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

• URL: http://arxiv.org/abs/2606.11578v1
• Date: Wed, 10 Jun 2026 02:12:34 GMT
• Title: Contactless 3D Human Body Measurement Using Depth Cameras for Smart Health Monitoring
• Abstract summary: We introduce a depth camera-based framework for estimating human body measurements utilizing 3D point cloud data.<n>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.
• Score: 4.9948584685520805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Validation of Smartphone-Based Photogrammetric 3D Body Scanning for Automated Anthropometric Measurements Compared with a Commercial Depth-Sensor-Based Body Scanner [9.76394677741046]
  Smartphone-based photogrammetric reconstruction provide a low-cost and accessible alternative to commercial 3D body scanners.<n>This study comprehensively validated the photogrammetric 3D scanning application by evaluating automatically extracted whole-body measurements and longitudinal body-shape monitoring.
  arXiv  Detail & Related papers  (2026-08-13T05:00:55Z)
• Manipulation as in Simulation: Enabling Accurate Geometry Perception in Robots [55.43376513158555]
  Camera Depth Models (CDMs) are a simple plugin on daily-use depth cameras.<n>We develop a neural data engine that generates high-quality paired data from simulation by modeling a depth camera's noise pattern.<n>For the first time, our experiments demonstrate, for the first time, that a policy trained on raw simulated depth, without the need for adding noise or real-world fine-tuning, generalizes seamlessly to real-world robots.
  arXiv  Detail & Related papers  (2025-09-02T17:29:38Z)
• Towards Scalable Spatial Intelligence via 2D-to-3D Data Lifting [64.64738535860351]
  We present a scalable pipeline that converts single-view images into comprehensive, scale- and appearance-realistic 3D representations.<n>Our method bridges the gap between the vast repository of imagery and the increasing demand for spatial scene understanding.<n>By automatically generating authentic, scale-aware 3D data from images, we significantly reduce data collection costs and open new avenues for advancing spatial intelligence.
  arXiv  Detail & Related papers  (2025-07-24T14:53:26Z)
• GRACE: Estimating Geometry-level 3D Human-Scene Contact from 2D Images [54.602947113980655]
  Estimating the geometry level of human-scene contact aims to ground specific contact surface points at 3D human geometries.<n> GRACE (Geometry-level Reasoning for 3D Human-scene Contact Estimation) is a new paradigm for 3D human contact estimation.<n>It incorporates a point cloud encoder-decoder architecture along with a hierarchical feature extraction and fusion module.
  arXiv  Detail & Related papers  (2025-05-10T09:25:46Z)
• CoL3D: Collaborative Learning of Single-view Depth and Camera Intrinsics for Metric 3D Shape Recovery [31.398410174061166]
  We propose a collaborative learning framework for estimating depth and camera intrinsics, named CoL3D, to learn metric 3D shapes from single images.<n>Specifically, CoL3D adopts a unified network and performs collaborative optimization at three levels: depth, camera intrinsics, and 3D point clouds.
  arXiv  Detail & Related papers  (2025-02-13T02:36:01Z)
• A Neural Anthropometer Learning from Body Dimensions Computed on Human 3D Meshes [0.0]
  We present a method to calculate right and left arm length, shoulder width, and inseam (crotch height) from 3D meshes with focus on potential medical, virtual try-on and distance tailoring applications. On the other hand, we use four additional body dimensions calculated using recently published methods to assemble a set of eight body dimensions which we use as a supervision signal to our Neural Anthropometer: a convolutional neural network capable of estimating these dimensions.
  arXiv  Detail & Related papers  (2021-10-06T12:56:05Z)
• Learning Geometry-Guided Depth via Projective Modeling for Monocular 3D Object Detection [70.71934539556916]
  We learn geometry-guided depth estimation with projective modeling to advance monocular 3D object detection. Specifically, a principled geometry formula with projective modeling of 2D and 3D depth predictions in the monocular 3D object detection network is devised. Our method remarkably improves the detection performance of the state-of-the-art monocular-based method without extra data by 2.80% on the moderate test setting.
  arXiv  Detail & Related papers  (2021-07-29T12:30:39Z)
• DONet: Learning Category-Level 6D Object Pose and Size Estimation from Depth Observation [53.55300278592281]
  We propose a method of Category-level 6D Object Pose and Size Estimation (COPSE) from a single depth image. Our framework makes inferences based on the rich geometric information of the object in the depth channel alone. Our framework competes with state-of-the-art approaches that require labeled real-world images.
  arXiv  Detail & Related papers  (2021-06-27T10:41:50Z)
• VR3Dense: Voxel Representation Learning for 3D Object Detection and Monocular Dense Depth Reconstruction [0.951828574518325]
  We introduce a method for jointly training 3D object detection and monocular dense depth reconstruction neural networks. It takes as inputs, a LiDAR point-cloud, and a single RGB image during inference and produces object pose predictions as well as a densely reconstructed depth map. While our object detection is trained in a supervised manner, the depth prediction network is trained with both self-supervised and supervised loss functions.
  arXiv  Detail & Related papers  (2021-04-13T04:25:54Z)
• 3D Human Body Reshaping with Anthropometric Modeling [59.51820187982793]
  Reshaping accurate and realistic 3D human bodies from anthropometric parameters poses a fundamental challenge for person identification, online shopping and virtual reality. Existing approaches for creating such 3D shapes often suffer from complex measurement by range cameras or high-end scanners. This paper proposes a novel feature-selection-based local mapping technique, which enables automatic anthropometric parameter modeling for each body facet.
  arXiv  Detail & Related papers  (2021-04-05T04:09:39Z)
• Optical Non-Line-of-Sight Physics-based 3D Human Pose Estimation [38.57899581285387]
  We describe a method for 3D human pose estimation from transient images. Our method can perceive 3D human pose by looking around corners' through the use of light indirectly reflected by the environment.
  arXiv  Detail & Related papers  (2020-03-31T17:57:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.