# AI Somatotype System Using 3D Body Images:
## 1 Introduction  
Humans share a similar body structure, but each individual possesses unique characteristics, which we define as one's body type [1]. Various classification methods have been devised to understand and assess these body types. Body type classification has garnered the interest of researchers in related fields to evaluate human body shape and composition [2]. One prominent method of body type classification is the Heath-Carter somatotype. Smatotyping provides a systematic and scientific approach for quantifying human body types [3] and has been widely utilized by researchers across various disciplines for extended periods [4]. However, because potential measurement errors depend on the skill of the measurer, somatotyping requires technical expertise [5]. Additionally, there are limitations to its applicability in modern individuals, as many are reluctant to expose or permit contact with sensitive areas of the body [6]. Recent research has applied artificial intelligence technology utilizing noninvasive measurement tools, such as 3D body scanners, which minimize physical contact [7, 8]. Compared with traditional methods, 3D body scanners analyze a larger amount of data in a shorter period of time, thereby increasing the efficiency of analyzing existing body measurement data (e.g., width, length, and circumference) [9]. Moreover, data in the form of various body measurements including height, circumference, cross-sectional area,and volume are collected [10]. Previous studies have shown that the use of machine learning algorithms to predict obesity based on body fat percentage [11] outperforms BMI (Body Mass Index) and BIA (Bioelectrical Impedance Analysis) in the classification of obesity. In addition, research exploring predictive models for somatotypes based on anthropometry [12] has shown excellent performance in classifying body types. Previous studies have indicated that 3D body scanners are reliable and valid measurement tools for predicting body type. Furthermore, data collected during 3D body scanner scans can capture not only body measurements but also three-dimensional (3D) body images. The aforementioned studies utilized body data, requiring complex procedures, such as setting markers for each segment of the body to extract data. Therefore, in terms of practicality, the resulting models may be less scalable to provide generalized functions. To compensate for these limitations, this study seeks to utilize 3D body images that were not used in previous studies. Two-dimensional (2D) images are already used to create three-dimensional (3D) body images, and a 3D body scanner is used to derive body shape values similar to actual human body values [13; 14]. However, it is still difficult to find research applying artificial intelligence technology related to the somatotype. The technology for generating complex 3D images from simple 2D images is predicted to have a significant impact on future scientific advancements. However, implementing systems that offer practicality to many individuals requires time and expertise from researchers. Therefore, the development of practical models by simplifying 3D body images collected using 3D body scanner into 2D images is essential. Through this study, it is anticipated that various limitations such as complex procedures and expertise can be overcome from a practical standpoint. Moreover, systems that are unaffected by time and location constraints, such as identifying body shape using a mobile phone camera, can be established. Thus, the objective of this study was to develop an artificial intelligence (AI) somatotype system capable of predicting the three body types proposed by [PERSON]'s somatotype theory using 3D body images collected using 3D body scanners. Specifically, after performing data pre-processing, such as converting the collected three-dimensional (3D) images into two-dimensional (2D) images, the model is trained and verified by segmenting the data set.