The Contribution of Pre-impact Posture on Restrained Occupant Finite Element Model Response in Frontal Impact

Objective: The objective of this study was to discuss the influence of the pre-impact posture to the response of a finite element human body model (HBM) in frontal impacts.

Methods: This study uses previously published cadaveric tests (PMHS), which measured six realistic pre-impact postures. Seven postured models were created from the THUMS occupant model (v4.0): one matching the standard UMTRI driving posture as it was the target posture in the experiments, and six matching the measured pre-impact postures. The same measurements as those obtained during the cadaveric tests were calculated from the simulations, and biofidelity metrics based on signals correlation (CORA) were established to compare the response of the seven models to the experiments.

Results: The HBM responses showed good agreement with the PMHS responses for the reaction forces (CORA = 0.80 ± 0.05) and the kinematics of the lower part of the torso but only fair correlation was found with the head, the upper spine, rib strains (CORA= 0.50 ± 0.05) and chest deflections (CORA = 0.67 ± 0.08). All models sustained rib fractures, sternal fracture and clavicle fracture. The average number of rib fractures for all the models was 5.3 ± 1.0, lower than in the experiments (10.8 ± 9.0).

Variation in pre-impact posture greatly altered the time histories of the reaction forces, deflections and the rib strains, mainly in terms of time delay, but no definite improvement in HBM response or injury prediction was observed. By modifying only the posture of the HBM, the variability in the impact response was found to be equivalent to that observed in the experiments. The postured HBM sustained from 4 to 8 rib fractures, confirming that the pre-impact posture influenced the injury outcome predicted by the simulation.

Conclusions: This study tries to answer an important question: what is the effect of occupant posture on kinematics and kinetics. Significant differences in kinematics observed between HBM and PMHS suggesting more coupling between the pelvis and the spine for the models which makes the model response very sensitive to any variation in the spine posture. Consequently, the findings observed for the HBM cannot be extended to PMHS. Besides, pre-impact posture should be carefully quantified during experiments and the evaluation of HBM should take into account the variation in the predicted impact response due to the variation in the model posture.     

Estimated Injury Risk for Specific Injuries and Body Regions in Frontal Motor Vehicle Crashes

Objective: Injury risk curves estimate motor vehicle crash (MVC) occupant injury risk from vehicle, crash, and/or occupant factors. Many vehicles are equipped with event data recorders (EDRs) that collect data including the crash speed and restraint status during a MVC. This study's goal was to use regulation-required data elements for EDRs to compute occupant injury risk for (1) specific injuries and (2) specific body regions in frontal MVCs from weighted NASS-CDS data.

Methods: Logistic regression analysis of NASS-CDS single-impact frontal MVCs involving front seat occupants with frontal airbag deployment was used to produce 23 risk curves for specific injuries and 17 risk curves for Abbreviated Injury Scale (AIS) 2+ to 5+ body region injuries. Risk curves were produced for the following body regions: head and thorax (AIS 2+, 3+, 4+, 5+), face (AIS 2+), abdomen, spine, upper extremity, and lower extremity (AIS 2+, 3+). Injury risk with 95% confidence intervals was estimated for 15–105 km/h longitudinal delta-Vs and belt status was adjusted for as a covariate.

Results: Overall, belted occupants had lower estimated risks compared to unbelted occupants and the risk of injury increased as longitudinal delta-V increased. Belt status was a significant predictor for 13 specific injuries and all body region injuries with the exception of AIS 2+ and 3+ spine injuries. Specific injuries and body region injuries that occurred more frequently in NASS-CDS also tended to carry higher risks when evaluated at a 56 km/h longitudinal delta-V. In the belted population, injury risks that ranked in the top 33% included 4 upper extremity fractures (ulna, radius, clavicle, carpus/metacarpus), 2 lower extremity fractures (fibula, metatarsal/tarsal), and a knee sprain (2.4–4.6% risk). Unbelted injury risks ranked in the top 33% included 4 lower extremity fractures (femur, fibula, metatarsal/tarsal, patella), 2 head injuries with less than one hour or unspecified prior unconsciousness, and a lung contusion (4.6–9.9% risk). The 6 body region curves with the highest risks were for AIS 2+ lower extremity, upper extremity, thorax, and head injury and AIS 3+ lower extremity and thorax injury (15.9–43.8% risk).

Conclusions: These injury risk curves can be implemented into advanced automatic crash notification (AACN) algorithms that utilize vehicle EDR measurements to predict occupant injury immediately following a MVC. Through integration with AACN, these injury risk curves can provide emergency medical services (EMS) and other patient care providers with information on suspected occupant injuries to improve injury detection and patient triage.     

