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Updated: Sep 13, 2025

Author Spotlight: Advancements in 3D Optical Imaging for Comprehensive Body Composition Assessment in Modern Research
Published on: June 7, 2024
Validation of Automated Somatotype Estimation Proposal Using Full-Body 3D Scanning
Bibiána Ondrejová1, Lucia Bednarčíková1, Norbert Ferenčík1
1Department of Biomedical Engineering and Measurement, Faculty of Mechanical Engineering, Technical University of Košice, 042 00 Košice, Slovakia.
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.

