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.

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.

The results demonstrate that a single metric is insufficient to provide a complete assessment of how well the simulated results match the experiments. The CORA method provided the most comprehensive evaluation of the signal. Regardless of the method selected, one primary recommendation of this work is that for any comparison, the results should be reported to provide separate assessments of a signal's match to experimental variance, magnitude, phase, and shape. Future work planned includes implementing any forthcoming International Organization for Standardization standards for objective evaluations. Supplemental materials are available for this article. Go to the publisher's online edition of Traffic Injury Prevention to view the supplemental file.

Though this work was focused on techniques to extract and analyze chestband data from FE models, the comparative results provide further validation of the model used in this study. The results suggest the importance of evaluating comparisons between virtual and experimental chestband data on a regional basis. These data also provide the potential to correlate chestband deformations to the loading of underlying thoraco-abdominal structures. Supplemental materials are available for this article. Go to the publisher's online edition of Traffic Injury Prevention to view the supplemental file.

Human body finite element (FE) models are beginning to play a more prevalent role in the advancement of automotive safety. A methodology has been developed to evaluate neck response at multiple levels in a human body FE model during simulated automotive impacts. Three different impact scenarios were simulated: a frontal impact of a belted driver with airbag deployment, a frontal impact of a belted passenger without airbag deployment and an unbelted side impact sled test. Cross sections were created at each vertebral level of the cervical spine to calculate the force and moment contributions of different anatomical components of the neck. Adjacent level axial force ratios varied between 0.74 and 1.11 and adjacent level bending moment ratios between 0.55 and 1.15. The present technique is ideal for comparing neck forces and moments to existing injury threshold values, calculating injury criteria and for better understanding the biomechanical mechanisms of neck injury and load sharing during sub-injurious and injurious loading.

The location and shape variation of the liver and spleen due to postural changes has implications in the prediction of blunt abdominal trauma via computational models or other human surrogates. Although abdominal injuries occur in only 3–5% of injuries in car crashes, they are often seen in serious crashes with AIS≥4 injuries. The location and shape variation of the liver and spleen due to postural changes are likely to play a role in the severity of injury, as a result of rib coverage and location. One means of studying this variation is through computational modeling for impact biomechanics studies. This study reviews the methods used to quantify variation in the location of the liver and the spleen relative to surrounding bony structures for two different postures, supine and seated. Through the use of various imaging modalities three-dimensional datasets depicting the affect of posture change on the size and shape of the liver and spleen can be investigated through three-dimensional renderings and center of gravity measurements.