Contactless body measurement technologies are becoming increasingly significant for smart health monitoring, digital health applications, and remote patient assessment. Traditional anthropometric measurements typically necessitate physical contact and trained personnel, which may constrain scalability in remote healthcare settings. In this study, we introduce a depth camera-based framework for estimating human body measurements utilizing 3D point cloud data. An Orbbec Astra 2 depth camera was employed to capture RGB images, depth maps, and 3D point clouds of participants. The captured point cloud was processed using Python-based tools, including Open3D, NumPy, and OpenCV, to segment the human body from the background. Key anthropometric measurements, such as height and arm span, were computed. The measurements were obtained through a combination of spatial filtering and landmark selection on the 3D point cloud, followed by the projection of the computed measurements onto the corresponding RGB image using camera intrinsic parameters. In addition to linear measurements, the approximate body volume and visible surface area were estimated using voxel-based occupancy analysis and mesh-based surface reconstruction methods. The experimental results from a single depth capture demonstrated that accurate body measurements and geometric estimates could be obtained from depth camera data without physical contact. This study provides a foundation for future real-time systems that integrate depth sensing with intelligent health monitoring and generative AI models for smart healthcare applications.

翻译：非接触式人体测量技术对于智能健康监测、数字健康应用及远程患者评估日益重要。传统人体测量方法通常需要物理接触和专业操作人员，这可能在远程医疗环境中限制其可扩展性。本研究提出了一种基于深度相机的框架，利用三维点云数据估计人体测量指标。采用Orbbec Astra 2深度相机采集参与者的RGB图像、深度图和三维点云。通过基于Python的工具（包括Open3D、NumPy和OpenCV）处理所获取的点云，实现人体与背景的分割。计算了身高、臂展等关键人体测量参数。测量值的获取结合了三维点云的空间滤波与特征点选择，并通过相机内参将计算得到的测量值投影至对应RGB图像。除线性测量外，还采用基于体素的占有率分析和基于网格的表面重建方法估算了近似体体积和可见表面积。单次深度捕获的实验结果表明，无需物理接触即可从深度相机数据中获得精准的人体测量值与几何估计值。本研究为未来集成深度感知技术、智能健康监测及生成式AI模型的实时系统应用于智慧医疗奠定了基础。