Assessment of the sensitivity of thoracic injury criteria to subject-specific characteristics using human body models.

Introduction: Chest deformation has been proposed as the best predictor of thoracic injury risk in frontal impacts. Finite Element Human Body Models (FE-HBM) can enhance the results obtained in physical crash tests with Anthropometric Test Devices (ATD) since they can be exposed to omnidirectional impacts and their geometry can be modified to reflect specific population groups. This study aims to assess the sensitivity of two thoracic injury risk criteria (PC Score and Cmax) to several personalization techniques of FE-HBMs. Methods: Three 30° nearside oblique sled tests were reproduced using the SAFER HBM v8 and three personalization techniques were applied to this model to evaluate the influence on the risk of thoracic injuries. First, the overall mass of the model was adjusted to represent the weight of the subjects. Second, the model anthropometry and mass were modified to represent the characteristics of the post-mortem human subjects (PMHS). Finally, the spine alignment of the model was adapted to the PMHS posture at t = 0 ms, to conform to the angles between spinal landmarks measured in the PMHS. The following two metrics were used to predict three or more fractured ribs (AIS3+) of the SAFER HBM v8 and the effect of personalization techniques: the maximum posterior displacement of any studied chest point (Cmax), and the sum of the upper and lower deformation of selected rib points (PC score). Results: Despite having led to statistically significant differences in the probability of AIS3+ calculations, the mass-scaled and morphed version provided, in general, lower values for injury risk than the baseline model and the postured version being the latter, which exhibited the better approximation to the PMHS tests in terms of probability of injury. Additionally, this study found that the prediction of AIS3+ chest injuries based on PC Score resulted in higher probability values than the prediction based on Cmax for the loading conditions and personalization techniques analyzed within this study. Discussion: This study could demonstrate that the personalization techniques do not lead to linear trends when they are used in combination. Furthermore, the results included here suggest that these two criteria will result in significantly different predictions if the chest is loaded more asymmetrically.

Injury Metrics for Assessing the Risk of Acute Subdural Hematoma in Traumatic Events.

Worldwide, the ocurrence of acute subdural hematomas (ASDHs) in road traffic crashes is a major public health problem. ASDHs are usually produced by loss of structural integrity of one of the cerebral bridging veins (CBVs) linking the parasagittal sinus to the brain. Therefore, to assess the risk of ASDH it is important to know the mechanical conditions to which the CBVs are subjected during a potentially traumatic event (such as a traffic accident or a fall from height). Recently, new studies on CBVs have been published allowing much more accurate prediction of the likelihood of mechanical failure of CBVs. These new data can be used to propose new damage metrics, which make more accurate predictions about the probability of occurrence of ASDH in road crashes. This would allow a better assessement of the effects of passive safety countermeasures and, consequently, to improve vehicle restraint systems. Currently, some widely used damage metrics are based on partially obsolete data and measurements of the mechanical behavior of CBVs that have not been confirmed by subsequent studies. This paper proposes a revision of some existing metrics and constructs a new metric based on more accurate recent data on the mechanical failure of human CBVs.

Experimental and Numerical Assessment of Supporting Road Signs Masts Family for Compliance with the Standard EN 12767.

The analysis aimed to assess the passive safety of supporting masts for road signs in accordance with EN 12767. Experimental tests were carried out based on the requirements of the standard for the smallest and the largest constructions within the product family. Numerical models of crash tests were prepared for whole product family using the Finite Element Method in the LS-Dyna environment. Based on the comparison of the experimental tests and the numerical calculations, the usefulness of the numerical model for estimating the actual value of the Acceleration Severity Index (ASI) and the Theoretical Head Impact Velocity (THIV) was assessed. With the use of these relationships the values of ASI and THIV for masts not tested experimentally were estimated. It was confirmed that the analyzed masts met the requirements for the passive safety of structures set out in the standard EN 12767. It was possible since as a result of the impact, the mast column detached from the base, allowing the vehicle to continue moving. The behavior of the masts was primarily influenced by the destruction of the safety connectors. The paper presents the most important elements from the point of view of designing such solutions.

Low-Rank Representation of Head Impact Kinematics: A Data-Driven Emulator.

