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Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

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Published on: April 11, 2018

A new approach to multibody model development: pedestrian lower extremity.

Jason R Kerrigan¹, Dan P Parent, Costin Untaroiu

¹University of Virginia Center for Applied Biomechanics, Charlottesville, Virginia 22902, USA. jrk3z@virginia.edu

Traffic Injury Prevention

|July 14, 2009

Summary

A new mathematical model accurately predicts pedestrian lower extremity injury risk and response during vehicle impacts. This validated model aids in designing safer vehicles and understanding pedestrian kinematics.

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Area of Science:

Biomechanics
Injury Biomechanics
Computational Modeling

Background:

Vehicle-pedestrian impacts pose significant injury risks.
Accurate simulation of lower extremity response is crucial for injury prevention.
Existing models may lack detailed kinematic prediction capabilities.

Purpose of the Study:

Develop a validated mathematical model of the 50th percentile male lower extremity.
Predict injury risk and simulate kinetic/kinematic responses in vehicle impacts.
Enhance understanding of pedestrian lower extremity behavior under impact loading.

Main Methods:

Developed a hip-to-foot multibody model in MADYMO software.
Utilized detailed finite element model (FEM) geometry and literature data for properties.
Optimized structural and contact parameters, validated against dynamic bending experiments and PMHS impact data.

Main Results:

Optimized model showed excellent correlation with experimental bending data.
Full-scale kinematic response closely matched postmortem human subject (PMHS) data.
Model accurately predicted valgus knee injury and tibia fracture patterns.

Conclusions:

The developed model accurately predicts lower extremity kinematics and injury risk in 40 km/h vehicle impacts.
The model is scalable for different anthropometries and can assess common pedestrian injuries.
Applications include vehicle design optimization, injury analysis, and real-world case reconstructions.