Non-invasive detection algorithm of thermal comfort based on computer vision

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Xiaogang, Cheng Zhang, Lichang 2018-09-03T12:29:31Z 2018-09-03T12:29:31Z 2018-08-20
dc.description.abstract The waste of building energy consumption is a major challenge in the world. Real-time detection of human thermal comfort is an effective way to deal with this issue. However, due to the difference of personal thermal comfort and changes caused by climatic variations, there is still a long way to reach this target. From another perspective, the current HVAC (heating, ventilating and air-conditioning) systems are reluctant to provide flexible interaction channels to adjust atmosphere which fails to follow continuously increasing requirements from users. All of them indicate the necessity to develop more intelligent detection method for human thermal comfort. In this paper, a non-invasion detection method toward thermal comfort is proposed from two perspectives: macro human postures and skin textures. In posture part, OpenPose is used for detecting the key points’ position coordinates of human body in images, which would be functionalized from the term of thermal comfort. In skin textures, deep neural network is used to regress the images of skin to its temperature. Based on Fanger’s theory of thermal comfort, the results of both parts are satisfying: subjects’ postures can be captured and interpreted into different thermal comfort level: hot, cold and comfort. And the absolute error of prediction from neurons network is less than 0.125 degrees centigrade which is the equipment error of thermometer used in data acquisition. With solutions of this paper, it is promising to non-invasively detect the thermal comfort level of users from postures and skin textures. And the conclusion and future work are discussed in final chapter. en
dc.format.extent 26 + 0
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.title Non-invasive detection algorithm of thermal comfort based on computer vision en
dc.type G2 Pro gradu, diplomityö fi Perustieteiden korkeakoulu fi
dc.subject.keyword non-invasive en
dc.subject.keyword deep learning en
dc.subject.keyword OpenPose en
dc.subject.keyword computer vision en
dc.subject.keyword human thermal comfort en
dc.identifier.urn URN:NBN:fi:aalto-201809034769
dc.programme.major ICT Innovation - EIT Digital Master School fi
dc.programme.mcode SCI3020 fi
dc.type.ontasot Master's thesis en
dc.type.ontasot Diplomityö fi
dc.contributor.supervisor David, McGookin
dc.programme Master's Programme in ICT Innovation fi
local.aalto.electroniconly yes
local.aalto.openaccess yes

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