Nicolas .
BUILDING AND ENVIRONMENT, 2011, 46 (01) :3-11
[14]  
Elgharib MA, 2015, PROC CVPR IEEE, P4119, DOI 10.1109/CVPR.2015.7299039
[15]  
Fanger PO, 1972, THERMAL COMFORT ANAL
[16]  
Farhan AA, 2015, IEEE INT CON AUTO SC, P708, DOI 10.1109/CoASE.2015.7294164
[17]   Towards unsupervised learning of thermal comfort using infrared thermography [J].
Ghahramani, Ali ;
Castro, Guillermo ;
Karvigh, Simin Ahmadi ;
Becerik-Gerber, Burcin .
APPLIED ENERGY, 2018, 211 :41-49
[18]   Infrared thermography of human face for monitoring thermoregulation performance and estimating personal thermal comfort [J].
Ghahramani, Ali ;
Castro, Guillermo ;
Becerik-Gerber, Burcin ;
Yu, Xinran .
BUILDING AND ENVIRONMENT, 2016, 109 :1-11
[19]   Energy savings from temperature setpoints and deadband: Quantifying the influence of building and system properties on savings [J].
Ghahramani, Ali ;
Zhang, Kenan ;
Dutta, Kanu ;
Yang, Zheng ;
Becerik-Gerber, Burcin .
APPLIED ENERGY, 2016, 165 :930-942
[20]  
Ghahramani A, 2015, WINT SIMUL C PROC, P1000, DOI 10.1109/WSC.2015.7408228
1234