Head motion induced by impacts has been deemed as one of the most important measures in brain injury prediction, given that the vast majority of brain injury metrics use head kinematics as input. Recently, researchers have focused on using fast approaches, such as machine learning, to approximate brain deformation in real time for early brain injury diagnosis. However, training such models requires large number of kinematic measurements, and therefore data augmentation is required given the limited on-field measured data available. In this study we present a principal component analysis-based method that emulates an empirical low-rank substitution for head impact kinematics, while requiring low computational cost. In characterizing our existing data set of 537 head impacts, each consisting of 6 degrees of freedom measurements, we found that only a few modes, e.g., 15 in the case of angular velocity, is sufficient for accurate reconstruction of the entire data set. Furthermore, these modes are predominantly low frequency since over 70% of the angular velocity response can be captured by modes that have frequencies under 40 Hz. We compared our proposed method against existing impact parametrization methods and showed significantly better performance in injury prediction using a range of kinematic-based metrics-such as head injury criterion (HIC), rotational injury criterion (RIC), and brain injury metric (BrIC)-and brain tissue deformation-based metrics-such as brain angle metric (BAM), maximum principal strain (MPS), and axonal fiber strains (FS). In all cases, our approach reproduced injury metrics similar to the ground truth measurements with no significant difference, whereas the existing methods obtained significantly different (p < 0.01) values as well as substantial differences in injury classification sensitivity and specificity. This emulator will enable us to provide the necessary data augmentation to build a head impact kinematic data set of any size. The emulator and corresponding examples are available on our website.

A Novel Testing Device to Assess the Effect of Neck Strength on Risk of Concussion.

Concussion awareness has become more prevalent in the past decade, leading to growing calls for prevention programs such as neck strengthening. However, previous research work has shown that not all training programs have been effective, and there is a need for a reliable testing device to measure cervical strength dynamically before and after training. Therefore, this work proposes a novel Concussion Active Prevention Testing Device composed of inertial measurement units mounted on the head and a custom-designed frame to measure head kinematics during controlled sub-concussive impacts. Through an experimental study with able-bodied participants, the proposed testing device demonstrated high intra-participant repeatability between waveforms of the head acceleration and angular velocity in the sagittal plane (multiple correlation coefficient of 80%). Similarly, good and excellent intra-class correlation coefficients were obtained for head injury metrics, including range, peak, Gadd severity index, head injury criterion, and range of motion. Finally, the results showed that significantly higher head injury metrics were measured for female participants, which was in line with the findings of previous research works. We conclude that the proposed testing device can be used to measure repeatable and informative metrics for evaluating the effectiveness of athletes' neck strengthening program.

Compression-based injury variables from chestbands in far-side impact THOR sled tests.

Objective: This study seeks to determine compression (Cmax) and compression-related injury variables (velocity and viscous injury criterion: Vmax and VCmax) from chestband data in pure lateral and oblique far-side impact sled tests.Methods: The 3-point belt-restrained mid-sized male Test Device for Human Occupant Restraint (THOR) dummy was placed on a buck and subjected to side impacts with and without center-mounted airbags. The change in velocity was 8.3 m/s for all conditions. Two chestbands were routed around the outer circumference of the THOR at the levels of the third and sixth ribs. Maximum chest deflections were computed using strain gauge signals from the chestbands and their temporal contours. Three methods were used to determine deflection metrics. The first method paralleled methods used in previously published human cadaver studies; the second method used the actual anchor point location and actual alignment of the dummy's internal sensors; and the third method used the anchor location of the internal sensor but determined the sensor's locations on the contour confining to the aspect of the sensor. These 3 approaches are abbreviated as the SD, ID, and TD variables. The injury variables Cmax, Vmax, and VCmax were determined according to accepted procedures. Their peak magnitudes were extracted and an evaluation of their accuracy was made based on the SD method.Results: The average SD-based Cmax magnitudes for the upper and lower chest levels were 0.12 and 0.17 m/s, the Vmax magnitudes were 5.3 and 1.8 m/s, and the VCmax magnitudes were 0.24 and 0.15 m/s, respectively. Other data are given for all variables at the 2 levels of the thorax in the body of this paper. The ID-based peak variables were the lowest, and this observation was true regardless of the aspect, right or left side. In contrast, the SD method produced the greatest magnitudes of the variables. The VCmax variable had the greatest normalized difference among all 3 injury variables.Conclusions: Though the present study is limited in scope, the predetermined placement of the internal sensors in the THOR dummy underpredicted chest deflection-related injury variables, and the viscous criterion was the least reliable variable in these lateral and oblique far-side impact sled tests.

