# AI Somatotype System Using 3D Body Images:
## 4 Discussion  
The issues that can be discussed based on the results of this study are as follows: first, the collected data were divided into training, validation, and testing sets. Approximately 10% of the 217 individuals (21 individuals) were used as test data. When dividing the test set, we considered the possibility that new data could be easily predicted for certain body types and sampled approximately 10% of each of the three body types. The remaining 90% of subjects (196 individuals) were used for training and validation, with 78 for endomorphy, 97 for mesomorphy, and 21 for ectomorphy, resulting in an imbalance in data distribution. Previous studies, such as that of [PERSON] et al. (2018), mentioned the difficulty of ensuring an even distribution of the three body types as a limitation of their research. Similarly, in this study, some participants were opposed to exposing their bodies to a measurer for body measurements, leading to difficulties in conducting the measurements. [PERSON] et al. (2018) mentioned the difficulty in recruiting ectomorphic participants, leading to an overprediction of mesomorphy. In this study, while recruiting for endomorphy and mesomorphy was relatively easy, there was a significant shortage of ectomorphy data. However, researchers have made efforts to equalize the data distribution through image augmentation, and various methods have been attempted to prevent overfitting of the augmented images during the model training process. It is necessary to obtain additional data in the future to address the limitations of data distribution. Furthermore, it is necessary to collect sufficient data from females to develop a sex-agnostic AI somatotype model. Second, MobileNetV2 was utilized as the transfer learning model. The rationale for selecting this model was based on preliminary tests conducted on various models commonly used in transfer learning, including MobileNetV2, VGG16, and ResNet50, which were found to be useful in previous studies. The model that exhibited the best performance was selected for this study. Although various other models are available for transfer learning, the researchers aim to develop the ultimate goal of a mobile AI somatotype model through subsequent research. Therefore, it was deemed appropriate to conduct preliminary tests using simpler structured models compared to other models. Ultimately, the MobileNetV2 model, which operates efficiently even on devices with limited computational capabilities, demonstrated good performance in predicting body images. Therefore, it is necessary to explore and compare lightweight models, such as MobileNetV2, for future model scalability. This would involve research methods to enhance the performance by investigating and comparing lightweight models similar to MobileNetV2. Third, model training resulted in approximately 91% accuracy for the training set and approximately 72% accuracy for the validation set. The corresponding loss values \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Predicted**} & \multicolumn{3}{c}{**Accuracy of the System’s Classification**} \\ \cline{3-6}  & **Endomorphy** & **Mesomorphy** & **Ectomorphy** & **C**irectomy** & **C**irectomy** \\ \hline \multirow{3}{*}{Actual} & Endomorphy & 5 & 1 & 1 & Accuracy & 0.85 \\ \cline{2-6}  & Mesomorphy & 1 & 11 & 0 & Recall & 0.87 \\ \cline{2-6}  & Ectomorphy & 0 & 0 & 2 & Precision & 0.80 \\ \hline \multicolumn{6}{l}{Accuracy calculation \(:\) 18(Positive)/21(Test Data) \(=\) 0.85} \\ \hline \multicolumn{6}{l}{Recall calculation} \\ \multicolumn{6}{l}{\(:\) Endomorphy Recall \(=\) 5/6, Mesomorphy Recall \(=\) 11/12, Ectomorphy Recall \(=\) 2/2} \\ \multicolumn{6}{l}{\(=\) ((5/6) + (11/12) + (2/2))/3 \(=\) 0.87} \\ \hline \multicolumn{6}{l}{Precision calculation} \\ \multicolumn{6}{l}{\(:\) Endomorphy Precision \(=\) 5/6, Mesomorphy Precision \(=\) 11/12, Ectomorphy Precision \(=\) 2/3} \\ \multicolumn{6}{l}{\(=\) ((5/6) + (11/12) + (2/3))/3 \(=\) 0.80} \\ \end{tabular}
\end{table} 
Table 7: AI somatotype model test result.