Development of a Deep Neural Network Model for Estimating Joint Location of Occupant Indoor Activities for Providing Thermal Comfort

Eun Ji Choi, Jin Woo Moon, Ji-hoon Han and Yongseok Yoo
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Eun Ji Choi: School of Architecture and Building Science, Chung-Ang University, Seoul 06974, Korea
Jin Woo Moon: School of Architecture and Building Science, Chung-Ang University, Seoul 06974, Korea
Ji-hoon Han: Department of Biomedical Engineering, Sungkyunkwan University, Suwon 16419, Korea
Yongseok Yoo: Department of Electronics Engineering, Incheon National University, Incheon 22012, Korea

Energies, 2021, vol. 14, issue 3, 1-14

Abstract: The type of occupant activities is a significantly important factor to determine indoor thermal comfort; thus, an accurate method to estimate occupant activity needs to be developed. The purpose of this study was to develop a deep neural network (DNN) model for estimating the joint location of diverse human activities, which will be used to provide a comfortable thermal environment. The DNN model was trained with images to estimate 14 joints of a person performing 10 common indoor activities. The DNN contained numerous shortcut connections for efficient training and had two stages of sequential and parallel layers for accurate joint localization. Estimation accuracy was quantified using the mean squared error (MSE) for the estimated joints and the percentage of correct parts (PCP) for the body parts. The results show that the joint MSEs for the head and neck were lowest, and the PCP was highest for the torso. The PCP for individual activities ranged from 0.71 to 0.92, while typing and standing in a relaxed manner were the activities with the highest PCP. Estimation accuracy was higher for relatively still activities and lower for activities involving wide-ranging arm or leg motion. This study thus highlights the potential for the accurate estimation of occupant indoor activities by proposing a novel DNN model. This approach holds significant promise for finding the actual type of occupant activities and for use in target indoor applications related to thermal comfort in buildings.

Keywords: thermal comfort; deep neural network; human joint estimation; indoor activity (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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