用照片和身高体重数据，AI能低成本估算体脂率。
Multimodal AI for Body Fat Estimation: Computer Vision and Anthropometry with DEXA Benchmarks
- 结合图像与人体测量数据，训练多模态AI模型。
- 图像模型误差仅4.44%，相关性达0.807，接近金标准。
- 为普通人健康监测提供可落地的体脂估算方案。
体脂率监测对有效管理体重至关重要，但金标准方法如DEXA扫描成本高、难以普及。本研究评估了人工智能模型作为低成本替代方案的可行性，使用正面身体图像和基础人体测量数据（体重、身高、颈围、踝围、腕围）。数据集包含535个样本：253例有记录的人体测量值，282张来自Reddit帖子的网络爬取图像，部分用户自报体脂率并标注为DEXA结果。因无公开的基于计算机视觉的体脂数据集，本研究专门构建该数据集。开发了两种方法：(1) 基于ResNet的图像模型，(2) 使用人体测量数据的回归模型。还提出了未来可扩展的多模态融合框架。图像模型取得4.44%的均方根误差（RMSE）和0.807的决定系数（R²），表明AI辅助模型可实现可及且低成本的体脂估计，支持未来在健康与健身领域的消费应用。
原文摘要 · Abstract (English)
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.
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