Predicting neck kinematics and tissue level response is essential to evaluate the potential for occupant injury in rear impact. A detailed 50th percentile male finite element model, previously validated for frontal impact, was validated for rear impact scenarios with material properties based on actual tissue properties from the literature. The model was validated for kinematic response using 4 g volunteer and 7 g cadaver rear impacts, and at the tissue level with 8 g isolated full spine rear impact data. The model was then used to predict capsular ligament (CL) strain for increasing rear impact severity, since CL strain has been implicated as a source of prolonged pain resulting from whiplash injury. The model predicted the onset of CL injury for a 14 g rear impact, in agreement with motor vehicle crash epidemiology. More extensive and severe injuries were predicted with increasing impact severity. The importance of muscle activation was demonstrated for a 7 g rear impact where the CL strain was reduced from 28 to 13% with active muscles. These aspects have not previously been demonstrated experimentally, since injurious load levels cannot be applied to live human subjects. This study bridges the gap between low intensity volunteer impacts and high intensity cadaver impacts, and predicts tissue level response to assess the potential for occupant injury
Keywords : Accidents,Traffic,Biomechanical Phenomena,Cadaver,Canada,Cervical Vertebrae,epidemiology,Finite Element Analysis,Humans,injuries,Ligaments,Male,Models,Anatomic,Models,Biological,Muscles,Neck,Pain,pathology,prevention & control,Spine,Universities,Whiplash Injuries,, Spine,Model,Predict,Capsular, ct scan private london
Date of Publication : 2011 Aug
Authors : Fice JB;Cronin DS;Panzer MB;
Organisation : Department of Mechanical Engineering, University of Waterloo, West, Waterloo, ON, Canada
Journal of Publication : Ann Biomed Eng
Pubmed Link : https://www.ncbi.nlm.nih.gov/pubmed/21533673
The London Spine Unit : Harley Street UK. Specialists in Cutting Edge Technologies for Spinal Surgery
English
Arabic
Chinese (Simplified)
Russian
Spanish
Urdu