Injury Risk Curves Research Articles

Modeling Injury Risk From Multiple-Impulse, Area-Distributed Flash-bangs Using an Uncertainty Bounding Approach to Dose Accumulation.

Modeling of injury risk from nonlethal weapons including flash-bangs is a critical step in the design, acquisition, and application of such devices for military purposes. One flash-bang design concept currently being developed involves multiple, area-distributed flash-bangs. It is particularly difficult to model the variation inherent in operational settings employing such devices due to the randomness of flash-bang detonation positioning relative to targets. The problem is exacerbated by uncertainty related to changes in the mechanical properties of auditory system tissues and contraction of muscles in the middle ear (the acoustic reflex), which can both immediately follow impulse-noise exposure. In this article, we demonstrate a methodology to quantify uncertainty in injury risk estimation related to exposure to multiple area-distributed flash-bang impulses in short periods of time and analyze the effects of factors such as the number of impulses, their spatial distribution, and the uncertainties in their parameters on estimated injury risk. We conducted Monte Carlo simulations of dispersion and timing of a mortar-and-submunition flash-bang device that distributes submunitions over an area, using the Auditory 4.5 model developed by L3 Applied Technologies to estimate the risk of hearing loss (permanent threshold shift) in an exposure area. We bound injury risk estimates by applying limiting assumptions for dose accumulation rules applied to short inter-pulse intervals and varied impulse-noise-intensity exposure characteristic of multi-impulse flash-bangs. The upper bound of risk assumes no trading of risk between the number of impulses and intensity of individual impulses, while the lower bound assumes a perfectly protective acoustic reflex. In general, the risk to individuals standing in the most hazardous zone of the simulation is quite sensitive to the pattern of submunitions, relative to the sensitivity for those standing farther from that zone. Larger mortar burst radii (distributing submunitions over a wider area) reduce expected peak risk, while increasing the number of submunitions, the intensity of individual impulses, or the uncertainty in impulse intensity increases expected risk. We find that injury risk calculations must factor in device output variation because the injury risk curve in the flash-bang dose regime is asymmetric. We also find that increased numbers of submunitions increase the peak risk in an area more rapidly than scene-averaged risk and that the uncertainty related to dose accumulation in the acoustic reflex regime can be substantial for large numbers of submunitions and should not be ignored. This work provides a methodology for exploring both the role of device parameters and the choice of dose accumulation rule in estimating the risk of significant injury and associated uncertainty for multi-impulse, area-distributed flash-bang exposures. This analysis can inform decisions about the design of flash-bangs and training for their operational usage. The methodology can be extended to other device designs or deployment concepts to generate risk maps and injury risk uncertainty ranges. This work does not account for additional injury types beyond permanent threshold shift that may occur as a result of flash-bang exposure. A useful extension of this work would be similar work connecting design and operational parameters to human effectiveness.

Safe speeds: fatality and injury risks of pedestrians, cyclists, motorcyclists, and car drivers impacting the front of another passenger car as a function of closing speed and age

As crash speed increases, so does the probability of injury. The vulnerability of different road users varies greatly, in part due to differences in their protective equipment. Therefore, for the same speed, their injury probabilities are different. The objective of this study is to define injury risk curves, mathematical relations between closing speed (the relative speed between two crash partners) and injury outcome, for different road users. These risk curves can be used to rank road user vulnerability and define safe speeds, i.e. speeds not exceeding tolerable injury probabilities. Crashes involving pedestrians, cyclists, motorcyclists, and car drivers impacting the front of another passenger car (i.e. frontal impacts from the other car’s perspective) were extracted from the German in-depth accident study (GIDAS). The injuries were modelled as a function of closing speed and road user age using a weighted binary logistic regression. In accordance with the Abbreviated Injury Scale 2015 revision, three injury severities were modelled: at-least-moderate injury severities, at-least-serious injury severities, and fatal injuries. The constructed risk curves predicted injury outcomes with an average Area under the Curve ranging from 0.66 to 0.94 in cross-validation. A 10% risk of sustaining at-least-serious injuries corresponds to a closing speed of 29 km/h for pedestrians, 44 km/h for cyclists, 48 km/h for motorcyclists, and 112 km/h for car drivers. If a 10% risk of serious injury is acceptable, the closing speeds can be translated into safe speed limits of 25 km/h for cars with pedestrian encounters; 20 to 25 km/h for cyclists, motorcyclists, and cars when they encounter each other; and 55 km/h for cars in head-on impacts. These safe speeds align with current speed limits of 20 to 30 km/h in urban centers but bring into question the current practices of much higher speed limits on rural roads shared by bicycles, motorcycles, and cars. However, safe speed limits could be increased (maintaining a 10% serious injury risk) if road users have more protective equipment and Automated Emergency Braking reliably reduces impact speeds in all crash types.

