Effect of pedestrian physique differences on head injury prediction in car-to-pedestrian accidents using deep learning

2021 ◽  
pp. 1-5
Author(s):  
Shouhei Kunitomi ◽  
Shinichi Takayama
Keyword(s):  
Deep Learning ◽  
Head Injury ◽  
Injury Prediction ◽  
Pedestrian Accidents

Head injury prediction capability of the HIC, HIP, SIMon and ULP criteria

2008 ◽  
Vol 40 (3) ◽  
pp. 1135-1148 ◽  
Author(s):  
Daniel Marjoux ◽  
Daniel Baumgartner ◽  
Caroline Deck ◽  
Rémy Willinger
Keyword(s):  
Head Injury ◽  
Injury Prediction ◽  
Prediction Capability

scholarly journals Head Injury Prediction Methods Based on 6 Degree of Freedom Head Acceleration Measurements during Impact

2011 ◽  
Vol 2 (2) ◽  
pp. 13-19 ◽  
Author(s):  
Hideyuki Kimpara ◽  
Yuko Nakahira ◽  
Masami Iwamoto ◽  
Steve Rowson ◽  
Stefan Duma
Keyword(s):  
Head Injury ◽  
Degree Of Freedom ◽  
Prediction Methods ◽  
Head Acceleration ◽  
Injury Prediction

scholarly journals INVESTIGATION ON RISK PREDICTION OF PEDESTRIAN HEAD INJURY BY REAL-WORLD ACCIDENTS

Transport ◽  
2019 ◽  
Vol 34 (3) ◽  
pp. 394-403
Author(s):  
Fan Li ◽  
Honggeng Li ◽  
Fuhao Mo ◽  
Sen Xiao ◽  
Zhi Xiao
Keyword(s):  
Head Injury ◽  
Brain Tissue ◽  
Real World ◽  
Injury Risk ◽  
Brain Injuries ◽  
Physical Parameters ◽  
Kinematic Parameters ◽  
Injury Criteria ◽  
Head Injury Criteria ◽  
Pedestrian Accidents

Head injury is the most common and fatal injury in car-pedestrian accidents. Due to the lack of human test data, real-world accident data is useful for the research on the mechanism and tolerance of head injuries. The objective of the present work is to investigate pedestrian head-brain injuries through real car-pedestrian accidents and evaluate the existed injury criteria. Seven car-to-pedestrian accidents in China were selected from the IVAC (Investigation of Vehicle Accident in Changsha) database. Accident reconstructions using multi-body models were conducted to determine the kinematic parameters associated with the injury and were used to measure head injury criteria. Kinematic parameters were input into a finite element model to run simulations on the head-brain and car interface to determine levels of brain tissue stress, strain, and brain tissue injury criteria. A binary logistic regression model was used to determine the probability of head injury risk associated with AIS3+ injuries (Abbreviated Injury Scale). The results showed that head injury criteria using kinematic parameters can effectively predict injury risk of a pedestrians’ head skull. Regarding brain injuries, physical parameters like coup/countercoup pressure are more effective predictors. The results of this study can be used as the background knowledge for pedestrian friendly car design.

Head injury criteria in child pedestrian accidents

2017 ◽  
Vol 23 (5) ◽  
pp. 497-506 ◽  
Author(s):  
D. Montoya ◽  
L. Thollon ◽  
M. Llari ◽  
C. Perrin ◽  
M. Behr
Keyword(s):  
Head Injury ◽  
Injury Criteria ◽  
Head Injury Criteria ◽  
Pedestrian Accidents

Neurosurgical Management of Head Injury

2000 ◽  
Vol 10 (2) ◽  
pp. 16-20
Author(s):  
Ahmad Khaldi ◽  
Woodford Beach ◽  
Tobias Clausen ◽  
Ross Bullock
Keyword(s):  
Head Injury
Sign in / Sign up

Export Citation Format

Share Document