# Validation of Automated Somatotype Estimation Proposal Using Full-Body 3D Scanning

## TL;DR

This paper shows that 3D body scanning can accurately estimate body composition compared to traditional methods, with minor issues in one component.

## Contribution

The study validates a new automated method for somatotype estimation using full-body 3D scanning.

## Key findings

- 3D scanning showed 87.18% agreement with manual somatotyping measurements.
- Mesomorphic and ectomorphic components had minimal differences compared to traditional methods.
- Endomorphy was slightly overestimated by 3D scanning but overall accuracy was high.

## Abstract

Somatotyping is essential for assessing body composition in sports science, anthropology, and medicine. Traditional methods, such as the Heath–Carter approach, rely on manual measurements, which can be prone to errors and variability. This study evaluates the validity and reliability of 3D body scanning as an alternative to manual somatotyping. A total of 117 participants (49 males, 68 females) aged 18 to 27 years were assessed using both traditional anthropometric methods and a full-body 3D scanning system (TC2 NX-16). The three somatotype components (ectomorphy, mesomorphy, and endomorphy) were calculated using the Heath–Carter method. A custom-developed application processed the scanned data to compute somatotype values. The results were compared using statistical metrics, including intraclass correlation coefficients (ICCs) and Bland–Altman analysis. The 3D scanning method showed high agreement (87.18%) with manual measurements. Minor discrepancies were observed particularly in the endomorphic component, which was slightly overestimated by 3D scanning. Mesomorphic and ectomorphic components exhibited minimal differences. Statistical analyses confirmed strong reliability with ICC values exceeding 0.87. Conclusions: Full-body 3D scanning is a viable, non-invasive, and efficient alternative to traditional somatotyping methods. Despite minor differences in endomorphy estimation, the overall accuracy and reliability supports its use in sports science, health monitoring, and anthropometric research. Future studies should refine predictive models for endomorphy estimation and integrate AI-driven classification techniques to enhance precision.