Integrating Human and Nonhuman Primate Data to Estimate Human Tolerances for Traumatic Brain Injury.

Traumatic brain injury (TBI) contributes to a significant portion of the injuries resulting from motor vehicle crashes, falls, and sports collisions. The development of advanced countermeasures to mitigate these injuries requires a complete understanding of the tolerance of the human brain to injury. In this study, we developed a new method to establish human injury tolerance levels using an integrated database of reconstructed football impacts, subinjurious human volunteer data, and nonhuman primate data. The human tolerance levels were analyzed using tissue-level metrics determined using harmonized species-specific finite element (FE) brain models. Kinematics-based metrics involving complete characterization of angular motion (e.g., diffuse axonal multi-axial general evaluation (DAMAGE)) showed better power of predicting tissue-level deformation in a variety of impact conditions and were subsequently used to characterize injury tolerance. The proposed human brain tolerances for mild and severe TBI were estimated and presented in the form of injury risk curves based on selected tissue-level and kinematics-based injury metrics. The application of the estimated injury tolerances was finally demonstrated using real-world automotive crash data.

Human pelvis injury risk curves from underbody blast impact

IntroductionUnderbody blast loading can result in injuries to the pelvis and the lumbosacral spine. The purpose of this study was to determine human tolerance in this region based on survival...

Deflection-based parametric survival analysis side impact chest injury risk curves AIS 2015

Objectives The objective of this study was to reanalyze lateral postmortem human surrogate (PMHS) sled test chestband data to construct updated lateral thoracic injury risk curves (IRCs) using survival analysis. Methods Chestband and injury data were gathered from 16 previously conducted PMHS sled tests. Briefly, 2 chestbands were wrapped around the thorax’s circumference at the levels of ribs 4 and 8. Tests were conducted at 6.7 m/s on a rigid and padded load wall fixed to the top of a rebound sled. The injuries were reclassified using the Abbreviated Injury Scale (AIS) 2015 coding scheme. Chestband signals were combined with pretest specimen measurements to calculate the chest deflection contour time history. Deflections were determined using updated processing techniques calculating the change in length of every point on the contour from the impacted side using the thorax’s midpoint as the origin. Four candidate metrics were selected: the deflection from rib 4, the deflection from rib 8, the greater of the deflections from ribs 4 and 8, and the average of the deflections from ribs 4 and 8. AIS 3+ IRCs were developed considering outcomes of AIS ≥3 injuries. All injury data were uncensored, and noninjury data were right-censored. Three specimen mass-based IRCs were determined using the IRC with the lowest Brier score metric (BSM): The first corresponded to the 5th percentile female mass (49 kg), the second to the 50th percentile male mass (77 kg), and the third to the average mass of the PMHS ensemble (65 kg). Results Sixteen PMHS were used in the current study. Six specimens were right-censored, and 10 were uncensored. The average metric had the lowest BSM, and mass was a significant covariate with 50% risk of AIS3+ injury at 72mm of chest deflection. The 50% risk deflection magnitudes for the 5th percentile female (49 kg), 50th percentile male (77 kg), and PMHS ensemble (PMHS-E) (65 kg) were 59, 81, and 71 mm. IRCs for the 4 metrics and the 3 occupant masses are given. Conclusions IRCs were developed using survival analysis, and the average of the peak deflections was found to best represent the thoracic chest deflection response. Mass-based side impact IRCs were calculated for occupants representing the WorldSID 5th percentile female and 50th percentile male anthropomorphic test device.

