New Advances in Body Composition Assessment with ShapedNet: A Single Image Deep Regression Approach

Autor:	Nascimento, Navar Medeiros M., Junior, Pedro Cavalcante de Sousa, Nunes, Pedro Yuri Rodrigues, da Silva, Suane Pires Pinheiro, Loureiro, Luiz Lannes, Bittencourt, Victor Zaban, Junior, Valden Luis Matos Capistrano, Filho, Pedro Pedrosa Rebouças
Rok vydání:	2023
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Preprinted version in October 2023. The paper is under consideration at Pattern Recognition Letters
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2310.09709 Zobrazit plný text záznamu View this record from Arxiv