New advances in body composition assessment with ShapedNet: A single image deep regression approach

IF 3.9 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Pattern Recognition Letters Pub Date : 2025-02-01 DOI:10.1016/j.patrec.2024.11.029

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

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Abstract

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.

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来源期刊

Pattern Recognition Letters 工程技术-计算机：人工智能

CiteScore

12.40

自引率

5.90%

发文量

287

审稿时长

9.1 months

期刊介绍： Pattern Recognition Letters aims at rapid publication of concise articles of a broad interest in pattern recognition. Subject areas include all the current fields of interest represented by the Technical Committees of the International Association of Pattern Recognition, and other developing themes involving learning and recognition.