Brain injury severity due to direct head contact from near-side motor vehicle collisions

Objective The objective of this study was to generate functional forms of brain injury risk curves using the National Automotive Sample System Crashworthiness Data System’s (NASS-CDS) database for the years of 2001–2015. The population of interest was near-side occupants who experienced a direct head impact with an injury source located lateral to a typical seated position. Methods Brain injuries were restricted to Abbreviated Injury Scale (AIS) 2005 Update 2008 defined concussions and internal organ injuries of the head. Near-side occupants comprised two major groups, both of which were required to have evidence of head contact (i.e., a head injury with DIRINJ = 1 and SOUCON = 1 or 2): brain injured occupants (MAIS1, MAIS2, MAIS3+) and non-brain injured occupants with some other direct contact head injury (MAIS0). Analyzed cases were required to have an indication of a reasonable crash reconstruction. Injury sources allowed within the final sample consisted of A-pillars, B-pillars, roof/roof rails, impacting vehicles/exterior objects, other components of the vehicle’s side interior, and other occupants or otherwise unspecified interior objects. Risk curves for occupants with brain injury severities of MAIS0, MAIS1+, MAIS2+, and MAIS3+ were generated using multivariate stepwise logistic regressions. Investigated predictors involved vehicle change in velocity, seat belt use, principal direction of force (PDOF), and injury source type (B-pillar and side window). Results Multivariate stepwise logistic regressions identified significant predictors of lateral change in velocity (dvlat) for all injury severity categories, and side window injury source (INJSOU = 56, 57, 58, 106, and 107) for MAIS0 and MAIS1+ risk curves. Although model sensitivity decreased for more severe injury predictions, risk curves dependent on only dvlat yielded accuracies of 70% for all presented models. Conclusions Real world crashes are often complex and lack the benefit of real time monitoring; however, NASS-CDS post-crash investigations provide data useful for injury risk prediction. Further analysis is needed to determine the effect of data confidence, injury source, and accident sequence restrictions on NASS-CDS sampling biases. The presented models likely favor a more conservative risk prediction due to the limitations of NASS-CDS data collection, AIS code conversion, and unweighted sample analysis.

Effect of blast loading on the risk of rib fractures: a preliminary 3D numerical investigation

Blast is a complex phenomenon which needs to be understood, especially in a military framework, where this kind of loading can have severe consequences on the human body. Indeed, the literature lists a number of studies which try to investigate the dangerousness of such a phenomenon, both at experimental and numerical level, and the injuries that could occur when the fighters or police officers are stroke by blast wave. When focusing on primary blast effect, this paper analyses the effect of this loading on the occurrence of rib fracture, using previously developed injury risk curves.

Evaluation of Head Injury Criteria for Injury Prediction Effectiveness: Computational Reconstruction of Real-World Vulnerable Road User Impact Accidents.

This study evaluates the effectiveness of various widely used head injury criteria (HICs) in predicting vulnerable road user (VRU) head injuries due to road traffic accidents. Thirty-one real-world car-to-VRU impact accident cases with detailed head injury records were collected and replicated through the computational biomechanics method; head injuries observed in the analyzed accidents were reconstructed by using a finite element (FE)-multibody (MB) coupled pedestrian model [including the Total Human Model for Safety (THUMS) head–neck FE model and the remaining body segments of TNO MB pedestrian model], which was developed and validated in our previous study. Various typical HICs were used to predict head injuries in all accident cases. Pearson’s correlation coefficient analysis method was adopted to investigate the correlation between head kinematics-based injury criteria and the actual head injury of VRU; the effectiveness of brain deformation-based injury criteria in predicting typical brain injuries [such as diffuse axonal injury diffuse axonal injury (DAI) and contusion] was assessed by using head injury risk curves reported in the literature. Results showed that for head kinematics-based injury criteria, the most widely used HICs and head impact power (HIP) can accurately and effectively predict head injury, whereas for brain deformation-based injury criteria, the maximum principal strain (MPS) behaves better than cumulative strain damage measure (CSDM0.15 and CSDM0.25) in predicting the possibility of DAI. In comparison with the dilatation damage measure (DDM), MPS seems to better predict the risk of brain contusion.

