用毫米波雷达扫描人体，无需脱衣即可估算体脂和内脏脂肪。
Non-intrusive Body Composition Assessment from Full-body mmWave Scans
- 通过合成毫米波点云与多任务学习，从雷达数据推算身体成分。
- 预测内脏脂肪和体脂率误差分别为1.0升和3.2%。
- 适合日常健康监测，隐私保护且无辐射，适用于普通人群。
体成分评估（BCA）可提供体内不同组织分布的详细信息，比体重或BMI更具个性化参考价值。然而，当前金标准方法如CT和MRI仅适用于有临床指征的患者，难以用于大众日常监测。本文探索一种尚未应用于医疗领域的成像技术——毫米波雷达。该技术常用于安检，能快速、非侵入式且隐私保护地重建全身轮廓，无需脱衣。为验证毫米波扫描实现快速便捷BCA的可行性，我们提出一种基于多任务学习的回归方法，利用临床影像与参数化人体模型生成的合成毫米波点云进行训练。在包含真实毫米波扫描的试点队列上评估，结合生物阻抗测量的体脂数据，结果表明可在站立姿势下通过穿衣服的毫米波扫描准确估计内脏脂肪（VAT）和体脂百分比（BFP）。模型预测误差分别为1.0升和3.2%，证明毫米波扫描在多种场景中实现常规体成分评估的潜力。
原文摘要 · Abstract (English)
Body composition assessment (BCA) provides detailed information about the distribution of different tissue types in the body, enabling more precise characterization of individuals than BMI or weight alone. Consistent and frequent BCA would be valuable for personalized medicine, but the gold standard methods for BCA, such as CT and MRI, are only practical for opportunistic monitoring of patients with clinical indications for imaging and are not suitable for routine use in the general population. Here, we consider an imaging modality which is not currently used in medical applications: millimeter wave (mmWave) radar. Commonly used in security settings, mmWave scans enable fast, non-intrusive, and privacy-preserving reconstruction of full body shape without the need to remove clothing. To demonstrate the feasibility of fast and convenient BCA from mmWave scans, we present a method for BCA value regression using a multi-task learning strategy that leverages synthetic mmWave-like point clouds derived from clinical imaging and parametric human models. We evaluate the model on a pilot cohort of real mmWave scans with bioimpedance-derived body fat measurements, supporting the feasibility of estimating VAT and body fat percentage (BFP) from mmWave data acquired through clothing in a standing posture. We find that the model can predict VAT and BFP with a mean absolute error of 1.0 L and 3.2%, respectively, demonstrating the potential of mmWave scanning for routine BCA in a wide range of settings.
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