Investigation of Brain Trauma Biomechanics in Vehicle Traffic Accidents Using Human Body Computational Models

2011 ◽  
pp. 5-14 ◽  
Author(s):  
Jikuang Yang
Keyword(s):  
Human Body ◽  
Traffic Accidents ◽  
Computational Models ◽  
Brain Trauma ◽  
Vehicle Traffic

Modeling and Reconstruction of Multi-Fidelity Traumatic Head Injury due to Blunt Impact

2017 ◽  
Author(s):  
X. Gary Tan ◽  
Amit Bagchi
Keyword(s):  
Brain Injury ◽  
Human Body ◽  
Computational Models ◽  
Head Model ◽  
High Fidelity ◽  
Human Body Model ◽  
Body Model ◽  
Blunt Impact ◽  
Real Scenario ◽  
The Brain

Traumatic brain injury (TBI) is one of the most common injuries to service members in recent conflicts. Computational models can offer insights in understanding the underlying mechanism of brain injury, which lead to the crucial development of effective personal protective equipment designed to prevent or mitigate the TBI. Historically many computational models were developed for the brain injury study. However, these models use relatively coarse mesh with a less detailed head anatomy. Many models consider the head only and thus cannot properly model the real scenario, i.e., accidental fall, blunt impact or blast loading. A whole-body finite element model can represent the real scenario but is very expensive to use. By combining the high-fidelity human head model with an articulated human body model, we developed the computational multi-fidelity human models to investigate the blunt- and blast-related TBI efficiently. A high-fidelity computational head model was generated from the high resolution image data to accurately reproduce the complex musculoskeletal and tissue structure of the head. The fast-running articulated human body model is based on the multi-body dynamics and was used to reconstruct the accidental falls. By utilizing the kinematics and force and moment at the joint of the articulated human body model, we can realistically simulate the blunt impact and assess the brain injury using the high-fidelity head model.

DEVELOPMENT AND BIOFIDELITY EVALUATION OF AN OCCUPANT BIOMECHANICAL MODEL OF A CHINESE 50TH PERCENTILE MALE FOR SIDE IMPACT

2017 ◽  
Vol 17 (07) ◽  
pp. 1740039 ◽  
Author(s):  
ZHENGWEI MA ◽  
LELE JING ◽  
FENGCHONG LAN ◽  
JINLUN WANG ◽  
JIQING CHEN
Keyword(s):  
Finite Element ◽  
Human Body ◽  
Computational Models ◽  
Rating Scale ◽  
Element Model ◽  
Side Impact ◽  
Body Parts ◽  
Lateral Impact ◽  
Human Body Model ◽  
Body Model

Finite element modeling has played a significant role in the study of human body biomechanical responses and injury mechanisms during vehicle impacts. However, there are very few reports on similar studies conducted in China for the Chinese population. In this study, a high-precision human body finite element model of the Chinese 50th percentile male was developed. The anatomical structures and mechanical characteristics of real human body were replicated as precise as possible. In order to analyze the model’s biofidelity in side-impact injury prediction, a global technical standard, ISO/TR 9790, was used that specifically assesses the lateral impact biofidelity of anthropomorphic test devices (ATDs) and computational models. A series of model simulations, focusing on different body parts, were carried out against the tests outlined in ISO/TR 9790. Then, the biofidelity ratings of the full human body model and different body parts were evaluated using the ISO/TR 9790 rating method. In a 0–10 rating scale, the resulting rating for the full human body model developed is 8.57, which means a good biofidelity. As to different body parts, the biofidelity ratings of the head and shoulder are excellent, while those of the neck, thorax, abdomen and pelvis are good. The resulting ratings indicate that the human body model developed in this study is capable of investigating the side-impact responses of and injuries to occupants’ different body parts. In addition, the rating of the model was compared with those of the other human body finite element models and several side-impact dummy models. This allows us to assess the robustness of our model and to identify necessary improvements.

