Kinematics of Pediatric Crash Dummies Seated on Vehicle Seats with Realistic Belt Geometry

<div><p><b>Objective:</b> A series of sled tests was performed using vehicle seats and Hybrid-III 6-year-old (6YO) and 10YO anthropomorphic test devices (ATDs) to explore possibilities for improving occupant protection for children who are not using belt-positioning booster seats.</p><p><b>Methods:</b> Cushion length was varied from production length of 450 mm to a shorter length of 350 mm. Lap belt geometry was set to rear, mid, and forward anchorage locations that span the range of lap belt angles found in vehicles. Six tests each were performed with the 6YO and 10YO Hybrid III ATDs. One additional test was performed using a booster seat with the 6YO. The ATDs were positioned using an updated version of the University of Michigan Transportation Research Institute (UMTRI) seating procedure that positions the ATD hips further forward with longer seat cushions to reflect the effect of cushion length on posture that has been measured with child volunteers. ATD kinematics were evaluated using peak head excursion, peak knee excursion, the difference between peak head and peak knee excursion, and the maximum torso angle.</p><p><b>Results:</b> Shortening the seat cushion improved kinematic outcomes, particularly for the 10YO. Lap belt geometry had a greater effect on kinematics with the longer cushion length, with mid or forward belt geometries producing better kinematics than the rearward belt geometry. The worst kinematics for both ATDs occurred with the long cushion length and rearward lap belt geometry. The improvements in kinematics from shorter cushion length or more forward belt geometry are smaller than those provided by a booster seat.</p><p><b>Conclusions:</b> The results show potential benefits in occupant protection from shortening cushion length and increasing lap belt angles, particularly for children the size of the 10YO ATD.</p></div

A Statistical Skull Geometry Model for Children 0-3 Years Old

<div><p>Head injury is the leading cause of fatality and long-term disability for children. Pediatric heads change rapidly in both size and shape during growth, especially for children under 3 years old (YO). To accurately assess the head injury risks for children, it is necessary to understand the geometry of the pediatric head and how morphologic features influence injury causation within the 0–3 YO population. In this study, head CT scans from fifty-six 0–3 YO children were used to develop a statistical model of pediatric skull geometry. Geometric features important for injury prediction, including skull size and shape, skull thickness and suture width, along with their variations among the sample population, were quantified through a series of image and statistical analyses. The size and shape of the pediatric skull change significantly with age and head circumference. The skull thickness and suture width vary with age, head circumference and location, which will have important effects on skull stiffness and injury prediction. The statistical geometry model developed in this study can provide a geometrical basis for future development of child anthropomorphic test devices and pediatric head finite element models.</p></div

Landmark information for 56 pediatric skulls from CT scans

This zip file includes 4 folders: subject info, landmark location, suture width, and thickness. Landmark information is provided for each subject in the last three folders, and the landmark numbers are corresponding to those listed in the paper

Landmarks on the skull surfaces and suture-bone boundaries.

<p>Note: B01 to B24 represent landmarks located at the skull (bone) surface, S01 to S32 represent landmarks located at the suture center lines, and C01 to C04 represent landmarks located at the intersections of the sutures. Landmarks on the skull surface are generally evenly distributed on the reference curves. For example, landmark No. B12 on the skull surface is midway between landmarks No. S27 and S14; and landmark No. B14 is in the middle of landmarks No. B12 and S14.</p

Skull thickness distribution by age.

<p>The models shown here were generated by morphing a template mesh into the model-predicted skull geometry using a Radial Basis Function. The color contours were generated based on the skull thickness values associated with each node on the morphed mesh. The quantitative skull thickness data corresponding to each landmark location can be found in Table D in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127322#pone.0127322.s001" target="_blank">S1 Appendix</a>.</p

Comparison between model predicted skull geometry and segmented CT geometry.

<p>Comparison between model predicted skull geometry and segmented CT geometry.</p

Pediatric head circumference and CirOffset relative to the CDC median head circumference curve.

<p>Pediatric head circumference and CirOffset relative to the CDC median head circumference curve.</p

Skull thickness distribution variation among 1.5 YO children with a range of head circumference.

<p>The models shown here were generated by morphing a template mesh into the model-predicted skull geometry using a Radial Basis Function. The color contours were generated based on the skull thickness values associated with each node on the morphed mesh. The quantitative skull thickness data corresponding to each landmark location can be found in Table D in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127322#pone.0127322.s001" target="_blank">S1 Appendix</a>.</p

Box plots of signed error in mm between predicted landmark coordinates and the actual landmarks collected from CT data of sampled six subjects.

<p>Top row represents the age in month and the head circumference in cm of the subjects.</p

Procedure of CT data processing.

<p>Procedure of CT data processing.</p