Contactless 3D Human Body Measurement Using Depth Cameras for Smart Health Monitoring

2026-06-09

AI Summary

This study addresses the limitations of traditional anthropometric methods, which rely on contact-based measurements and are unsuitable for telehealth applications. The authors propose a non-contact approach for estimating human geometric parameters from a single-frame depth image. Using an Orbbec Astra 2 depth camera, synchronized RGB images, depth maps, and 3D point clouds are captured. Linear dimensions such as height and arm span are derived through point cloud segmentation, spatial filtering, and keypoint localization, while body volume and visible surface area are simultaneously estimated via voxel occupancy analysis and mesh reconstruction. Experimental results demonstrate that the method achieves high accuracy in measuring critical anthropometric indicators from a single depth capture, enabling fully automated, concurrent estimation of linear dimensions, volume, and surface area—offering a practical solution for real-time intelligent health monitoring.

Abstract

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.

Problem

• contactless measurement
• 3D human body
• depth camera
• anthropometric measurement
• smart health monitoring

Innovation

• depth camera
• 3D point cloud
• contactless anthropometry
• voxel-based volume estimation
• mesh-based surface reconstruction