Smartphone three-dimensional imaging for body composition assessment using non-rigid avatar reconstruction
Document Type
Article
Publication Date
10-7-2024
Publication Title
Frontiers in Medicine
Abstract
Background: Modern digital anthropometry applications utilize smartphone cameras to rapidly construct three-dimensional humanoid avatars, quantify relevant anthropometric variables, and estimate body composition. Methods: In the present study, 131 participants ([73 M, 58 F] age 33.7 ± 16.0 y; BMI 27.3 ± 5.9 kg/m2, body fat 29.9 ± 9.9%) had their body composition assessed using dual-energy X-ray absorptiometry (DXA) and a smartphone 3D scanning application using non-rigid avatar reconstruction. The performance of two new body fat % estimation equations was evaluated through reliability and validity statistics, Bland-Altman analysis, and equivalence testing. Results: In the reliability analysis, the technical error of the measurement and intraclass correlation coefficient were 0.5-0.7% and 0.996-0.997, respectively. Both estimation equations demonstrated statistical equivalence with DXA based on ±2% equivalence regions and strong linear relationships (Pearson's r 0.90; concordance correlation coefficient 0.89-0.90). Across equations, mean absolute error and standard error of the estimate values were ~ 3.5% and ~ 4.2%, respectively. No proportional bias was observed. Conclusion: While continual advances are likely, smartphone-based 3D scanning may now be suitable for implementation for rapid and accessible body measurement in a variety of applications.
First Page
1
Last Page
7
DOI
10.3389/fmed.2024.1485450
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Tinsley, Grant M.; Rodriguez, Christian; Florez, Christine M.; Siedler, Madelin R.; Tinoco, Ethan; McCarthy, Cassidy; and Heymsfield, Steven B., "Smartphone three-dimensional imaging for body composition assessment using non-rigid avatar reconstruction" (2024). LSU-LCMC Cancer Center Faculty Publications. 183.
https://digitalscholar.lsuhsc.edu/llcc_facpubs/183
10.3389/fmed.2024.1485450