# A Mobile LiDAR-Based Deep Learning Approach for Real-Time 3D Body Measurement
###### Abstract  
In this study, we propose a solution for automatically measuring body circumferences by utilizing the built-in LiDAR sensor in mobile devices. Traditional body measurement methods mainly rely on 2D images or manual measurements. This research, however, utilizes 3D depth information to enhance both accuracy and efficiency. By employing HRNet-based keypoint detection and transfer learning through deep learning, the precise locations of body parts are identified and combined with depth maps to automatically calculate body circumferences. Experimental results demonstrate that the proposed method exhibits a relative error of up to 8% for major body parts such as waist, chest, hip, and buttock circumferences, with waist and buttock measurements recording low error rates below 4%. Although some models showed error rates of 7.8% and 7.4% in hip circumference measurements, this was attributed to the complexity of 3D structures and the challenges in selecting keypoint locations. Additionally, the use of depth map-based keypoint correction and regression analysis significantly improved accuracy compared to conventional 2D-based measurement methods. The real-time processing speed was also excellent, ensuring stable performance across various body types. LiDAR; HRNet; deep learning; keypoint +
Footnote †: journal: Applied sciences  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-00000-0000-0000-00000-00000-00000-00000-00000-00000-00000-00000-00000-000000-00000-000000-000000-00000-000000-000000-000000-000000-000000-0000000-0000000-000000 control. Additionally, image segmentation technology [7] is utilized to accurately identify and analyze tumors or other lesions in medical imaging, and motion recognition technology is applied in analyzing the movements of athletes or in human behavior recognition systems. However, these computer vision technologies are primarily based on 2D images or video data as shown in the Figure 1, and accurate length measurement solutions in 3D space have not yet been sufficiently developed. 3D length measurement can play a significant role in various fields such as architecture, manufacturing, healthcare, and fashion, and can greatly enhance accuracy and efficiency in personalized services like body size measurement. In recent years, LiDAR (Light Detection And Ranging) [8] technology has rapidly advanced by expanding its range of applications. LiDAR is a technology that calculates distances by emitting lasers and measuring the time it takes for the reflected light from objects to return, thereby generating high-resolution 3D point clouds. This technology is utilized in various fields such as autonomous vehicles, drones, robotics, and Geographic Information Systems (GIS), and it particularly excels in precise data collection and analysis in 3D spaces. Additionally, LiDAR sensors embedded in mobile devices provide high-resolution depth information in consumer-grade devices, enabling general users to easily utilize 3D data. LiDAR sensors can operate in both vertical and horizontal directions and can create precise 3D models of objects through multi-directional scanning. Due to these advantages, LiDAR is extensively applied in specialized fields such as surveying, geomorphology, archaeology, laser guidance, and high-resolution mapping. In particular, high-resolution 3D scanning has established itself as an essential tool for precisely analyzing complex terrains and structures. However, up to now, LiDAR technology has been primarily used in specialized equipment or industrial settings, and its utilization in everyday life has been limited. This is due to the high cost of equipment and complex maintenance requirements, making it perceived as a technology that is difficult for general users to access. Therefore, developing a more affordable and user-friendly 3D measurement solution by leveraging LiDAR sensors embedded in mobile devices has become an important research task.