Masami Iwamoto

Finite Element Analysis of Knee Injury Risks in Car-to-Pedestrian Impacts

In vehicle–pedestrian collisions, lower extremities of pedestrians are frequently injured by vehicle front structures. In this study, a finite element (FE) model of THUMS (total human model for safety) was modified in order to assess injuries to a pedestrian lower extremity. Dynamic impact responses of the knee joint of the FE model were validated on the basis of data from the literature. Since in real-world accidents, the vehicle bumper can impact the lower extremities in various situations, the relations between lower extremity injury risk and impact conditions, such as between impact location, angle, and impactor stiffness, were analyzed. The FE simulation demonstrated that the motion of the lower extremity may be classified into a contact effect of the impactor and an inertia effect from a thigh or leg. In the contact phase, the stress of the bone is high in the area contacted by the impactor, which can cause fracture. Thus, in this phase the impactor stiffness affects the fracture risk of bone. In the inertia phase, the behavior of the lower extremity depends on the impact locations and angles, and the knee ligament forces become high according to the lower extremity behavior. The force of the collateral ligament is high compared with other knee ligaments, due to knee valgus motions in vehicle-pedestrian collisions.

Analysis of traumatic brain injury due to primary head contact during vehicle-to-pedestrian impact

We developed a 50th-percentile American male pedestrian model including a detailed brain, and the mechanical responses and kinematic biofidelity predicted by this model were validated against the available cadaveric test data. Vehicle-to-pedestrian impact simulations were then performed to investigate a potential mechanism for traumatic brain injury resulting from a lateral blunt impact to the head. Due to inertia of the brain mass, it was found that the average traction force produced in the cervical spinal cord exceeded 50 N in the impact involving a sport utility vehicle and 25 N in the impact involving a sedan, when the striking vehicle was travelling at 40 km/h. This inertial loading may play a key role in a brainstem, or upper-cervical-cord, lesion occurring before head strike. Results of this study suggest that close attention should be paid to pedestrian kinematics during free flight even before the head makes primary contact with the striking vehicle.

Development of a Finite Element Model of the Human Lower Extremity for Assessing Automotive Crash Injury Potential

A finite element (FE) model of the human lower extremity has been developed and validated against published experimental data quasi-statically and dynamically. The calculated results indicate that the current FE model possesses reasonable biomechanical characteristics and adequate biofidelity in dorsiflexion behavior. In future work, it is hoped that the application of this model for realistic automotive crash analyses can increase not only better understanding of the injury mechanisms but also basic biomechanical data for further occupant protection.

Development and validation of a head/brain FE model and investigation of influential factor on the brain response during head impact

Higher brain dysfunction due to traumatic brain injury (TBI) caused by head rotational impact in traffic accidents is one of the most serious automotive safety problems. However, the injury mechanism still remains unclear. In this study, we developed two human head finite element (FE) models based on THUMS for further understanding of TBI mechanism. Parametric studies were performed to investigate the factors affecting brain tissue displacements and intracranial pressures during head impact by using these models. The mesh fineness, material properties of cerebrospinal fluid (CSF) and contact conditions between brain parenchyma and surrounding external organisation had little influence on validation accuracy against test data on brain responses of post mortem human subjects (PMHS). However, there were significant differences in the values of cumulative strain damage measure (CSDM) and the contours of strain distribution between these models. These findings have the potential for better understanding of TBI mechanism.

Effects of pre-impact body orientation on traumatic brain injury in a vehicle?pedestrian collision

A series of minivan-pedestrian collisions was simulated, and pre-impact body orientation was found to considerably affect the mechanical responses of injury predictors for traumatic brain injury (TBI). The maximum average traction force generated in the cervical spinal cord prior to head strike took its peak value when the pedestrian was subjected to purely lateral head rotation in a sideways collision and decreased by up to one-half in a symmetric manner as the pedestrian changed his direction toward or away from the vehicle. The intracranial strain concentration and the cumulative strain damage measure following the head strike increased by more than 60% as the initial pedestrian configuration changed from the backward to frontal collision. Since the outcome of injury predictors is closely associated with an initial body facing angle to the striking vehicle, regulatory impactor tests should consider the effects of pre-impact body orientation for accurately assessing real-world TBIs in the future.

Numerical Analysis of the Biomechanical Characteristics and Impact Response of the Human Chest

The mass density, Young’s modulus (E), tangent modulus (Et ) and yield stress (σy ) of the human ribs, sternum, internal organs and muscles play important roles when determining impact responses of the chest associated with pendulum impact. A series of parametric studies was conducted using a commercially available three-dimensional finite element (FE) model, Total HUman Model for Safety (THUMS) of the whole human body, to determine the effect of changing these material properties on the impact force, chest deflection, and the number of rib fractures and fractured ribs. Results from this parametric study indicate that the initial chest stiffness was mainly influenced by the mass density of the muscles covering the torso. The number of rib fractures and fractured ribs were primarily determined by E, Et and σy of the ribcage and sternum. Similarly, the E, Et and σy of the ribcage, which is defined as the bony skeleton of the chest, and sternum and E of the internal organs contributed to the maximum chest deflection in frontal impact, while the maximum chest deflection for lateral impact was mainly affected by the E, Et and σy of the ribcage.Copyright