Pelvic Response of a Total Human Body Finite Element Model During Simulated Injurious Under Body Blast Impacts

Abstract Military operations in Iraq and Afghanistan have resulted in the increased exposure of military personnel to explosive threats. Combat-related pelvic fractures are a relatively new battlefield injury that poses a serious threat to military personnel. Injury prediction for these events continues to be a challenge due to the limited availability of blast-specific test studies and the use of established automotive-based injury criteria that do not directly translate to combat-related exposures. The objective of this study is to evaluate the pelvic response of the global human body models consortium (GHBMC) 50th percentile detailed male model (v4.3) in under body blast (UBB) loading scenarios. Nine simulations were conducted with mild or enhanced threat levels, and nominal or obtuse occupant positions. Cross-sectional force outputs from the superior pubic ramus (SPR), ilium, and sacroiliac (SI) regions were evaluated using previously developed injury risk curves (IRC). Additionally, maximum principal strain (MPS) data were extracted from the pelvic cortical bone elements. Results showed that shear force was the best predictor of fracture for the ischial and SI regions, while axial force was the best predictor for the SPR region. These outcomes were consistent with the load path of the simulated UBB events. The obtuse posture had higher peak force values for injurious and noninjurious outcomes for the SPR and SI region. The nominal posture had higher peak force values for noninjurious outcomes in the ischial region. These outcomes were supported by the MPS response present in these postures.

Fracture Injury Risk of the Restrained Mandible to Anterior-Posterior Blunt Impacts.

This study describes the results of anterior-posterior impacts conducted on the mandibles of 22 male postmortem human subjects (PMHSs). The objective of this study was to develop an injury criterion for the mandible based on blunt impact while the jaw was restrained. Previous studies have attempted to characterize the injury risk of blunt impact to the mandible; however, due to the translation of the mandible during impact and a limited number of fractured specimens, previous studies were not able to produce an injury criterion. Blunt impact to a restrained mandible is relevant to a wide array of helmeted individuals, including the military population and sports that require helmets with chinstraps. Therefore, in this study, specimens were positioned with restrained jaws and impacted using a monorail drop tower with a gravity-driven cylindrical impactor. Nineteen of 22 specimens sustained at least one fracture during testing. Injury cases had an average impact energy of 15.0 ± 5.7 J (11.1 ± 4.2 ft-lb) and a fracture force of 2684 ± 726 N (603 ± 163 lbf). Results were used to generate an impactor force based injury criterion through survival analysis. Risk of injury was modeled using a Weibull distribution and a 50% risk of injury was found to occur at approximately 2834 N (637 lbf). The developed injury risk curve can be used to characterize injury to the restrained mandible for future testing and research studies, especially in the development of maxillofacial protective equipment.

Pelvic Injury Risk Curves for the Military Populations From Lateral Impact.

Current methods for transporting military troops include nonstandard seating orientations, which may result in novel injuries because of different types/directions of loading impact. The objective of this study is to develop pelvic injury risk curves (IRCs) under lateral impacts from human cadaver tests using survival analysis for application to military populations. Published data from lateral impacts applied to whole-body cadaver specimens were analyzed. Forces were treated as response variables. Demographics and body mass index (BMI) were covariates. Injury risk curves were developed for forces without covariates, for males, females, 83 kg body mass, and 25 kg/m2 BMI. Mean and ± 95% confidence interval IRCs, normalized confidence interval sizes at discrete risk levels, and quality indices were obtained for each metric-covariate combination curve. Mean age, stature, total body mass, and BMI were 70.1 ± 8.6 years, 1.67 ± 0.1 m, 67.0 ± 14.4 kg, and 23.9 ± 3.97 kg/m2, respectively. For a total body mass of 83 kg, peak forces at 10%, 25%, and 50% probability levels were 5.7 kN, 7.4 kN, and 9.6 kN, respectively. For males, peak forces at the 10%, 25%, and 50% probability levels were 4.8 kN, 6.4 kN, and 8.4 kN, respectively. For females, peak forces at the 10%, 25%, and 50% probability levels were 3.0 kN, 4.0 kN, and 5.2 kN, respectively. Other data and risk curves are given. The IRCs developed in this study can be used as injury criteria for the crashworthiness of future generation military vehicles. The introduction of BMI, sex, and total body mass as covariates quantified their contributions. These IRCs can be used with finite element models to assess and predict injury in impact environments to advance Soldier safety. Manikins specific to relevant military anthropometry may be designed and/or evaluated with the present IRCs to assess and mitigate musculoskeletal injuries associated with this posture and impact direction.

Biomechanically Based Correlate for Localized Lung Contusion From Nonlethal Blunt Impact Projectiles.

