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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.

A model of PC is described in which innate inflammation is activated locally and systemically. Systemic levels of CC and CXC chemokines are increased after PC. This correlates with elevated PMN CD-11b expression, enhanced pulmonary ICAM-1 expression, and mononuclear and PMN infiltration into the lung and alveolar space. Elevated levels of CC and CXC chemokines are seen after PC and may be involved in the lung's inflammatory response to injury.

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.

Introduction:A simplified and computationally efficient human body finite element model is presented. The model complements the Global Human Body Models Consortium (GHBMC) detailed 50th percentile occupant (M50-O) by providing kinematic and kinetic data with a significantly reduced run time using the same body habitus.Methods: The simplified occupant model (M50-OS) was developed using the same source geometry as the M50-O. Though some meshed components were preserved, the total element count was reduced by remeshing, homogenizing, or in some cases omitting structures that are explicitly contained in the M50-O. Bones are included as rigid bodies, with the exception of the ribs, which are deformable but were remeshed to a coarser element density than the M50-O. Material models for all deformable components were drawn from the biomechanics literature. Kinematic joints were implemented at major articulations (shoulder, elbow, wrist, hip, knee, and ankle) with moment vs. angle relationships from the literature included for the knee and ankle. The brain of the detailed model was inserted within the skull of the simplified model, and kinematics and strain patterns are compared.Results: The M50-OS model has 11 contacts and 354,000 elements; in contrast, the M50-O model has 447 contacts and 2.2 million elements. The model can be repositioned without requiring simulation. Thirteen validation and robustness simulations were completed. This included denuded rib compression at 7 discrete sites, 5 rigid body impacts, and one sled simulation. Denuded tests showed a good match to the experimental data of force vs. deflection slopes. The frontal rigid chest impact simulation produced a peak force and deflection within the corridor of 4.63 kN and 31.2%, respectively. Similar results vs. experimental data (peak forces of 5.19 and 8.71 kN) were found for an abdominal bar impact and lateral sled test, respectively. A lateral plate impact at 12 m/s exhibited a peak of roughly 20 kN (due to stiff foam used around the shoulder) but a more biofidelic response immediately afterward, plateauing at 9 kN at 12 ms. Results from a frontal sled simulation showed that reaction forces and kinematic trends matched experimental results well. The robustness test demonstrated that peak femur loads were nearly identical to the M50-O model. Use of the detailed model brain within the simplified model demonstrated a paradigm for using the M50-OS to leverage aspects of the M50-O. Strain patterns for the 2 models showed consistent patterns but greater strains in the detailed model, with deviations thought to be the result of slightly different kinematics between models. The M50-OS with the deformable skull and brain exhibited a run time 4.75 faster than the M50-O on the same hardware.Conclusions: The simplified GHBMC model is intended to complement rather than replace the detailed M50-O model. It exhibited, on average, a 35-fold reduction in run time for a set of rigid impacts. The model can be used in a modular fashion with the M50-O and more broadly can...

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.

The purpose of this study was to acquire external landmark, undeformed surface, and volume data from a pre-screened individual representing a mid-sized male (height 174.9 cm, weight 78.6 ± 0.77 kg) in the seated and standing postures. The individual matched the 50th percentile value of 15 measures of external anthropometry from previous anthropometric studies with an average deviation of 3%. As part of a related study, a comprehensive full body medical image data set was acquired from the same individual on whom landmark data were collected. Three dimensional bone renderings from this data were used to visually verify the landmark and surface results. A total of 54 landmarks and external surface data were collected using a 7-axis digitizer. A seat buck designed in-house with removable back and seat pan panels enabled collection of undeformed surface contours of the back, buttocks, and posterior thigh. Eight metrics describing the buck positioning are provided. A repeatability study was conducted with three trials to assess intra-observer variability. Total volume and surface area of the seated model were found to be 75.8 × 10(3) cm(3) and 18.6 × 10(3) cm(2) and match the volume and surface area of the standing posture within 1%. Root mean squared error values from the repeatability study were on average 5.9 and 6.6 mm for the seated and standing postures respectively. The peak RMS error as a percentage of the centroid size of the landmark data sets were 3% for both the seated and standing trials. The data were collected as part of a global program on the development of an advanced human body model for blunt injury simulation. In addition, the reported data can be used for many diverse applications of biomechanical research such as ergonomics and morphometrics studies.

This study outlines a protocol for image data collection acquired from human volunteers. The data set will serve as the foundation of a consolidated effort to develop the next generation full-body Finite Element Analysis (FEA) models for injury prediction and prevention. The geometry of these models will be based off the anatomy of four individuals meeting extensive prescreening requirements and representing the 5th and 50th percentile female, and the 50th and 95th percentile male. Target values for anthropometry are determined by literature sources. Because of the relative strengths of various modalities commonly in use today in the clinical and engineering worlds, a multi-modality approach is outlined. This approach involves the use of Computed Tomography (CT), upright and closed-bore Magnetic Resonance Imaging (MRI), and external anthropometric measurements. CT data provide sub-millimeter resolution and slice thickness of the subjects in the supine and an approximately seated position. Closed-bore MRI complements CT data by providing high-resolution images with improved contrast between soft tissues. MRI pulse sequences that image fat-water interfaces out-of-phase are used to enhance contrast and facilitate segmenting organ and muscle boundaries. Upright MRI data complement closed-bore data by enabling quantification of morphological changes that occur when a subject is oriented upright with respect to gravity. The final component in this suite of image data is a set of external anthropometry (EA) measurements. EA measurements include three-dimensional point cloud acquisition of external bony landmarks as well as surface contours. These data serve as a valuable geometric validation tool for the assembled full-body FEA models. Protocol development results, including preliminary image data sets, in-plane resolution and slice thickness achieved for each modality, pulse sequence designs for MRI acquisition protocols, and custom positioning systems used in image acquisition are presented. The approach outlined in this study is expected to provide sufficient data to develop models in both the seated and standing posture. This suite of imaging and anthropometry data will serve as a strong foundation for the collaborative development of a group of fullbody FEA models for injury prediction in the coming years.