We introduce a novel technique called ShapedNet to enhance body composition assessment. This method employs a deep neural network capable of estimating Body Fat Percentage (BFP), performing individual identification, and enabling localization using a single photograph. The accuracy of ShapedNet is validated through comprehensive comparisons against the gold standard method, Dual-Energy X-ray Absorptiometry (DXA), utilizing 1273 healthy adults spanning various ages, sexes, and BFP levels. The results demonstrate that ShapedNet outperforms in 19.5% state of the art computer vision-based approaches for body fat estimation, achieving a Mean Absolute Percentage Error (MAPE) of 4.91% and Mean Absolute Error (MAE) of 1.42. The study evaluates both gender-based and Gender-neutral approaches, with the latter showcasing superior performance. The method estimates BFP with 95% confidence within an error margin of 4.01% to 5.81%. This research advances multi-task learning and body composition assessment theory through ShapedNet.
@article{arxiv.2310.09709,
title = {New Advances in Body Composition Assessment with ShapedNet: A Single Image Deep Regression Approach},
author = {Navar Medeiros M. Nascimento and Pedro Cavalcante de Sousa Junior and Pedro Yuri Rodrigues Nunes and Suane Pires Pinheiro da Silva and Luiz Lannes Loureiro and Victor Zaban Bittencourt and Valden Luis Matos Capistrano Junior and Pedro Pedrosa Rebouças Filho},
journal= {arXiv preprint arXiv:2310.09709},
year = {2023}
}
Comments
Preprinted version in October 2023. The paper is under consideration at Pattern Recognition Letters