# A Mobile LiDAR-Based Deep Learning Approach for Real-Time 3D Body Measurement
## 3 Proposed Algorithm for Mobile 3D Body Circumference Measurement
### Utilization of Uniform 3D Data  
In this study, to enhance the accuracy and consistency of the body measurement algorithm, experiments were conducted using uniform 3D data such as cylinder and mannequin. These standardized 3D objects, which closely resemble the human body while exhibiting minimal deformation, were useful for systematically verifying the performance of the algorithm in its initial stages. Cylinders, with their simple structure, are ideal objects for evaluating whether the algorithm can accurately measure complex body structures.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & **Mean Absolute Error (MAE, cm)** & **Coefficient of Determination (\(R^{2}\))** \\ \hline Separate-constant method (\(a_{1}C_{f}+a_{2}C_{s}\)) & 6.8 & 0.76 \\ Summation-then-constant method (\(\beta(C_{f}+C_{s})\)) & 5.2 & 0.84 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the separate-constant method and the summation-then-constant method.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Setting** & **Keypoint Detection** & **LiDAR Sensor** & **Correction Algorithm** & **Relative Error (\%)** \\ \hline Model A (1) & Yes & No & No & Not measurable \\ Model B (2) & Yes & Yes & No & 19.2 \\ Model C (3, Ours) & Yes & Yes & Yes & 5.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of body circumference measurement performance for each setting.  
Additionally, mannequin, which mimic the shape of the human body, were used to test the measurement accuracy of various body parts. Through this approach, it was confirmed that the algorithm could accurately measure body parts of different shapes and sizes. The experimental procedure involved first capturing RGB images and depth maps of the cylinder and mannequin from various angles using an iPhone's LiDAR sensor. Using an HRNet-based keypoint detection model, the endpoints of the major parts of each object were detected. The detected keypoints were then adjusted to their accurate positions in 3D space through the aforementioned correction process. Based on the corrected keypoints, circumferences were calculated, and the total circumference was estimated by integrating the measurements taken from the front and side views. The measured circumference values were compared with the actual values to evaluate the accuracy of the algorithm. Through these experiments, the accuracy and consistency of the proposed body circumference measurement algorithmwere systematically validated, and methods to minimize errors in measuring various body parts were established.  
The body circumference measurement algorithm proposed in this study consists of the following key components. First, RGB images and depth maps are extracted at the same scale to facilitate subsequent data merging and analysis. Second, the keypoints detected by the HRNet model are adjusted to accurate 3D positions through a depth map-based correction process. Third, the circumferences of body parts are calculated using the corrected keypoints, and the total circumference is estimated by integrating the measurements taken from the front and side views. Finally, a regression equation was derived to estimate the total circumference based on the measured circumferences from the front and side views, enabling accurate measurements. Additionally, to enhance the efficiency of the algorithm, the following optimization techniques were introduced. First, model lightweighting and parallel processing were employed to enable real-time body circumference measurements. Data captured from various angles and lighting conditions were utilized to improve the model's generalization performance. Furthermore, filtering techniques were applied to minimize noise in the depth maps.