New Advances in Body Composition Assessment with ShapedNet: A Single
  Image Deep Regression Approach
Navar Medeiros M. Nascimento Pedro Cavalcante de Sousa Junior Pedro Yuri Rodrigues Nunes Suane Pires Pinheiro da Silva Luiz Lannes Loureiro Victor Zaban Bittencourt Valden Luis Matos Capistrano Junior Pedro Pedrosa Rebouças Filho
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.