Development and Validation of the Total HUman Model for Safety (THUMS) Toward Further Understanding of Occupant Injury Mechanisms in Precrash and During Crash

Objective: Active safety devices such as automatic emergency brake (AEB) and precrash seat belt have the potential to accomplish further reduction in the number of the fatalities due to automotive accidents. However, their effectiveness should be investigated by more accurate estimations of their interaction with human bodies. Computational human body models are suitable for investigation, especially considering muscular tone effects on occupant motions and injury outcomes. However, the conventional modeling approaches such as multibody models and detailed finite element (FE) models have advantages and disadvantages in computational costs and injury predictions considering muscular tone effects. The objective of this study is to develop and validate a human body FE model with whole body muscles, which can be used for the detailed investigation of interaction between human bodies and vehicular structures including some safety devices precrash and during a crash with relatively low computational costs.

Methods: In this study, we developed a human body FE model called THUMS (Total HUman Model for Safety) with a body size of 50th percentile adult male (AM50) and a sitting posture. The model has anatomical structures of bones, ligaments, muscles, brain, and internal organs. The total number of elements is 281,260, which would realize relatively low computational costs. Deformable material models were assigned to all body parts. The muscle–tendon complexes were modeled by truss elements with Hill-type muscle material and seat belt elements with tension-only material. The THUMS was validated against 35 series of cadaver or volunteer test data on frontal, lateral, and rear impacts. Model validations for 15 series of cadaver test data associated with frontal impacts are presented in this article. The THUMS with a vehicle sled model was applied to investigate effects of muscle activations on occupant kinematics and injury outcomes in specific frontal impact situations with AEB.

Results and Conclusions: In the validations using 5 series of cadaver test data, force–time curves predicted by the THUMS were quantitatively evaluated using correlation and analysis (CORA), which showed good or acceptable agreement with cadaver test data in most cases. The investigation of muscular effects showed that muscle activation levels and timing had significant effects on occupant kinematics and injury outcomes. Although further studies on accident injury reconstruction are needed, the THUMS has the potential for predictions of occupant kinematics and injury outcomes considering muscular tone effects with relatively low computational costs.     

Lower Leg Injury Reference Values and Risk Curves from Survival Analysis for Male and Female Dummies: Meta-analysis of Postmortem Human Subject Tests

Objective: Derive lower leg injury risk functions using survival analysis and determine injury reference values (IRV) applicable to human mid-size male and small-size female anthropometries by conducting a meta-analysis of experimental data from different studies under axial impact loading to the foot–ankle–leg complex.

Methods: Specimen-specific dynamic peak force, age, total body mass, and injury data were obtained from tests conducted by applying the external load to the dorsal surface of the foot of postmortem human subject (PMHS) foot–ankle–leg preparations. Calcaneus and/or tibia injuries, alone or in combination and with/without involvement of adjacent articular complexes, were included in the injury group. Injury and noninjury tests were included. Maximum axial loads recorded by a load cell attached to the proximal end of the preparation were used. Data were analyzed by treating force as the primary variable. Age was considered as the covariate. Data were censored based on the number of tests conducted on each specimen and whether it remained intact or sustained injury; that is, right, left, and interval censoring. The best fits from different distributions were based on the Akaike information criterion; mean and plus and minus 95% confidence intervals were obtained; and normalized confidence interval sizes (quality indices) were determined at 5, 10, 25, and 50% risk levels. The normalization was based on the mean curve. Using human-equivalent age as 45 years, data were normalized and risk curves were developed for the 50th and 5th percentile human size of the dummies.

Results: Out of the available 114 tests (76 fracture and 38 no injury) from 5 groups of experiments, survival analysis was carried out using 3 groups consisting of 62 tests (35 fracture and 27 no injury). Peak forces associated with 4 specific risk levels at 25, 45, and 65 years of age are given along with probability curves (mean and plus and minus 95% confidence intervals) for PMHS and normalized data applicable to male and female dummies. Quality indices increased (less tightness-of-fit) with decreasing age and risk level for all age groups and these data are given for all chosen risk levels.

Conclusions: These PMHS-based probability distributions at different ages using information from different groups of researchers constituting the largest body of data can be used as human tolerances to lower leg injury from axial loading. Decreasing quality indices (increasing index value) at lower probabilities suggest the need for additional tests. The anthropometry-specific mid-size male and small-size female mean human risk curves along with plus and minus 95% confidence intervals from survival analysis and associated IRV data can be used as a first step in studies aimed at advancing occupant safety in automotive and other environments.     

