Caroline Deck

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

The objective of the present study is to synthesize and investigate using the same set of sixty-one real-world accidents the human head injury prediction capability of the head injury criterion (HIC) and the head impact power (HIP) based criterion as well as the injury mechanisms related criteria provided by the simulated injury monitor (SIMon) and the Louis Pasteur University (ULP) finite element head models. Each accident has been classified according to whether neurological injuries, subdural haematoma and skull fractures were reported. Furthermore, the accidents were reconstructed experimentally or numerically in order to provide loading conditions such as acceleration fields of the head or initial head impact conditions. Finally, thanks to this large statistical population of head trauma cases, injury risk curves were computed and the corresponding regression quality estimators permitted to check the correlation of the injury criteria with the injury occurrences. As different kinds of accidents were used, i.e. footballer, motorcyclist and pedestrian cases, the case-independency could also be checked. As a result, FE head modeling provides essential information on the intracranial mechanical behavior and, therefore, better injury criteria can be computed. It is clearly shown that moderate and severe neurological injuries can only be distinguished with a criterion that is computed using intracranial variables and not with the sole global head acceleration.

Improved head injury criteria based on head FE model

Head injury remains one of the most frequent and severe injuries sustained by vehicle occupants, motorcyclists, pedestrians and cyclists in road accidents and account for approximately 40% of road fatalities in the European Union (EU). One essential requirement for reducing the incidence of fatal and severe head injuries is to develop head injury assessment methods that can accurately and comprehensively assess the potential head injury risk under a broad range of head impact conditions. At present, the most widely accepted method of assessing head injury risk in road safety research is the Head Injury Criterion (HIC). However, HIC only considers the injury risk to the head resulting from linear head accelerations. In an attempt to develop improved head injury criteria for specific mechanisms, 68 head impact conditions that occurred in motor sport, motorcyclist, American football and pedestrian accidents were re-constructed with a state of the art finite element (FE) human head model (ULP head model). Statistical regression analysis was then carried out on the head loading parameters from the accidents, such as the peak linear and rotational acceleration of the head, and predictions from the head model, such as the Von Mises stress or strain and pressure in the brain, in order to determine which of the investigated parameters provided the most accurate metrics for the injuries sustained in the real world head trauma under consideration. The results show that Von Mises shearing strain within the brain is much better correlated with moderate Diffuse Axonal Injuries (DAI) as HIC or acceleration peaks are. For severe DAI, however, this improvement is less important. Another significant improvement of injury prediction based on FE head model is the one related to skull fracture, for which the proposed criteria present a higher correlation factor than HIC. Finally, SubDural Haematomas (SDH) are also better predicted with the FE model than HIC even if improvement is still needed for this injury mechanism.

Finite-element models of the human head and their applications in forensic practice

Since the 1960s, predictive human head impact indices have been developed to help the investigation of causation of human head injury. Finite-element models (FEM) can provide interesting tools for the forensic scientists when various human head injury mechanisms need to be evaluated. Human head FEMs are mainly used for car crash evaluations and are not in common use in forensic science. Recent technological progress has resulted in creating more simple tools, which will certainly help to consider the use of FEM in routine forensic practice in the coming years. This paper reviews the main FEMs developed and focuses on the models which can be used as predictive tools. Their possible applications in forensic medicine are discussed.

Development and validation of an advanced anisotropic visco-hyperelastic human brain FE model

This paper proposes the implementation of fractional anisotropy and axonal fiber orientation from diffusion tensor imaging (DTI) of 12 healthy patients into an existing human FE head model to develop a more realistic brain model with advanced constitutive laws. Further, the brain behavior was validated in terms of brain strain against experimental data published by Hardy et al. (2001, 2007) and for brain pressure against Nahum et al. (1977) experimental impacts. A reasonable agreement was observed between the simulation and experimental data. Results showed the feasibility of integrating axonal direction information into FE analysis and established the context of computation of axonal elongation in case of head trauma.

Experimental in vitro mechanical characterization of porcine Glisson's capsule and hepatic veins.

Understanding the mechanical properties of human liver is the most critical aspect of numerical modeling for medical applications and impact biomechanics. Many researchers work on identifying mechanical properties of the liver both in vivo and in vitro considering the high liver injury percentage in abdominal trauma and for easy detection of fatal liver diseases such as viral hepatitis, cirrhosis, etc. This study is performed to characterize mechanical properties of individual parts of the liver, namely Glissons capsule and hepatic veins, as these parts are rarely characterized separately. The long term objective of this study is to develop a realistic liver model by characterizing individual parts and later integrating them. In vitro uniaxial quasi-static tensile tests are done on fresh unfrozen porcine hepatic parts for large deformations at the rate of 0.1mm/s with a Bose Electroforce 3200 biomaterials test instrument. Results show that mean values of small strain and large strain elastic moduli are 8.22 ± 3.42 and 48.15 ± 4.5 MPa for Glissons capsule (30 samples) and 0.62 ± 0.41 and 2.81 ± 2.23 MPa for veins (20 samples), respectively, and are found to be in good agreement with data in the literature. Finally, a non-linear hyper-elastic constitutive law is proposed for the two separate liver constituents under study.

