Development of occupant pose classification model using deep neural network for personalized thermal conditioning

Eun Ji Choi, Yongseok Yoo, Bo Rang Park, Young Jae Choi, Jin Woo Moon

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

This study aims to propose a pose classification model using indoor occupant images. For developing the intelligent and automated model, a deep learning neural network was employed. Indoor posture images and joint coordinate data were collected and used to conduct the training and optimization of the model. The output of the trained model is the occupant pose of the sedentary activities in the indoor space. The performance of the developed model was evaluated for two different indoor environments: Home and offce. Using the metabolic rates corresponding to the classified poses, the model accuracy was compared with that of the conventional method, which considered the fixed activity. The result showed that the accuracy was improved by as much as 73.96% and 55.26% in home and offce, respectively. Thus, the potential of the pose classification model was verified for providing a more comfortable and personalized thermal environment to the occupant.

Original languageEnglish
Article number45
JournalEnergies
Volume13
Issue number1
DOIs
StatePublished - Dec 20 2019
Externally publishedYes

Funding

Funding: This research was supported by the energy efficiency technology program of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20182010600010) and by the Chung-Ang University Graduate Research Scholarship in 2019.

Keywords

  • Deep neural network
  • Pose classification
  • Thermal environment

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