Injury mechanics of blunt impact projectiles differ from those experienced in whole body motor vehicle collisions because the effects are localized around the point of impact, and thus, injury thresholds based upon gross chest kinematics (e.g., force, velocity) may not be applicable across impact types. Therefore, knowledge of biomechanically based tissue injury correlates for blunt impact projectiles are needed to better guide design and development of protective systems as well as assess injury risks from blunt impact projectile weapons. In this study, subject-specific swine finite element models were used to quantify the tissue-level stresses and strains resulting from high speed projectile impact. These tissue-level injury doses were correlated to pathology injury outcomes to produce injury risk curves for lung contusion. Details of the pathology data and finite element results are provided in Appendix 1. Survival analysis regression methods were applied to develop lung injury regression curves and a number of statistical methods were used to evaluate several biomechanical metrics as correlates to lung contusion. Uncertainty and sensitivity analyses were used to further confirm the selection of the correlate. Statistical analysis revealed that normalized strain-energy density was the best correlate for prediction of lung tissue damage. Going further, normalized strain-energy density also proved to be suitable for prediction of the percentage of contused lung volume, a more meaningful medical diagnosis. As expected, peak strain-energy density is most sensitive to muscle-skin properties, as quantified through a comprehensive uncertainty and sensitivity analysis over three sets of projectile weights and speeds. Normalized strain-energy density was found to be the best correlate for prediction of lung tissue damage and correlate well to extent of contused lung volume.

Development of a detailed human neck finite element model and injury risk curves under lateral impact

Advanced neck finite element modeling and development of neck injury criteria are important for the design of optimal neck protection systems in automotive and other environments. They are also important in virtual tests. The objectives of the present study were to develop a detailed finite element model (FEM) of the human neck and couple it to the existing head model, validate the model with kinematic data from legacy human volunteer and human cadaver impact datasets, and derive lateral impact neck injury risk curves using survival analysis from the upper and lower neck forces and moments. The detailed model represented the anatomy of a young adult mid-size male. It included all the cervical and first thoracic vertebrae, intervening discs, upper and lower spinal ligaments, bilateral facet joints, and passive musculature. Material properties were obtained from literature. Frontal, oblique, and lateral impacts to the distal end of the model was applied based on human volunteer and human cadaver experimental data. Corridor and cross-correlation methods were used for validation. The CORrelation and Analysis (CORA) score was used for objective assessments. Forces and moments were obtained at the occipital condyles (OC) and T1, and parametric survival analysis was used to derive injury risk curves to define human neck injury tolerance to lateral impact. The Brier Score Metric (BSM) was used to determine the hierarchical sequence among the injury metrics. The CORA scores for the lateral, frontal, and oblique impact loading conditions were 0.80, 0.91, and 0.87, respectively, for human volunteer data, and the mean score was 0.7 for human cadaver lateral impacts. Injury risk curves along with ±95% confidence intervals are given for all the four biomechanical metrics. The OC shear force was the optimal metric based on the BSM. A force of 1.5 kN was associated with the 50% probability level of AIS3+ neck injury. As a first step, the presented risk curves serve as human tolerance criteria under lateral impact, hitherto not available in published literatures, and they can be used in virtual testing and advancing restraint systems for improving human safety.

An Improved Method for Developing Injury Risk Curves Using the Brier Metric Score.

Many injury metrics are routinely proposed from measured or derived quantities from biomechanical experiments using post mortem human subjects (PMHS). The existing literature did not provide guidance on deciding between parameters collected in an experiment that would be best to use for the development of human injury probability curves (HIPC). The objective of this study was to use the Brier Metric Score (BMS) to identify the most appropriate metric from an experiment that predicts injury outcomes. The Brier Metric Score assesses how well a metric predicts the outcome for a censored data point (a lower BMS is better). Survival analysis was then conducted with the selected metric and the best distribution was selected using Akaike information criterion (AIC). Confidence intervals (CIs) and the normalized confidence interval width (NCIS) were calculated for the injury probability curve. The testing and validation of the methods described were performed using biomechanics data in the open literature. The methods for the HIPC development procedure detailed herein have been rigorously tested and used in the generation of WIAMan HIPCs and Injury Assessment Reference Curves (IARCs) for the WIAMan ATD, but can also be used in other ATD or PMHS injury risk curve development.

