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

Author

Listed:
  • Eun Ji Choi

    (School of Architecture and Building Science, Chung-Ang University, Seoul 06974, Korea
    These authors contributed equally to this work as co-first author.)

  • Jin Woo Moon

    (School of Architecture and Building Science, Chung-Ang University, Seoul 06974, Korea
    These authors contributed equally to this work as co-first author.)

  • Ji-hoon Han

    (Department of Biomedical Engineering, Sungkyunkwan University, Suwon 16419, Korea)

  • Yongseok Yoo

    (Department of Electronics Engineering, Incheon National University, Incheon 22012, Korea)

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.

Suggested Citation

  • Eun Ji Choi & Jin Woo Moon & Ji-hoon Han & Yongseok Yoo, 2021. "Development of a Deep Neural Network Model for Estimating Joint Location of Occupant Indoor Activities for Providing Thermal Comfort," Energies, MDPI, vol. 14(3), pages 1-14, January.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:3:p:696-:d:489594
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    References listed on IDEAS

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    1. Bo Rang Park & Eun Ji Choi & Young Jae Choi & Jin Woo Moon, 2020. "Accuracy Analysis of DNN-Based Pose-Categorization Model and Activity-Decision Algorithm," Energies, MDPI, vol. 13(4), pages 1-14, February.
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    Cited by:

    1. Wang, Junqi & Jiang, Lanfei & Yu, Hanhui & Feng, Zhuangbo & Castaño-Rosa, Raúl & Cao, Shi-jie, 2024. "Computer vision to advance the sensing and control of built environment towards occupant-centric sustainable development: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).

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