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Injury Metrics, Real World Crash Reconstruction, and Robust Injury Prediction for a Human Body Model in Side Impact

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abstract
Motor vehicle crashes (MVCs) are a significant public health problem. Worldwide, MVCs kill over 1.2 million people each year. Improved understanding of the occupant loading conditions in real world crashes is critical for injury prevention and new vehicle design. Real world crash reconstructions using vehicle and human body finite element models (HBM) have the potential to elucidate injury mechanism, predict injury risk, and evaluate injury mitigation system effectiveness. The purpose of the work presented herein was to create a novel paradigm in finite element (FE) MVC injury analysis to evaluate injury risk in real world crash scenarios.
subject
finite element analysis
Human body model
injury metrics
injury prediction
injury risk
real world motor vehicle crash
contributor
Golman, Adam Joseph (author)
Stitzel, Joel D (committee chair)
Danelson, Kerry A (committee member)
Gayzik, F Scott (committee member)
date
2013-06-06T21:19:36Z (accessioned)
2013 (issued)
degree
Biomedical Engineering (discipline)
10000-01-01 (liftdate)
embargo
forever (terms)
identifier
http://hdl.handle.net/10339/38566 (uri)
language
en (iso)
publisher
Wake Forest University
title
Injury Metrics, Real World Crash Reconstruction, and Robust Injury Prediction for a Human Body Model in Side Impact
type
Thesis

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