Performances of the PIPER scalable child human body model in accident reconstruction

Area of Science:

• Biomechanical Engineering
• Pediatric Injury Biomechanics
• Computational Human Body Modeling

Background:

• Human body models (HBMs) are crucial for understanding pediatric responses to impact.
• Existing models may lack scalability and personalization for diverse child demographics.
• Accurate child accident reconstructions are vital for improving traffic safety.

Purpose of the Study:

• To present a scalable and posable approach for child accident reconstructions using the PIPER HBM.
• To validate the PIPER HBM's ability to predict injury severity and location in real-world scenarios.
• To establish a foundation for determining child injury tolerances using accident databases.

Main Methods:

• Utilized the Position and Personalize Advanced Human Body Models for Injury Prediction (PIPER) scalable child HBM.
• Employed the PIPER tool for model scaling and positioning across different ages and postures.
• Reconstructed real-life child crash scenarios using documented medical records.

Main Results:

• The PIPER scalable child HBM demonstrated reasonable accuracy in predicting injury severity and location.
• The model successfully represented children of various ages and positions in crash reconstructions.
• The developed methodology provides a workflow for future injury tolerance studies.

Conclusions:

• The PIPER scalable HBM and associated tool offer a viable approach for child accident reconstruction.
• This methodology is essential for advancing research on child injury tolerances.
• The open-source PIPER HBM has the potential to significantly improve child traffic safety designs.