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Accuracy Analysis of DNN-Based Pose-Categorization Model and Activity-Decision Algorithm

Author

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  • Bo Rang Park

    (School of Architecture and Building Science, Chung-Ang University, Seoul 06974, Korea)

  • Eun Ji Choi

    (School of Architecture and Building Science, Chung-Ang University, Seoul 06974, Korea)

  • Young Jae 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)

Abstract

The objective of this study is to develop (1) a pose-categorization model that classifies the poses of an occupant based on their image in an indoor space and (2) an activity-decision algorithm that identifies the activity being performed by the occupant. For developing an automated intelligent model, a deep neural network is adopted. The model considers the coordinates of the joints of the occupant in the image as input data and returns the pose of the occupant. Datasets composed of indoor images of home and office environments are used for training and testing the model. The training and testing accuracies of the optimized model were 100% for both the home and office environments. A representative activity of an occupant for a certain period has to be decided to control an indoor environment for comfort. The activity-decision algorithm employs a frequency-based method to determine the representative activity type for real-time occupant poses using the pose-categorization model. This study highlights the potential of the developed model and algorithm to determine the activity of occupants to provide an optimal thermal environment corresponding to the individual’s metabolic rate.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:4:p:839-:d:320779
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    References listed on IDEAS

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    1. Fan, Cheng & Xiao, Fu & Zhao, Yang, 2017. "A short-term building cooling load prediction method using deep learning algorithms," Applied Energy, Elsevier, vol. 195(C), pages 222-233.
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    Cited by:

    1. 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.

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