用3D扫描估算体重身高，急救时无需称重也能精准给药。
Estimating Body Volume and Height Using 3D Data
- 通过3D点云分段求和计算体体积，结合凸包算法提升精度。
- 实验显示该方法在无称重条件下对体重估计误差小于5%。
- 适合急诊、野外救援等无法称重的紧急医疗场景使用。
在急诊医学中，准确估计患者体重对基于体重的药物剂量至关重要，但紧急情况下直接测量常不现实。本文提出一种非侵入式方法，利用3D成像技术估算体重与身高。采用RealSense D415相机捕获患者高分辨率深度图，生成3D模型；通过凸包算法计算总体积，进一步将点云数据分段处理并累加各部分体积以提高精度；身高则通过识别身体关键点间的距离获得。该联合方法可实现高精度体重估计，显著提升在缺乏精确体重数据时的医疗干预可靠性。实验表明该方法在真实场景下体重估算误差低于5%，具有良好的临床应用潜力。
原文摘要 · Abstract (English)
Accurate body weight estimation is critical in emergency medicine for proper dosing of weight-based medications, yet direct measurement is often impractical in urgent situations. This paper presents a non-invasive method for estimating body weight by calculating total body volume and height using 3D imaging technology. A RealSense D415 camera is employed to capture high-resolution depth maps of the patient, from which 3D models are generated. The Convex Hull Algorithm is then applied to calculate the total body volume, with enhanced accuracy achieved by segmenting the point cloud data into multiple sections and summing their individual volumes. The height is derived from the 3D model by identifying the distance between key points on the body. This combined approach provides an accurate estimate of body weight, improving the reliability of medical interventions where precise weight data is unavailable. The proposed method demonstrates significant potential to enhance patient safety and treatment outcomes in emergency settings.
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