Deep learning methodology for predicting time history of head angular kinematics from simulated crash videos.

Head kinematics information is important as it is used to measure brain injury risk. Currently, head kinematics are measured using wearable devices or instrumentation mounted on the head. This paper evaluates the deep learning approach in predicting time history of head angular kinematics directly from videos without any instrumentation. To prove the concept, a deep learning model was developed for predicting time history of head angular velocities using finite element (FE) based crash simulation videos. This FE dataset was split into training, validation, and test datasets. A combined convolutional neural network and recurrent neural network based deep learning model was developed using the training and validations sets. The test (unseen) dataset was used to evaluate the predictive capability of the deep learning model. On the test dataset, correlation coefficient obtained between the actual and predicted peak angular velocities was 0.73, 0.85, and 0.92 for X, Y, and Z components respectively.

Development of brain injury criteria (BrIC).

Rotational motion of the head as a mechanism for brain injury was proposed back in the 1940s. Since then a multitude of research studies by various institutions were conducted to confirm/reject this hypothesis. Most of the studies were conducted on animals and concluded that rotational kinematics experienced by the animal's head may cause axonal deformations large enough to induce their functional deficit. Other studies utilized physical and mathematical models of human and animal heads to derive brain injury criteria based on deformation/pressure histories computed from their models. This study differs from the previous research in the following ways: first, it uses two different detailed mathematical models of human head (SIMon and GHBMC), each validated against various human brain response datasets; then establishes physical (strain and stress based) injury criteria for various types of brain injury based on scaled animal injury data; and finally, uses Anthropomorphic Test Devices (ATDs) (Hybrid III 50th Male, Hybrid III 5th Female, THOR 50th Male, ES-2re, SID-IIs, WorldSID 50th Male, and WorldSID 5th Female) test data (NCAP, pendulum, and frontal offset tests) to establish a kinematically based brain injury criterion (BrIC) for all ATDs. Similar procedures were applied to college football data where thousands of head impacts were recorded using a six degrees of freedom (6 DOF) instrumented helmet system. Since animal injury data used in derivation of BrIC were predominantly for diffuse axonal injury (DAI) type, which is currently an AIS 4+ injury, cumulative strain damage measure (CSDM) and maximum principal strain (MPS) were used to derive risk curves for AIS 4+ anatomic brain injuries. The AIS 1+, 2+, 3+, and 5+ risk curves for CSDM and MPS were then computed using the ratios between corresponding risk curves for head injury criterion (HIC) at a 50% risk. The risk curves for BrIC were then obtained from CSDM and MPS risk curves using the linear relationship between CSDM - BrIC and MPS - BrIC respectively. AIS 3+, 4+ and 5+ field risk of anatomic brain injuries was also estimated using the National Automotive Sampling System - Crashworthiness Data System (NASS-CDS) database for crash conditions similar to the frontal NCAP and side impact conditions that the ATDs were tested in. This was done to assess the risk curve ratios derived from HIC risk curves. The results of the study indicated that: (1) the two available human head models - SIMon and GHBMC - were found to be highly correlated when CSDMs and max principal strains were compared; (2) BrIC correlates best to both - CSDM and MPS, and rotational velocity (not rotational acceleration) is the mechanism for brain injuries; and (3) the critical values for angular velocity are directionally dependent, and are independent of the ATD used for measuring them. The newly developed brain injury criterion is a complement to the existing HIC, which is based on translational accelerations. Together, the two criteria may be able to capture most brain injuries and skull fractures occurring in automotive or any other impact environment. One of the main limitations for any brain injury criterion, including BrIC, is the lack of human injury data to validate the criteria against, although some approximation for AIS 2+ injury is given based on the angular velocities calculated at 50% probability of concussion in college football players instrumented with 5 DOF helmet system. Despite the limitations, a new kinematic rotational brain injury criterion - BrIC - may offer a way to capture brain injuries in situations when using translational accelerations based HIC alone may not be sufficient.