Computational Models on Graphs for the Nonlinear Hyperbolic System of Equations

2004 ◽  
Author(s):  
Alexander S. Kholodov ◽  
Yaroslav A. Kholodov
Keyword(s):  
Hyperbolic System ◽  
Human Body ◽  
Partial Derivative ◽  
Computational Models ◽  
Heavy Traffic ◽  
Large River ◽  
System Of Equations ◽  
Information Flows ◽  
Flood Water ◽  
Nonlinear Hyperbolic System

The problems in the form of nonlinear partial derivative equations on graphs (nets, trees) arise in different applications. As the examples of such models we can name the circulatory and respiratory systems of the human body, the model of heavy traffic in the big cities, the model of flood water and pollution propagation in the large river systems, the model of bar structures and frames behavior under the different impacts, the model of the intensive information flows in the computer networks and others.

Risk factors of fatality in motor vehicle traffic accidents

1994 ◽  
Vol 26 (3) ◽  
pp. 391-397 ◽  
Author(s):  
Akira Shibata ◽  
Katsuhiro Fukuda
Keyword(s):  
Risk Factors ◽  
Traffic Accidents ◽  
Motor Vehicle ◽  
Vehicle Traffic

Hospital-based emergency department visits in children with motor vehicle traffic accidents: estimates from the nationwide emergency department sample

2014 ◽  
Vol 11 (3) ◽  
pp. 261-266 ◽  
Author(s):  
Veerajalandhar Allareddy ◽  
Ingrid M. Anderson ◽  
Min Kyeong Lee ◽  
Veerasathpurush Allareddy ◽  
Sankeerth Rampa ◽  
...  
Keyword(s):  
Emergency Department ◽  
Traffic Accidents ◽  
Motor Vehicle ◽  
Emergency Department Visits ◽  
Vehicle Traffic

Patterns of injuries and motor-vehicle traffic accidents in Athens

2006 ◽  
Vol 13 (3) ◽  
pp. 190-193 ◽  
Author(s):  
E. Pikoulis ◽  
V. Filias ◽  
N. Pikoulis ◽  
P. Daskalakis ◽  
E. D. Avgerinos ◽  
...  
Keyword(s):  
Traffic Accidents ◽  
Motor Vehicle ◽  
Vehicle Traffic

Multiple-vehicle traffic accidents in Hong Kong

2006 ◽  
Vol 38 (6) ◽  
pp. 1157-1161 ◽  
Author(s):  
Kelvin K.W. Yau ◽  
H.P. Lo ◽  
Sherrice H.H. Fung
Keyword(s):  
Hong Kong ◽  
Traffic Accidents ◽  
Vehicle Traffic

scholarly journals A Hybrid Deep Learning Model for Recognizing Actions of Distracted Drivers

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7424
Author(s):  
Shuang-Jian Jiao ◽  
Lin-Yao Liu ◽  
Qian Liu
Keyword(s):  
Deep Learning ◽  
Human Body ◽  
Traffic Accidents ◽  
Clustering Algorithm ◽  
Short Term Memory ◽  
Learning Model ◽  
Cluster Center ◽  
Navigation Systems ◽  
Driver Assistance Systems ◽  
Deep Learning Model

With the rapid spreading of in-vehicle information systems such as smartphones, navigation systems, and radios, the number of traffic accidents caused by driver distractions shows an increasing trend. Timely identification and warning are deemed to be crucial for distracted driving and the establishment of driver assistance systems is of great value. However, almost all research on the recognition of the driver’s distracted actions using computer vision methods neglected the importance of temporal information for action recognition. This paper proposes a hybrid deep learning model for recognizing the actions of distracted drivers. Specifically, we used OpenPose to obtain skeleton information of the human body and then constructed the vector angle and modulus ratio of the human body structure as features to describe the driver’s actions, thereby realizing the fusion of deep network features and artificial features, which improve the information density of spatial features. The K-means clustering algorithm was used to preselect the original frames, and the method of inter-frame comparison was used to obtain the final keyframe sequence by comparing the Euclidean distance between manually constructed vectors representing frames and the vector representing the cluster center. Finally, we constructed a two-layer long short-term memory neural network to obtain more effective spatiotemporal features, and one softmax layer to identify the distracted driver’s action. The experimental results based on the collected dataset prove the effectiveness of this framework, and it can provide a theoretical basis for the establishment of vehicle distraction warning systems.

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