# A Mobile LiDAR-Based Deep Learning Approach for Real-Time 3D Body Measurement
## 6 Summary and Conclusions  
In this study, the proposed measurement algorithm overcame the limitations of conventional 2D-based human measurement techniques by integrating HRNet-based keypoint detection with a depth map-based correction process, thereby improving precision. The main conclusions are as follows: \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Front (cm)** & **Side (cm)** & **Estimated (cm)** & **Actual (cm)** & **Error (\%)** \\ \hline Model 1 & 34 & 28 & 82 & 76 & 7.8 \\ Model 2 & 36 & 30 & 87 & 94 & 7.4 \\ Model 3 & 34 & 28 & 82 & 82 & 0 \\ Model 4 & 51 & 41 & 120.32 & 118 & 1.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The results of the buttock circumference measurements from the camera’s perspective are presented. The total circumference was calculated by multiplying the sum of the front and side lengths by a factor of 1.322. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Front (cm)** & **Side (cm)** & **Estimated (cm)** & **Actual (cm)** & **Error (\%)** \\ \hline Model 1 & 35 & 25 & 91.8 & 91 & 0.8 \\ Model 2 & 38 & 30 & 104 & 104 & 0 \\ Model 3 & 35 & 26 & 98 & 97 & 1 \\ Model 4 & 45 & 32 & 117.81 & 121 & 2.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The results of the hip circumference measurements from the camera’s perspective are presented. The total circumference was calculated by multiplying the sum of the front and side lengths by a factor of 1.53. * Ensuring Accuracy and Practicality in Body Circumference Measurement: Experimental results showed that for most body regions, the relative error remained within 8%, demonstrating higher accuracy compared to existing 2D-based methods. In particular, key areas such as the waist, chest, and hip were measured with consistent performance, confirming the algorithm's potential for practical applications. * Proposing a 2D Keypoint Correction Method Using Depth Maps: By correcting the 2D keypoints detected in images with the 3D data from depth maps, the influence of factors such as camera angles and lighting variations was mitigated. This enabled more precise measurements in 3D space, complementing the limitations of 2D-based approaches. * Achieving Real-Time Capability through Lightweight Processing: A simple computational method that calculates only the Euclidean distance after 2D keypoint detection was adopted, reducing the computational burden. As a result, the approach shows the potential for near-real-time operation even in resource-constrained environments such as mobile devices. * Potential Applications and Limitations: This algorithm is expected to be applicable in diverse fields, including fitness, healthcare, and the apparel industry. Nonetheless, the error was slightly higher for certain body areas, and the experiment was conducted with a limited sample, pointing to existing constraints. Future studies should expand the sample to various nationalities and ethnic groups to enhance the algorithm's generalizability and accuracy. In conclusion, the 3D landmark-based measurement algorithm proposed in this study achieves both high accuracy and near-real-time performance, offering significantly greater versatility compared to existing methods. Through ongoing refinement and expansion, this algorithm is anticipated to contribute to the development of more precise and reliable body measurement systems. Future work should focus on expanding the dataset to include a wider range of body types and ethnicities to ensure broader applicability. Additionally, further optimization of the algorithm can facilitate seamless integration into mobile applications, enabling real-time 3D body measurement for various practical uses in healthcare, fitness, and apparel industries. Conceptualization, Y.J., T.N. and S.K. ; Formal analysis, T.N. and Y.J. ; Funding acquisition, S.K. ; investigation, T.N. and Y.J. ; Methodology, T.N. and Y.J. ; Project administration, S.K. ; Resources, T.N. and Y.J. ; Software, T.N. and Y.J. ; Supervision, S.K. ; Validation, Y.J. and T.N. ; Visualization, T.N. and Y.J.