Finite element model of ocular injury in abusive head trauma.

PURPOSE: To develop a finite element analysis of the eye and orbit that can be subjected to virtual shaking forces. METHODS: LS-DYNA computer software was used to design a finite element model of the human infant eye, including orbit, fat, sclera, retina, vitreous, and muscles. The orbit was modeled as a rigid solid; the sclera and retina as elastic shells; the vitreous as viscoelastic solid or Newtonian fluid; and fat as elastic or viscoelastic solid. Muscles were modeled as spring-damper systems. Orbit-fat, fat-sclera, sclera-retina, and vitreous nodes-retina interfaces were defined with the use of the tied surface-surface function in LS-DYNA. The model was subjected to angular acceleration pulses obtained from shaking tests of a biofidelic doll (Aprica 2.5 kg dummy). Parametric studies were conducted to evaluate the effect of varying the material properties of vitreous/fat on maximum stress and stress distribution. RESULTS: With the vitreous modeled as a Newtonian fluid, the repeated acceleration-deceleration oscillatory motion characteristic of abusive head trauma (AHT) causes cumulative increases in the forces experienced at the vitreoretinal interface. Under these vitreous conditions, retinal stress maximums occur at the posterior pole and peripheral retina, where AHT retinal hemorrhage is most often found. CONCLUSIONS: Our model offers an improvement on dummy and animal models in allowing analysis of the effect of shaking on ocular tissues. It can be used under certain material conditions to demonstrate progressive "stacking" of intraocular stresses in locations corresponding to typical AHT injury patterns, allowing a better understanding of the mechanisms of retinal hemorrhage patterns.

Investigation of traumatic brain injuries using the next generation of simulated injury monitor (SIMon) finite element head model.

The objective of this study was to investigate potential for traumatic brain injuries (TBI) using a newly developed, geometrically detailed, finite element head model (FEHM) within the concept of a simulated injury monitor (SIMon). The new FEHM is comprised of several parts: cerebrum, cerebellum, falx, tentorium, combined pia-arachnoid complex (PAC) with cerebro-spinal fluid (CSF), ventricles, brainstem, and parasagittal blood vessels. The model's topology was derived from human computer tomography (CT) scans and then uniformly scaled such that the mass of the brain represents the mass of a 50th percentile male's brain (1.5 kg) with the total head mass of 4.5 kg. The topology of the model was then compared to the preliminary data on the average topology derived from Procrustes shape analysis of 59 individuals. Material properties of the various parts were assigned based on the latest experimental data. After rigorous validation of the model using neutral density targets (NDT) and pressure data, the stability of FEHM was tested by loading it simultaneously with translational (up to 400 g) combined with rotational (up to 24,000 rad/s2) acceleration pulses in both sagittal and coronal planes. Injury criteria were established in the manner shown in Takhounts et al. (2003a). After thorough validation and injury criteria establishment (cumulative strain damage measure--CSDM for diffuse axonal injuries (DAI), relative motion damage measure--RMDM for acute subdural hematoma (ASDH), and dilatational damage measure--DDM for contusions and focal lesions), the model was used in investigation of mild TBI cases in living humans based on a set of head impact data taken from American football players at the collegiate level. It was found that CSDM and especially RMDM correlated well with angular acceleration and angular velocity. DDM was close to zero for most impacts due to their mild severity implying that cavitational pressure anywhere in the brain was not reached. Maximum principal strain was found to correlate well with RMDM and angular head kinematic measures. Maximum principal stress didn't correlate with any kinematic measure or injury metric. The model was then used in the investigation of brain injury potential in NHTSA conducted side impact tests. It was also used in parametric investigations of various "what if" scenarios, such as side versus frontal impact, to establish a potential link between head kinematics and injury outcomes. The new SIMon FEHM offers an advantage over the previous version because it is geometrically more representative of the human head. This advantage, however, is made possible at the expense of additional computational time.