A fast-modeling framework for personalized human body models based on a single image

Finite element human body models (HBMs) are the primary method for predicting human biological responses in vehicle collisions, especially personalized HBMs that allow accounting for diverse populations. Yet, creating personalized HBMs from a single image is a challenging task. This study addresses this challenge by providing a framework for HBM personalization, starting from a single image used to estimate the subject's skin point cloud, the skeletal point cloud, and the relative positions of the skeletons. Personalized HBMs were created by morphing the baseline HBM accounting skin and skeleton point clouds using a point cloud registration-based mesh morphing method. Using this framework, eight personalized HBMs with various biological characteristics (e.g., sex, height, and weight) were created, with comparable element quality to the baseline HBM. The mean geometric errors of the personalized FEMs generated by the framework are less than 7 mm, which was found to be acceptable based on biomechanical response evaluations conducted in this study.