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

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.

Research on the International Trade Performance Evaluation of Cross-Border e-Commerce Based on the Deep Neural Network Model

“…Deep neural network evaluation results are precise. Compared with other methods, the deep neural network system has the following advantages: (1) The structure of the neural network can be determined by self-study in the laboratory participating in the comparison, and it is repeated according to the optimal training requirements, and the structure of the neural network is continuously improved until it achieves a stable relative condition; therefore, using this method eliminates many human factors and is suitable for the purpose of verifying economic results. (2) The error is small, and the systematic error can meet any correct standard.…”

“…The model uses image data for analysis to assess the development prospects of common types of cross-border e-commerce. In addition, the model features efficient learning, two-stage matching, and multiple quick links of parallel levels to accurately locate the judging criteria [1]. Every once in a while, researchers make model improve-ments to deep learning models.…”

Utilization of the Evaluation System for Spatial Comfort toward Multi-Layered Public Hanok Facilities

“…A study performed by Choi et al reached the conclusion that "the main factors composing the indoor environment quality (IEQ) are classified into thermal comfort, indoor air quality (IAQ), and visual comfort, and among these factors, the degree of thermal comfort is decided by predicted mean vote (PMV), one of the thermal comfort indices". However, they insisted that the individual factors like CLO and MET are difficult to measure objectively and accurately due to the real-time rate of change in the heat production of the human body, while physical factors can be measured simply with sensors [18].…”

“…the individual factors like CLO and MET are difficult to measure objectively and accurately due to the real-time rate of change in the heat production of the human body, while physical factors can be measured simply with sensors [18].…”