Effect of Aging on Brain Injury Prediction in Rotational Head Trauma—A Parameter Study with a Rat Finite Element Model

Objective: The aim of this study was to investigate the possible effects of age-related intracranial changes on the potential outcome of diffuse axonal injuries and acute subdural hematoma under rotational head loading.

Methods: A simulation-based parametric study was conducted using an updated and validated finite element model of a rat head. The validation included a comparison of predicted brain cortex sliding with respect to the skull. Further, model material properties were modified to account for aging; predicted tissue strains were compared with experimental data in which groups of rats in 2 different lifecycle stages, young adult and mature adult, were subjected to rotational trauma. For the parameter study, 2 age-dependent factors—brain volume and region-specific brain material properties—were implemented into the model. The models young adult and old age were subjected to several injurious and subinjurious sagittal plane rotational acceleration levels.

Results: Sequential analysis of the simulated trauma progression indicates that an increase in acute subdural hematoma injury risk indicator occurs at an early stage of the trauma, whereas an increase in diffuse axonal injury risk indicators occurs at a later stage. Tissue stiffening from young adult to mature adult rats produced an increase in strain-based thresholds accompanied by a wider spread of strain distribution toward the rear part of the brain, consistent with rotational trauma experiments with young adult and mature adult rats. Young adult to old age brain tissue softening and brain atrophy resulted in an increase in diffuse axonal injuries and acute subdural hematoma injury risk indicators, respectively.

Conclusions: The findings presented in this study suggest that age-specific injury thresholds should be developed to enable the development of superior restraint systems for the elderly. The findings also motivate other further studies on age-dependency of head trauma.     

New Methodology for an Expert-Designed Map From International Classification of Diseases (ICD) to Abbreviated Injury Scale (AIS) 3+ Severity Injury

Objective: There has been a longstanding desire for a map to convert International Classification of Diseases (ICD) injury codes to Abbreviated Injury Scale (AIS) codes to reflect the severity of those diagnoses. The Association for the Advancement of Automotive Medicine (AAAM) was tasked by European Union representatives to create a categorical map classifying diagnoses codes as serious injury (Abbreviated Injury Scale [AIS] 3+), minor/moderate injury (AIS 1/2), or indeterminate. This study's objective was to map injury-related ICD-9-CM (clinical modification) and ICD-10-CM codes to these severity categories.

Methods: Approximately 19,000 ICD codes were mapped, including injuries from the following categories: amputations, blood vessel injury, burns, crushing injury, dislocations/sprains/strains, foreign body, fractures, internal organ, nerve/spinal cord injury, intracranial, laceration, open wounds, and superficial injury/contusion. Two parallel activities were completed to create the maps: (1) An in-person expert panel and (2) an electronic survey. The panel consisted of expert users of AIS and ICD from North America, the United Kingdom, and Australia. The panel met in person for 5 days, with follow-up virtual meetings to create and revise the maps. Additional qualitative data were documented to resolve potential discrepancies in mapping. The electronic survey was completed by 95 injury coding professionals from North America, Spain, Australia, and New Zealand over 12 weeks. ICD-to-AIS maps were created for: ICD-9-CM and ICD-10-CM. Both maps indicated whether the corresponding AIS 2005/Update 2008 severity score for each ICD code was AIS 3+, 1/2, or indeterminable. Though some ICD codes could be mapped to multiple AIS codes, the maximum severity of all potentially mapped injuries determined the final severity categorization.

Results: The in-person panel consisted of 13 experts, with 11 Certified AIS specialists (CAISS) with a median of 8 years and an average of 15 years of coding experience. Consensus was reached for AIS severity categorization for all injury-related ICD codes. There were 95 survey respondents, with a median of 8 years of injury coding experience. Approximately 15 survey responses were collected per ICD code. Results from the 2 activities were compared, and any discrepancies were resolved using additional qualitative and quantitative data from the in-person panel and survey results, respectively.

Conclusions: Robust maps of ICD-9-CM and ICD-10-CM injury codes to AIS severity categories (3+ versus <3) were successfully created from an in-person panel discussion and electronic survey. These maps provide a link between the common ICD diagnostic lexicons and the AIS severity coding system and are of value to injury researchers, public health scientists, and epidemiologists using large databases without available AIS coding.     