Traumatic brain injury in pedestrian-vehicle collisions: Convexity and suitability of some functionals used as injury metrics.

BACKGROUND AND OBJECTIVE: Abrupt accelerations or decelerations can cause large strain in brain tissues and, consequently, different forms of Traumatic Brain Injury (TBI). In order to predict the effect of the accelerations on the soft tissues of the brain, many different injury metrics have been proposed (typically, an injury metric is a real valued functional of the accelerations). The objective of this article is to make a formal and empirical comparison, in order to identify general criteria for reasonable injury metrics, and propose a general guideline to avoid ill-proposed injury metrics. METHODS: A medium-sized sample of vehicle-pedestrian collisions, from Post Mortem Human Subject (PMHS) tests, is analyzed. A statistical study has been conducted in order to determine the discriminant power of the usual metrics. We use Principal Component Analysis to reduce dimensionality and to check consistency among the different metrics. In addition, this article compares the mathematical properties of some of these functionals, trying to identify the desirable properties that any of those functionals needs to fulfill in order to be useful for optimization. RESULTS: We have found a pair-wise consistency of all the currently used metrics (any two injury metrics are always positively related). In addition, we observed that two independent principal factors explain about 72.5% of the observed variance among all collision tests. This is remarkable because it indicates that despite high number of different injury metrics, a reduced number of variables can explain the results of all these metrics. With regard to the formal properties, we found that essentially all injury mechanisms can be accounted by means of scalable, differentiable and convex functionals (we propose to call minimization suitable injury metric any metric having these three formal properties). In addition three useful functionals, usable as injury metrics, are identified on the basis of the empirical comparisons. CONCLUSIONS: The commonly used metrics are highly consistent, but also highly redundant. Formal minimal conditions of a reasonable injury metric has been identified. Future proposals of injury metrics can benefit from the results of this study.

Injury prediction in a side impact crash using human body model simulation.

BACKGROUND: Improved understanding of the occupant loading conditions in real world crashes is critical for injury prevention and new vehicle design. The purpose of this study was to develop a robust methodology to reconstruct injuries sustained in real world crashes using vehicle and human body finite element models. METHODS: A real world near-side impact crash was selected from the Crash Injury Research and Engineering Network (CIREN) database. An average sedan was struck at approximately the B-pillar with a 290 degree principal direction of force by a lightweight pickup truck, resulting in a maximum crush of 45 cm and a crash reconstruction derived Delta-V of 28 kph. The belted 73-year-old midsized female driver sustained severe thoracic injuries, serious brain injuries, moderate abdominal injuries, and no pelvic injury. Vehicle finite element models were selected to reconstruct the crash. The bullet vehicle parameters were heuristically optimized to match the crush profile of the simulated struck vehicle and the case vehicle. The Total Human Model for Safety (THUMS) midsized male finite element model of the human body was used to represent the case occupant and reconstruct her injuries using the head injury criterion (HIC), half deflection, thoracic trauma index (TTI), and pelvic force to predict injury risk. A variation study was conducted to evaluate the robustness of the injury predictions by varying the bullet vehicle parameters. RESULTS: The THUMS thoracic injury metrics resulted in a calculated risk exceeding 90% for AIS3+ injuries and 70% risk of AIS4+ injuries, consistent with her thoracic injury outcome. The THUMS model predicted seven rib fractures compared to the case occupant's 11 rib fractures, which are both AIS3 injuries. The pelvic injury risk for AIS2+ and AIS3+ injuries were 37% and 2.6%, respectively, consistent with the absence of pelvic injury. The THUMS injury prediction metrics were most sensitive to bullet vehicle location. The maximum 95% confidence interval width for the mean injury metrics was only 5% demonstrating high confidence in the THUMS injury prediction. CONCLUSIONS: This study demonstrates a variation study methodology in which human body models can be reliably used to robustly predict injury probability consistent with real world crash injury outcome.