Vision-Based Contactless Pose Estimation for Human Thermal Discomfort
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Atmosphere |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4433/11/4/376 |
| _version_ | 1797570865716527104 |
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| author | Junpeng Qian Xiaogang Cheng Bin Yang Zhe Li Junchi Ren Thomas Olofsson Haibo Li |
| author_facet | Junpeng Qian Xiaogang Cheng Bin Yang Zhe Li Junchi Ren Thomas Olofsson Haibo Li |
| author_sort | Junpeng Qian |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T20:31:31Z |
| format | Article |
| id | doaj.art-f182fdecbefc4f2cb28194119eafe0c5 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-10T20:31:31Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-f182fdecbefc4f2cb28194119eafe0c52023-11-19T21:24:07ZengMDPI AGAtmosphere2073-44332020-04-0111437610.3390/atmos11040376Vision-Based Contactless Pose Estimation for Human Thermal DiscomfortJunpeng Qian0Xiaogang Cheng1Bin Yang2Zhe Li3Junchi Ren4Thomas Olofsson5Haibo Li6College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaCollege of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaSchool of Building Services Science and Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, ChinaSchool of Building Services Science and Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, ChinaCollege of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaDepartment of Applied Physics and Electronics, Umeå University, 90187 Umeå, SwedenCollege of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaReal-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.https://www.mdpi.com/2073-4433/11/4/376thermal discomfortmachine learningcomputer visionhuman centered intelligent buildings |
| spellingShingle | Junpeng Qian Xiaogang Cheng Bin Yang Zhe Li Junchi Ren Thomas Olofsson Haibo Li Vision-Based Contactless Pose Estimation for Human Thermal Discomfort Atmosphere thermal discomfort machine learning computer vision human centered intelligent buildings |
| title | Vision-Based Contactless Pose Estimation for Human Thermal Discomfort |
| title_full | Vision-Based Contactless Pose Estimation for Human Thermal Discomfort |
| title_fullStr | Vision-Based Contactless Pose Estimation for Human Thermal Discomfort |
| title_full_unstemmed | Vision-Based Contactless Pose Estimation for Human Thermal Discomfort |
| title_short | Vision-Based Contactless Pose Estimation for Human Thermal Discomfort |
| title_sort | vision based contactless pose estimation for human thermal discomfort |
| topic | thermal discomfort machine learning computer vision human centered intelligent buildings |
| url | https://www.mdpi.com/2073-4433/11/4/376 |
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