Estimating Body Volume and Height Using 3D Data

TL;DR

This paper introduces a non-invasive 3D imaging method using depth maps and convex hull algorithms to accurately estimate body weight and height, aiding emergency medical procedures when direct measurement isn't feasible.

Contribution

It presents a novel approach combining 3D imaging and segmentation techniques to improve body weight estimation accuracy in urgent medical contexts.

Findings

High-resolution 3D models enable precise volume and height calculation.

Segmented volume summation improves estimation accuracy.

Method enhances patient safety in emergency scenarios.

Abstract

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.