Multimodal AI for Body Fat Estimation: Computer Vision and Anthropometry with DEXA Benchmarks

TL;DR

This study explores AI models using images and anthropometric data to estimate body fat percentage, aiming to provide accessible alternatives to expensive DEXA scans, with promising initial results.

Contribution

It introduces a novel dataset and evaluates AI models for body fat estimation using computer vision and anthropometry, paving the way for low-cost health monitoring tools.

Findings

Image model achieved RMSE of 4.44%

Model explained 80.7% of variance in fat percentage

Demonstrates potential for accessible body composition assessment

Abstract

Tracking body fat percentage is essential for effective weight management, yet gold-standard methods such as DEXA scans remain expensive and inaccessible for most people. This study evaluates the feasibility of artificial intelligence (AI) models as low-cost alternatives using frontal body images and basic anthropometric data. The dataset consists of 535 samples: 253 cases with recorded anthropometric measurements (weight, height, neck, ankle, and wrist) and 282 images obtained via web scraping from Reddit posts with self-reported body fat percentages, including some reported as DEXA-derived by the original posters. Because no public datasets exist for computer-vision-based body fat estimation, this dataset was compiled specifically for this study. Two approaches were developed: (1) ResNet-based image models and (2) regression models using anthropometric measurements. A multimodal fusion framework is also outlined for future expansion once paired datasets become available. The image-based model achieved a Root Mean Square Error (RMSE) of 4.44% and a Coefficient of Determination (R^2) of 0.807. These findings demonstrate that AI-assisted models can offer accessible and low-cost body fat estimates, supporting future consumer applications in health and fitness.