New Advances in Body Composition Assessment with ShapedNet: A Single Image Deep Regression Approach
This work addresses body fat estimation for health monitoring, offering a non-invasive alternative to gold-standard methods like DXA, but it is incremental as it builds on existing deep learning and multi-task learning approaches.
The researchers tackled body composition assessment by developing ShapedNet, a deep neural network that estimates Body Fat Percentage (BFP) from a single photograph, achieving a Mean Absolute Percentage Error (MAPE) of 4.91% and outperforming state-of-the-art computer vision methods by 19.5%.
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.