## 5 Conclusions  
This study proposes a human body simulation that can predict body changes induced by selected exercise plans for disease prevention, and even aesthetic purposes. The proposed simulation is based on a body measurement technique using a 3D body model modeled from 2D images, which has the advantage of being time- and cost-effective, compared to conventional expensive 3D scanners and time-consuming direct measurement approaches. Previous studies have attempted to reconstruct three-dimensional images from two-dimensional images. However, this study is notable for its approach of measuring each part of the body through a three-dimensional model created for the purpose of visualizing three-dimensional changes in the body according to an exercise plan. This is achieved by aggregating existing studies to create an exercise effect database. Experiments conducted on subjects with various demographic characteristics to verify the validity of the proposed simulation demonstrate that the proposed simulation is sufficiently accurate and versatile. The paired \(t\)-test result between constructed images and actual anthropometric measurement was 0.3483 in terms of the \(p\)-value. In addition, considering that the proposed simulator can predict body changes according to various exercise plans, it is expected to be widely used in the recently growing field of preventive treatment and personalized healthcare.  
This study has the possibility of expansion through future studies in body change prediction. First, the prediction of body shape change through the proposed simulator is based on the existing body change experiment, which only considers exercise plans. However, since body shape changes are significantly affected by eating habits, through future research, the simulator is expected to provide more accurate and realistic predictions of body shape changes.  
Conceptualization, J.S., S.Y. and S.K.; methodology, J.S. and S.K.; software, J.S. and S.K.; validation, J.S. and S.K.; formal analysis, J.S. and S.K.; investigation, J.S., S.Y. and S.K.; resources, J.S., S.Y. and S.K.; writing--original draft, J.S. and S.K.; writing--review and editing, J.S., S.Y. and S.K.; visualization, J.S. and S.K.; funding acquisition, S.Y.; supervision, S.K. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metrics** & **ASPP Condition (1)** & **ASPP Condition (2)** & **ASPP Condition (3)** \\ \hline FLOPs & 8.8 G & 9.1 G & 9.8 G \\ \hline \# param. & 1.9 M & 2.0 M & 2.1 M \\ \hline IoU (\%) & 72.4\% & 72.7\% & 73.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Experimental results of semantic segmentation with ASPP module conditions: (1) ASPP module with Atrous rate of 12; (2) ASPP module with Atrous rate of 6 and 12; and (3) ASPP module with Atrous rates of 6, 12, and 18.  
**Funding:** This work was supported by a National Research Foundation of Korea (NRF) (No. 2023R1A2C2004252).  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy information of participants.  
**Conflicts of Interest:** The authors declare no conflicts of interest.