Development of chest deflection injury risk curve in oblique frontal small female PMHS sled tests

Objective The study's aim is to examine which sternum deflection measure best represents injury in oblique frontal impacts with small female surrogates. Methods Data from sixteen PMHS sled tests were used to calculate sternum deflection using displacements in the A-P (x) direction, transverse (xy) plane, sagittal (xz) plane, and triplanar (xyz). Peak deflections were the response variable and were combined with injury outcomes to generate injury risk curves (IRCs) using parametric statistical survival modeling. The IRC with the lowest Brier Score Metric was considered the best deflection measure representing injury. Results The triplanar (xyz) deflection metric was the best indicator of injury. At the 10 and 50% probability levels, the magnitudes of this metric were 33 mm and 55 mm, respectively. The quality of the risk curve was fair for 10% and good for 50%, based on the ISO recommendations. Conclusions The current study reports on the injury risk to small females in an upright seated position in oblique frontal impacts. The triplanar and transverse plane deflection metrics were similar for this posture; however, occupants in reclined configurations may demonstrate a different response, and further investigations are necessary.

Injury risk curves in far-side lateral motor vehicle crashes by AIS level, body region and injury code

Objective The objective of this study was to develop injury risk curves as a function of change in vehicle velocity for occupants in far-side lateral motor vehicle crashes (MVCs) by AIS level, body region, and specific AIS codes that commonly occur in this crash mode. Methods The National Automotive Sampling System-Crashworthiness Data System (NASS-CDS) years 2000–2015 database was queried, resulting in 4,495 non-weighted far-side crashes. For each case, occupant age, sex, and the following metadata were collected: vehicle model year, vehicle body type, lateral delta-v, normalized PDOF, multiple impacts, belt use, seat position, object contacted, striking vehicle body type, maximum crush extent and side airbag deployment. Multivariable logistic regression was used to develop risk curves for AIS 2+ through 5+ injuries, AIS 2+ injuries by body region (head, thorax, lower extremity), and for each of the 10 most frequent far-side AIS 2+ injuries. Significant covariates were determined by backwards elimination (p < 0.05). The full dataset and a subsampled dataset of only cases with side airbag deployment were used to develop risk curves. Results For AIS 2+ through 5+ injury, greater delta-V was associated with greater injury risk (OR’s: 2.48–3.66 per 11.9 kph increase) and belt use was associated with lower risk (OR’s: 0.04–0.36 compared to unbelted). Multiple impacts were significant predictors of increased AIS 3+, 4+ and 5+ injury risk (OR’s: 2.56, 2.27 and 2.83 compared to single impact). For AIS 2+ body region injuries, lateral delta-V and maximum CDC extent were positively associated with increased head, thorax and lower extremity injury risk while belt use was associated with lower risk. Increased lateral delta-v, unbelted status, and greater maximum CDC extent frequently increased injury risk for the most common far-side injuries. Side airbag deployment was not a significant covariate for the injury risk models. Conclusions The resulting risk models expand upon previous literature gaps to provide a more comprehensive view of contributors to injury risk for occupants in far-side MVCs. This study yields risk curves based on the latest available NASS-CDS data.

Pulmonary injury risk curves and behavioral changes from blast overpressure exposures of varying frequency and intensity in rats

At present, there are no set guidelines establishing cumulative limits for blast exposure numbers and intensities in military personnel, in combat or training operations. The objective of the current study was to define lung injury, pathology, and associated behavioral changes from primary repeated blast lung injury under appropriate exposure conditions and combinations (i.e. blast overpressure (BOP) intensity and exposure frequency) using an advanced blast simulator. Male Sprague Dawley rats were exposed to BOP frontally and laterally at a pressure range of ~ 8.5–19 psi, for up to 30 daily exposures. The extent of lung injury was identified at 24 h following BOP by assessing the extent of surface hemorrhage/contusion, Hematoxylin and Eosin staining, and behavioral deficits with open field activity. Lung injury was mathematically modeled to define the military standard 1% lung injury threshold. Significant levels of histiocytosis and inflammation were observed in pressures ≥ 10 psi and orientation effects were observed at pressures ≥ 13 psi. Experimental data demonstrated ~ 8.5 psi is the threshold for hemorrhage/contusion, up to 30 exposures. Modeling the data predicted injury risk up to 50 exposures with intensity thresholds at 8 psi for front exposure and 6psi for side exposures, which needs to be validated further.

