Smartphone three-dimensional imaging for body composition assessment using non-rigid avatar reconstruction
Modern digital anthropometry applications utilize smartphone cameras to rapidly construct three-dimensional humanoid avatars, quantify relevant anthropometric variables, and estimate body composition. In the present study, 131 participants ([73 M, 58 F] age 33.7 ± 16.0 y; BMI 27.3 ± 5.9 kg/m , body...
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Published in | Frontiers in medicine Vol. 11; p. 1485450 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
Published |
Switzerland
Frontiers Media S.A
07.10.2024
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Subjects | |
Online Access | Get full text |
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Summary: | Modern digital anthropometry applications utilize smartphone cameras to rapidly construct three-dimensional humanoid avatars, quantify relevant anthropometric variables, and estimate body composition.
In the present study, 131 participants ([73 M, 58 F] age 33.7 ± 16.0 y; BMI 27.3 ± 5.9 kg/m
, 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.
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
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.
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. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Reviewed by: Velyn Wu, University of Florida, United States Edited by: Arch Mainous, University of Florida, United States Pooja Sharma, University of Florida, United States |
ISSN: | 2296-858X 2296-858X |
DOI: | 10.3389/fmed.2024.1485450 |