Fun ride or risky transport: Golf cart-related injuries treated in U.S. emergency departments from 2007 through 2017

Introduction: Golf cart-related injuries constitute a substantial source of morbidity, most notably in pediatric populations. Despite the high rate of injuries, there have been no meaningful changes in golf cart design or legislation to reduce the overall burden of these injuries. This study sought to characterize the epidemiology of golf cart-related injuries treated in United States hospital emergency departments. Method: A retrospective analysis was conducted by using data from the National Electronic Injury Surveillance System for patients of all ages who were treated in emergency departments (EDs) (2007–2017) for a golf cart-related injury. Results: From 2007 through 2017, an estimated 156,040 (95% CI = 102,402–209,679) patients were treated in U.S. EDs for golf cart-related injuries. The average rate of traumatic brain injuries (TBIs) in children (1.62 per 100,000 children) was more than three times that of TBIs in adults (0.52 per 100,000 adults; rate ratio = 2.38; 95% CI = 2.36–2.41) and nearly twice that of TBIs in seniors (1.11 per 100,000 seniors; rate ratio = 1.21; 95% CI = 1.19–1.22). The rate of injuries in seniors increased significantly by 67.6% from 4.81 per 100,000 seniors in 2007 to 8.06 per 100,000 seniors in 2017 (slope = 0.096; p = 0.041). Conclusions: Golf cart use remains an important source of injury for people of all ages, especially in children. As use continues to increase, it is unlikely that golf cart-related injuries will decrease without substantial changes to product design, regulation, and/or legislation. Practical Applications: Use of golf carts pose a considerable risk of injury and morbidity; safety recommendations should be followed.     

Injury risk functions for frontal oblique collisions

Objective: The objective of this article was the construction of injury risk functions (IRFs) for front row occupants in oblique frontal crashes and a comparison to IRF of nonoblique frontal crashes from the same data set.

Method: Crashes of modern vehicles from GIDAS (German In-Depth Accident Study) were used as the basis for the construction of a logistic injury risk model. Static deformation, measured via displaced voxels on the postcrash vehicles, was used to calculate the energy dissipated in the crash. This measure of accident severity was termed objective equivalent speed (oEES) because it does not depend on the accident reconstruction and thus eliminates reconstruction biases like impact direction and vehicle model year. Imputation from property damage cases was used to describe underrepresented low-severity crashes―a known shortcoming of GIDAS. Binary logistic regression was used to relate the stimuli (oEES) to the binary outcome variable (injured or not injured).

Results: IRFs for the oblique frontal impact and nonoblique frontal impact were computed for the Maximum Abbreviated Injury Scale (MAIS) 2+ and 3+ levels for adults (18–64 years). For a given stimulus, the probability of injury for a belted driver was higher in oblique crashes than in nonoblique frontal crashes. For the 25% injury risk at MAIS 2+ level, the corresponding stimulus for oblique crashes was 40 km/h but it was 64 km/h for nonoblique frontal crashes.

Conclusions: The risk of obtaining MAIS 2+ injuries is significantly higher in oblique crashes than in nonoblique crashes. In the real world, most MAIS 2+ injuries occur in an oEES range from 30 to 60 km/h.     

Evaluation of geometrically personalized THUMS pedestrian model response against sedan–pedestrian PMHS impact test data

Objective: Evaluating the biofidelity of pedestrian finite element models (PFEM) using postmortem human subjects (PMHS) is a challenge because differences in anthropometry between PMHS and PFEM could limit a model's capability to accurately capture cadaveric responses. Geometrical personalization via morphing can modify the PFEM geometry to match the specific PMHS anthropometry, which could alleviate this issue. In this study, the Total Human Model for Safety (THUMS) PFEM (Ver 4.01) was compared to the cadaveric response in vehicle–pedestrian impacts using geometrically personalized models.

Methods: The AM50 THUMS PFEM was used as the baseline model, and 2 morphed PFEM were created to the anthropometric specifications of 2 obese PMHS used in a previous pedestrian impact study with a mid-size sedan. The same measurements as those obtained during the PMHS tests were calculated from the simulations (kinematics, accelerations, strains), and biofidelity metrics based on signals correlation (correlation and analysis, CORA) were established to compare the response of the models to the experiments. Injury outcomes were predicted deterministically (through strain-based threshold) and probabilistically (with injury risk functions) and compared with the injuries reported in the necropsy.

Results: The baseline model could not accurately capture all aspects of the PMHS kinematics, strain, and injury risks, whereas the morphed models reproduced biofidelic response in terms of trajectory (CORA score = 0.927 ± 0.092), velocities (0.975 ± 0.027), accelerations (0.862 ± 0.072), and strains (0.707 ± 0.143). The personalized THUMS models also generally predicted injuries consistent with those identified during posttest autopsy.

Conclusions: The study highlights the need to control for pedestrian anthropometry when validating pedestrian human body models against PMHS data. The information provided in the current study could be useful for improving model biofidelity for vehicle–pedestrian impact scenarios.