Development of a Full-Body CAD Dataset for a Computational Model of the 5th Percentile Female

Motor vehicle injuries and fatalities remain a leading public health concern worldwide. In 2013, the World Health Organization reported more than 1.2 million deaths as a result of motor vehicle crashes (MVCs). MVCs are also estimated to have a yearly economic cost of nearly $145 billion. To mitigate the societal impact of these crashes, researchers are using a variety of tools, including finite element (FE) models, to develop and evaluate vehicle safety devices. Such models are often developed to represent an average (50th percentile by height and weight) male occupant. However, a 2011 study from the University of Michigan Transportation Research Institute marked the first year that there were more licensed female drivers than male drivers in the United States. In order to predict the response of a greater portion of the vehicle occupants beyond the 50th percentile male, there is interest in developing such models for other cohorts among the driving public. This work focuses on the female driver in the 5th percentile of height and weight. In the past, 5th percentile female models have primarily been scaled from existing average male models, since scan data for such a specific target anthropometry is limited. However, as part of the Global Human Body Models Consortium (GHBMC) project, comprehensive image and anthropometrical data of a 5th percentile female was acquired. The long range objective of this project is to develop a detailed 5th percentile female full body finite element model in two postures: standing (pedestrian) and seated (vehicle occupant). This thesis describes the first phase of this effort, in which this multi-modality dataset is used to develop a whole-body set of CAD data for the 5th percentile female.

Grantor dc:publisher

Wake Forest University

Year dc:date.issued

2014

Author dc:creator

• Davis, Matthew L.

• Anthropometry

Language dc:language.iso

en

Handle dc:identifier.uri

http://hdl.handle.net/10339/39321

OAI identifier oai:identifier

oai:wakespace.lib.wfu.edu:10339/39321