Three-Dimensional Mesh Recovery from Common 2-Dimensional Pictures for Automated Assessment of Body Posture in Camptocormia

BackgroundThree-dimensional (3D) human body estimation from common photographs is an evolving method in the field of computer vision. It has not yet been evaluated on postural disorders. We generated 3D models from 2-dimensional pictures of camptocormia patients to measure the bending angle of the trunk according to recommendations in the literature. MethodsWe used the Part Attention Regressor algorithm to generate 3D models from photographs of camptocormia patients' posture and validated the resulting angles against the gold standard. A total of 2 virtual human models with camptocormia were generated to evaluate the performance depending on the camera angle. ResultsThe bending angle assessment using the 3D mesh correlated highly with the gold standard (R = 0.97, P < 0.05) and is robust to deviations of the camera angle. ConclusionsThe generation of 3D models offers a new method for assessing postural disorders. It is automated and robust to nonperfect pictures, and the result offers a comprehensive analysis beyond the bending angle.

Automated summary

Using computer vision technology, 3D human body models are generated from regular photographs to evaluate postural disorders. The accuracy and applicability of this method are validated by comparing with the gold standard. The generated 3D models are not affected by the camera angle and provide a comprehensive analysis.