Uncertainty Evaluations for Risk Assessment in Impact Injuries and Implications for Clinical Practice.

Injury risk curves (IRCs) represent the quantification of risk of adverse outcomes, such as a bone fracture, quantified by a biomechanical metric such as force or deflection. From a biomechanical perspective, they are crucial in crashworthiness studies to advance human safety. In clinical settings, they can be used as an assistive tool to aid in the decision-making process for surgical or conservative treatment. The estimation of risk corresponding to a level of biomechanical metric is done using a regression technique, such as a parametric survival regression model. As with any statistical procedure, error measures are computed for the IRC, representing the quality of the estimated risk. For example, confidence intervals (CIs) are recommended by the International Standards Organization, and the normalized confidence interval width (NCIW) is computed based on the width of the CI. This is a surrogate for the quality of the risk curve. A 95% CI means that if the same experiment were hypothetically repeated 100 times, at least 95 of the computed CIs should contain the true risk curve. Such an interpretation is problematic in most biomechanical contexts as rarely the same experiment is repeated. The notion that a wider confidence interval implies a poorer quality risk curve can be misleading. This article considers the evaluation of CIs and its implications in biomechanical settings for safety engineering and clinical practice. Alternatives are suggested for future studies.

Simulating Automated Emergency Braking with and without Torricelli Vacuum Emergency Braking for cyclists: Effect of brake deceleration and sensor field-of-view on accidents, injuries and fatalities

This study estimates how many additional cyclist accidents, injuries or fatalities are avoided or mitigated by adding a system which increases braking levels, the Torricelli Vacuum Emergency Brake (VEB), to a state-of-the-art Automated Emergency Braking (AEB) system. To obtain a realistic state-of-the-art AEB system, the AEB parameter settings were defined to fulfil but not exceed the performance necessary to achieve a full score in the European New Car Assessment Program (Euro NCAP). The systems are simulated in a simple but realistic simulation model in MATLAB with varying brake deceleration and sensor field-of-view (FoV).This study utilised data from the German In-Depth Accident Study (GIDAS), released in January 2019, and the related Pre-Crash Matrix (PCM), released in February 2019. Cyclist Injury Risk Curves were created from 2,662 GIDAS accidents involving a passenger car and a cyclist. The sample of cyclist accidents from the GIDAS-PCM database used in the final simulations comprised 1,340 collisions between the front of a passenger car and a cyclist. Both data samples were weighted to be representative of Germany as a whole.Adding the VEB was found to avoid over 20% more accidents than the AEB alone. Although increasing the FoV from 75° to 180° for the AEB system increases its accident avoidance rate to a level comparable to the VEB, the VEB remains about 8–20% more effective in reducing fatalities and injuries, and thus offers greater safety benefits than simply increasing AEB FoV.While the initial accidents in the representative simulation sample are fairly evenly distributed over the vehicle front, the remaining accidents (those that cannot be prevented by AEB or VEB) are more concentrated at the vehicle corners and are further characterized by high cyclist speeds. High cyclist speeds and impact to the vehicle corners potentially increase the relative frequency of head impacts to the stiff A-pillars. We therefore recommend that, for passenger cars, VEB and other advanced AEB systems should be combined with in-crash protection, especially in the A-pillar area, to best protect cyclists from injury.

Human Pelvis Bayesian Injury Probability Curves From Whole Body Lateral Impact Experiments.

Injury criteria are used in military, automotive, and aviation environments to advance human safety. While injury risk curves (IRCs) for the human pelvis are published under vertical loading, there is a paucity of analysis that describe IRCs under lateral impact. The objective of the present study is to derive IRCs under this mode. Published data were used from 60 whole-body postmortem human surrogate (PMHS) tests that used repeated testing protocols. In the first analysis, from single impact tests, all injury data points were considered as left censored and noninjury points were considered as right censored, while repeated testing results were treated as interval censored data. In the second analysis, injury data were treated uncensored. Peak force was used as the response variable. Age, total body mass, gender, and body mass index (BMI) were used as covariates in different combinations. Bayesian survival analysis model was used to derive the IRCs. Plus-minus 95% credible intervals (CI) and their normalized CI sizes (NCIS) were obtained. This is the first study to develop IRCs in whole body PMHS tests to describe the human pelvic tolerance under lateral impact using Bayesian models.