Developments in the simulation of real world car to pedestrian accidents using a pedestrian humanoid finite element model

Hardy, Roger; Watson, James; Howard, Mark

doi:10.1533/cras.2000.0127

Cited by 8 publications

(1 citation statement)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, Vavalle et al utilized a human body FEA model to simulate impacts to the chest and abdomen under various localized conditions, verifying the accuracy of their model by demonstrating its consistency with experimental data [22]. Traditionally, most research on rib fractures has focused on those resulting from collisions or other accidents [23][24][25][26][27][28][29]. Unlike these studies, rib fractures occurring during cardiopulmonary resuscitation (CPR) represent a unique situation where loads are repetitively applied.…”

Section: Introductionmentioning

confidence: 99%

Finite Element Analysis of a Rib Cage Model: Influence of Four Variables on Fatigue Life during Simulated Manual CPR

Jeon,

Sul,

et al. 2024

Bioengineering

View full text Add to dashboard Cite

Cardiopulmonary resuscitation (CPR) is a life-saving technique used in emergencies when the heart stops beating, typically involving chest compressions and ventilation. Current adult CPR guidelines do not differentiate based on age beyond infancy and childhood. This oversight increases the risk of fatigue fractures in the elderly due to decreased bone density and changes in thoracic structure. Therefore, this study aimed to investigate the correlation and impact of factors influencing rib fatigue fractures for safer out-of-hospital manual cardiopulmonary resuscitation (OHMCPR) application. Using the finite element analysis (FEA) method, we performed fatigue analysis on rib cage models incorporating chest compression conditions and age-specific trabecular bone properties. Fatigue life analyses were conducted on three age-specific rib cage models, each differentiated by trabecular bone properties, to determine the influence of four explanatory variables (the properties of the trabecular bone (a surrogate for the age of the subject), the site of application of the compression force on the breastbone, the magnitude of applied compression force, and the rate of application of the compression force) on the fatigue life of the model. Additionally, considering the complex interaction of chest compression conditions during actual CPR, we aimed to predict rib fatigue fractures under conditions simulating real-life scenarios by analyzing the sensitivity and interrelation of chest compression conditions on the model’s fatigue life. Time constraints led to the selection of optimal analysis conditions through the use of design of experiments (DOE), specifically orthogonal array testing, followed by the construction of a deep learning-based metamodel. The predicted fatigue life values of the rib cage model, obtained from the metamodel, showed the influence of the four explanatory variables on fatigue life. These results may be used to devise safer CPR guidelines, particularly for the elderly at a high risk of acute cardiac arrest, safeguarding against potential complications like fatigue fractures.

show abstract