The objective of this study was to develop full body CAD geometry of a seated 50th percentile male. Model development was based on medical image data acquired for this study, in conjunction with extensive data from the open literature. An individual (height, 174.9 cm, weight, 78.6 ± 0.77 kg, and age 26 years) was enrolled in the study for a period of 4 months. 72 scans across three imaging modalities (CT, MRI, and upright MRI) were collected. The whole-body dataset contains 15,622 images. Over 300 individual components representing human anatomy were generated through segmentation. While the enrolled individual served as a template, segmented data were verified against, or augmented with, data from over 75 literature sources on the average morphology of the human body. Non-Uniform Rational B-Spline (NURBS) surfaces with tangential (G1) continuity were constructed over all the segmented data. The sagittally symmetric model consists of 418 individual components representing bones, muscles, organs, blood vessels, ligaments, tendons, cartilaginous structures, and skin. Length, surface area, and volumes of components germane to crash injury prediction are presented. The total volume (75.7 × 103 cm(3)) and surface area (1.86 × 102 cm(2)) of the model closely agree with the literature data. The geometry is intended for subsequent use in nonlinear dynamics solvers, and serves as the foundation of a global effort to develop the next-generation computational human body model for injury prediction and prevention.

This study presents four validation cases of a mid-sized male (M50) full human body finite element model-two lateral sled tests at 6.7 m/s, one sled test at 8.9 m/s, and a lateral drop test. Model results were compared to transient force curves, peak force, chest compression, and number of fractures from the studies. For one of the 6.7 m/s impacts (flat wall impact), the peak thoracic, abdominal and pelvic loads were 8.7, 3.1 and 14.9 kN for the model and 5.2 ± 1.1 kN, 3.1 ± 1.1 kN, and 6.3 ± 2.3 kN for the tests. For the same test setup in the 8.9 m/s case, they were 12.6, 6, and 21.9 kN for the model and 9.1 ± 1.5 kN, 4.9 ± 1.1 kN, and 17.4 ± 6.8 kN for the experiments. The combined torso load and the pelvis load simulated in a second rigid wall impact at 6.7 m/s were 11.4 and 15.6 kN, respectively, compared to 8.5 ± 0.2 kN and 8.3 ± 1.8 kN experimentally. The peak thorax load in the drop test was 6.7 kN for the model, within the range in the cadavers, 5.8-7.4 kN. When analyzing rib fractures, the model predicted Abbreviated Injury Scale scores within the reported range in three of four cases. Objective comparison methods were used to quantitatively compare the model results to the literature studies. The results show a good match in the thorax and abdomen regions while the pelvis results over predicted the reaction loads from the literature studies. These results are an important milestone in the development and validation of this globally developed average male FEA model in lateral impact.

The location and morphology of abdominal organs due to postural changes have implications in the prediction of trauma via computational models. The purpose of this study is to use data from a multimodality image set to devise a method for examining changes in organ location, morphology, and rib coverage from the supine to seated postures. Medical images of a male volunteer (78.6 ± 0.77 kg, 175 cm) in three modalities (Computed Tomography, Magnetic Resonance Imaging (MRI), and Upright MRI) were used. Through image segmentation and registration, an analysis between organs in each posture was conducted. For the organs analyzed (liver, spleen, and kidneys), location was found to vary between postures. Increases in rib coverage from the supine to seated posture were observed for the liver, with a 9.6% increase in a lateral projection and a 4.6% increase in a frontal projection. Rib coverage area was found to increase 11.7% for the spleen. Morphological changes in the organs were also observed. The liver expanded 7.8% cranially and compressed 3.4% and 5.2% in the anterior-posterior and medial-lateral directions, respectively. Similar trends were observed in the spleen and kidneys. These findings indicate that the posture of the subject has implications in computational human body model development.