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Vision-Based Contactless Pose Estimation for Human Thermal Discomfort

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College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
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School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, 10044 Stockholm, Sweden
3
School of Building Services Science and Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
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Department of Applied Physics and Electronics, Umeå University, 90187 Umeå, Sweden
*
Author to whom correspondence should be addressed.
Atmosphere 2020, 11(4), 376; https://doi.org/10.3390/atmos11040376
Received: 13 February 2020 / Revised: 8 April 2020 / Accepted: 8 April 2020 / Published: 12 April 2020
(This article belongs to the Section Biometeorology)
Real-time and effective human thermal discomfort detection plays a critical role in achieving energy efficient control of human centered intelligent buildings because estimation results can provide effective feedback signals to heating, ventilation and air conditioning (HVAC) systems. How to detect occupant thermal discomfort is a challenge. Unfortunately, contact or semi-contact perception methods are inconvenient in practical application. From the contactless perspective, a kind of vision-based contactless human discomfort pose estimation method was proposed in this paper. Firstly, human pose data were captured from a vision-based sensor, and corresponding human skeleton information was extracted. Five thermal discomfort-related human poses were analyzed, and corresponding algorithms were constructed. To verify the effectiveness of the algorithms, 16 subjects were invited for physiological experiments. The validation results show that the proposed algorithms can recognize the five human poses of thermal discomfort. View Full-Text
Keywords: thermal discomfort; machine learning; computer vision; human centered intelligent buildings thermal discomfort; machine learning; computer vision; human centered intelligent buildings
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MDPI and ACS Style

Qian, J.; Cheng, X.; Yang, B.; Li, Z.; Ren, J.; Olofsson, T.; Li, H. Vision-Based Contactless Pose Estimation for Human Thermal Discomfort. Atmosphere 2020, 11, 376. https://doi.org/10.3390/atmos11040376

AMA Style

Qian J, Cheng X, Yang B, Li Z, Ren J, Olofsson T, Li H. Vision-Based Contactless Pose Estimation for Human Thermal Discomfort. Atmosphere. 2020; 11(4):376. https://doi.org/10.3390/atmos11040376

Chicago/Turabian Style

Qian, Junpeng, Xiaogang Cheng, Bin Yang, Zhe Li, Junchi Ren, Thomas Olofsson, and Haibo Li. 2020. "Vision-Based Contactless Pose Estimation for Human Thermal Discomfort" Atmosphere 11, no. 4: 376. https://doi.org/10.3390/atmos11040376

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