Multi-Directional Optimisation Against Biomechanical Criteria of a Head-Helmet Coupling

Abstract The main function of a helmet is to reduce or to avoid injuries that may occur to the head during an impact. Today, helmets are designed to reduce headform deceleration and not optimised to reduce head injury. The aim of this work is to optimise numerically a full face helmet finite element model (FEM) based on the dynamic behaviour of its components against biomechanical criteria. After a validation with a headform FEM as used in the experimental normative tests (ECE-R022/04), the helmet model was coupled with a previously developed FEM of the human head in order to predict intra-cranial field parameters (brain pressure, Von Mises stress and global energy of the cerebro- spinal-fluid) sustained during the normative impacts (frontal, lateral rear and top impact). Results show that normative impacts led some lesions; it is the reason we propose a solution to optimise the helmets mechanical parameters against intra-cerebral stress levels.

Anisotropic composite human skull model and skull fracture validation against temporo-parietal skull fracture.

A composite material model for skull, taking into account damage is implemented in the Strasbourg University finite element head model (SUFEHM) in order to enhance the existing skull mechanical constitutive law. The skull behavior is validated in terms of fracture patterns and contact forces by reconstructing 15 experimental cases. The new SUFEHM skull model is capable of reproducing skull fracture precisely. The composite skull model is validated not only for maximum forces, but also for lateral impact against actual force time curves from PMHS for the first time. Skull strain energy is found to be a pertinent parameter to predict the skull fracture and based on statistical (binary logistical regression) analysis it is observed that 50% risk of skull fracture occurred at skull strain energy of 544.0mJ.

Angular Impact Mitigation system for bicycle helmets to reduce head acceleration and risk of traumatic brain injury

Angular acceleration of the head is a known cause of traumatic brain injury (TBI), but contemporary bicycle helmets lack dedicated mechanisms to mitigate angular acceleration. A novel Angular Impact Mitigation (AIM) system for bicycle helmets has been developed that employs an elastically suspended aluminum honeycomb liner to absorb linear acceleration in normal impacts as well as angular acceleration in oblique impacts. This study tested bicycle helmets with and without AIM technology to comparatively assess impact mitigation. Normal impact tests were performed to measure linear head acceleration. Oblique impact tests were performed to measure angular head acceleration and neck loading. Furthermore, acceleration histories of oblique impacts were analyzed in a computational head model to predict the resulting risk of TBI in the form of concussion and diffuse axonal injury (DAI). Compared to standard helmets, AIM helmets resulted in a 14% reduction in peak linear acceleration (p<0.001), a 34% reduction in peak angular acceleration (p<0.001), and a 22-32% reduction in neck loading (p<0.001). Computational results predicted that AIM helmets reduced the risk of concussion and DAI by 27% and 44%, respectively. In conclusion, these results demonstrated that AIM technology could effectively improve impact mitigation compared to a contemporary expanded polystyrene-based bicycle helmet, and may enhance prevention of bicycle-related TBI. Further research is required.

A Study of Adult Pedestrian Head Impact Conditions and Injury Risks in Passenger Car Collisions Based on Real-World Accident Data

Objective: The aim of the current study was to study the kinematics of adult pedestrians and assess head injury risks based on real-world accidents. Methods: A total of 43 passenger car versus pedestrian accidents, in which the pedestrians head impacted the windscreen, were selected from accident databases for simulation study. According to real-world accident investigation, accident reconstructions were conducted using multibody system (MBS) pedestrian and car models under MADYMO environment (Strasbourg University) to calculate head impact conditions in terms of head impact velocity, head position, and head orientation. Pedestrian head impact conditions from MADYMO simulation results were then used to set the initial conditions in a simulation of a head striking a windscreen using finite element (FE) approach. Results: The results showed strong correlations between vehicle impact velocity and head contact time, throw distance, and head impact velocity using a quadratic regression model. In the selected samples, the results indicated that Abbreviated Injury Scale (AIS) 2+ and AIS 3+ severe head injuries with probability of 50 percent were caused by head impact velocity at about 33 and 49 km/h, respectively. Further, the predicted head linear acceleration (head injury criterion, HIC) value, resultant angular velocity, and resultant angular acceleration for 50 percent probability of AIS 2+ and AIS 3+ head injury risk were 116 g, 825, 40 rad/s, 11,368 rad/s2 and 162 g, 1442, 55 rad/s, 18,775 rad/s2, respectively, and the predicted value of 50 percent probability of skull fracture was 135 g. Conclusions: The present study provides new insight into pedestrian head impact conditions in terms of velocity, angle, and impact location based on a number of real-world cases. Therefore, it may perform a critical analysis for current pedestrian head standard tests.

Effects of pedestrian gait, vehicle-front geometry and impact velocity on kinematics of adult and child pedestrian head

The objective of the study is to investigate the effects of pedestrian gait, vehicle-front geometry and impact velocity on the dynamic responses of the head. The multi-body dynamic (MBD) models were used to simulate the head responses in vehicle to pedestrian collisions with different vehicle types in terms of head-impact point measured with Wrap Around Distance (WAD), head relative velocity and impact angle. Furthermore, the distribution of the head contact point on the vehicle fronts is analysed for a comparison of the contact point with the testing areas in the EEVC headform impactor test procedures. A simulation matrix is established using five vehicle types, two mathematical models of the pedestrians represented a 50th male adult and a 6-year-old child as well as seven pedestrian gaits based on typical postures in pedestrian accidents. In order to simulate a large range of impact conditions, four vehicle velocities (30 km/h, 40 km/h, 50 km/h and 60 km/h) are considered for each pedestrian position and vehicle type. The results indicated that the pedestrian gait and vehicle-impact velocity strongly influence head-impact condition. It is obvious that due to different vehicle-front geometry, the head-impact velocity, impact angle and head-impact point could be varied as well. This study provides suggestions for different head-impact conditions of subsystem test to the front part of different passenger vehicles.