An integrated framework for rapid creation of finite element subject-specific human body models for injury analysis

Subject-specific finite element (FE) human body models are valuable for biomechanics, injury analysis, and personalized medical planning; however, generating such models efficiently while maintaining mesh quality and solver compatibility remains challenging. This study presents an integrated framework for rapid FE human body model morphing with three key features. First, the framework uses automatic multi-source landmark generation, combining surface correspondences, extremal points, curvature-based landmarks, edge or boundary points, and FE-proximity anchors to improve anatomical coverage and deformation control. Second, it incorporates landmark down-sampling before nonrigid radial basis function (RBF) morphing to reduce computational cost while preserving a spatially distributed set of control points. Third, it directly updates the nodal coordinates of the original FE model, thereby preserving element connectivity, part definitions, material assignments, contacts, boundary conditions, and other solver keyword structures. The framework was demonstrated using two model-to-model morphing cases based on existing FE human body model geometries rather than CT- or MRI-derived subject anatomy: morphing a THUMS 10YO thoracic spine toward CHARM-10 geometry and morphing a VIVA+ F50 torso toward VIVA+ M50 geometry. The morphed models showed improved geometric agreement with the target surfaces and remained numerically stable in spine torsion and sternum impact simulations, which were used as solver-readiness checks rather than full biomechanical validation. Runtime analysis showed that landmark alignment is the main computational bottleneck, and reducing landmark density before alignment can substantially lower computational cost, with a trade-off in geometric accuracy. These results demonstrate a promising solver-compatible FE morphing framework at regional and torso scales. Future work will evaluate additional anatomies, postures, mesh densities, model families, full-body models, and patient-derived geometries to fully validate the proposed framework as a general solution for subject-specific FE model generation.