Finite element analysis of biological soft tissue surrounded by a deformable membrane that controls transmembrane flow

BackgroundMany biological soft tissues are hydrated porous hyperelastic materials, which consist of a complex solid skeleton with fine voids and fluid filling these voids. Mechanical interactions between the solid and the fluid in hydrated porous tissues have been analyzed by finite element methods (FEMs) in which the mixture theory was introduced in various ways. Although most of the tissues are surrounded by deformable membranes that control transmembrane flows, the boundaries of the tissues have been treated as rigid and/or freely permeable in these studies. The purpose of this study was to develop a method for the analysis of hydrated porous hyperelastic tissues surrounded by deformable membranes that control transmembrane flows.ResultsFor this, we developed a new nonlinear finite element formulation of the mixture theory, where the nodal unknowns were the pore water pressure and solid displacement. This method allows the control of the fluid flow rate across the membrane using Neumann boundary condition. Using the method, we conducted a compression test of the hydrated porous hyperelastic tissue, which was surrounded by a flaccid impermeable membrane, and a part of the top surface of this tissue was pushed by a platen. The simulation results showed a stress relaxation phenomenon, resulting from the interaction between the elastic deformation of the tissue, pore water pressure gradient, and the movement of fluid. The results also showed that the fluid trapped by the impermeable membrane led to the swelling of the tissue around the platen.ConclusionsThese facts suggest that our new method can be effectively used for the analysis of a large deformation of hydrated porous hyperelastic material surrounded by a deformable membrane that controls transmembrane flow, and further investigations may allow more realistic analyses of the biological soft tissues, such as brain edema, brain trauma, the flow of blood and lymph in capillaries and pitting edema.

Muscle Synergy–Driven Robust Motion Control

Humans are able to robustly maintain desired motion and posture under dynamically changing circumstances, including novel conditions. To accomplish this, the brain needs to optimize the synergistic control between muscles against external dynamic factors. However, previous related studies have usually simplified the control of multiple muscles using two opposing muscles, which are minimum actuators to simulate linear feedback control. As a result, they have been unable to analyze how muscle synergy contributes to motion control robustness in a biological system. To address this issue, we considered a new muscle synergy concept used to optimize the synergy between muscle units against external dynamic conditions, including novel conditions. We propose that two main muscle control policies synergistically control muscle units to maintain the desired motion against external dynamic conditions. Our assumption is based on biological evidence regarding the control of multiple muscles via the corticospinal tract. One of the policies is the group control policy (GCP), which is used to control muscle group units classified based on functional similarities in joint control. This policy is used to effectively resist external dynamic circumstances, such as disturbances. The individual control policy (ICP) assists the GCP in precisely controlling motion by controlling individual muscle units. To validate this hypothesis, we simulated the reinforcement of the synergistic actions of the two control policies during the reinforcement learning of feedback motion control. Using this learning paradigm, the two control policies were synergistically combined to result in robust feedback control under novel transient and sustained disturbances that did not involve learning. Further, by comparing our data to experimental data generated by human subjects under the same conditions as those of the simulation, we showed that the proposed synergy concept may be used to analyze muscle synergy–driven motion control robustness in humans.

Modeling Passive and Active Muscles

Abstract Due to time delay of muscles to be activated, muscle activation is considered to have no significant effects on occupant motion or injury outcomes from high-speed impacts. However, due to the advanced technology that has been developed in recent years, active safety equipment reduces the velocity at which the human body is impacted. In such low-speed impacts, the impact duration increases, and therefore muscle activation has significant effects on occupant motions and injury outcomes. Thus, inclusion of active muscles is critical in modeling of the human body for injury prediction. Muscles have two primary characteristic properties in mechanics: passive and active. The passive property is tensile when the muscle is extended in the direction of the muscle fibers and compressive in the direction orthogonal to the fibers. Contractile elements are used to represent muscles that are activated. In this chapter, we outline the anatomical structures and physiological functions of passive and active muscles and how the muscles can be modeled. This chapter also describes the application of muscle models for injury biomechanics and how the muscle can be activated in simulations. The primary focus is on injury during automotive accidents.

Human Brain Modeling with Its Anatomical Structure and Realistic Material Properties for Brain Injury Prediction

Impairments of executive brain function after traumatic brain injury (TBI) due to head impacts in traffic accidents need to be obviated. Finite element (FE) analyses with a human brain model facilitate understanding of the TBI mechanisms. However, conventional brain FE models do not suitably describe the anatomical structure in the deep brain, which is a critical region for executive brain function, and the material properties of brain parenchyma. In this study, for better TBI prediction, a novel brain FE model with anatomical structure in the deep brain was developed. The developed model comprises a constitutive model of brain parenchyma considering anisotropy and strain rate dependency. Validation was performed against postmortem human subject test data associated with brain deformation during head impact. Brain injury analyses were performed using head acceleration curves obtained from reconstruction analysis of rear-end collision with a human whole-body FE model. The difference in structure was found to affect the regions of strain concentration, while the difference in material model contributed to the peak strain value. The injury prediction result by the proposed model was consistent with the characteristics in the neuroimaging data of TBI patients due to traffic accidents.