The effects of axial preload and dorsiflexion on the tolerance of the ankle/subtalar joint to dynamic inversion and eversion

Monte carlo method for estimating whole-body injury metrics from pedestrian impact simulation results

The goal of the current study was to develop a method to estimate whole-body injury metrics (WBIMs), which measure the overall impact of injuries, using stochastic injury prediction results from a computational human surrogate. First, hospitalized pedestrian data was queried to identify injuries sustained by pedestrians and their frequencies. Second, with consideration for an understanding of injury mechanisms and the capability of the computational human surrogate, the whole-body was divided into 17 body regions. Then, an injury pattern database was constructed for each body region for various maximum abbreviated injury scale (MAIS) levels. Third, a two-step Monte Carlo sampling process was employed to generate N virtual pedestrians with an assigned list of injuries in AIS codes. Then, the expected values of WBIMs such as injury severity score (ISS), probability of death, whole-body functional capacity index (WBFCI), and lost years of life (LYL), were estimated. Lastly, the proposed method was verified using injury information from the inpatient pedestrian database. Also, the proposed method was applied to pedestrian impact simulations with various impact speeds to estimate the probability of death with respect to the impact speed. The probability of death from the proposed method was compared with those from epidemiological studies. The proposed method accurately estimated WBIMs such as ISS and WBFCI using either for a given distribution of injury risk or MAIS levels. The predicted probability of death with respect to the impact speed showed a good correlation with those from the epidemiological study. These results imply that if we have a human surrogate that can predict the risk of injury accurately, we can accurately estimate WBIMs using the proposed method. The proposed method can simplify a vehicle design optimization process by transforming the multi-objective optimization problem into the single-objective one. Lastly, the proposed method can be applied to other human surrogates such as occupant models.

Sensitivity of scale factor choice on injury response for equal-stress equal-velocity scaling

OBJECTIVE

This study aims to evaluate the assumption of geometric similitude inherent to equal-stress equal-velocity scaling by determining if scale factors created with different anthropometry metrics result in different scaled injury tolerance predictions. This assumption will be evaluated when equal-stress equal-velocity scaling is employed across dissimilar (e.g., 50^th male to small female) and similar (e.g., small female to a reference small female anthropometry) anthropometries.

METHODS

Three average male and three small female lower extremity specimens that were tested in ankle inversion/eversion were selected for scaling analysis. Three additional female specimens were selected as a reference dataset, such that the accuracy of the scaled data could be compared to an independent measured dataset. The failure moments, total height and total weight for these donors were determined from literature. Additional anthropometry metrics (leg length, calcaneus height, and bimalleolar width) were taken from each of their respective CT scans. Scale factors were calculated from these previously determined anthropometric metrics for the six donors selected for scaling analysis by targeting the averaged anthropometry metrics of the reference small female dataset. Equal-stress equal-velocity scaling was applied to the failure moments from literature using different scale factors. The mean predicted failure tolerance and standard deviation for scaled data using different scale factors were compared to one another and to the mean failure tolerance from the reference (unscaled) small female dataset.

RESULTS

When using average male data to predict ankle failure moment for a small female anthropometry, scaled moments were statistically significantly different from measured small female failure moment. Furthermore, scaled failure moments predicted using scale factors based on different anthropometry metrics were found to be significantly different from one another. Conversely, predicted mean failure moment using scaled female data of a similar size to the reference data was not significantly different from measured female failure moment, and the predicted failure moments were not significantly affected by choice of scale factor.

CONCLUSIONS

This study shows that an injury metric predicted with equal-stress equal-velocity scaling is sensitive to choice of scale factor when employing scaling across occupants of dissimilar size and sex. This conclusion suggests error can be introduced into scaled response due to choice of anthropometry metric used to create a scale factor, and therefore, anthropometry metrics used to create scale factors should be justified mechanistically and shown to apply across size and sex before being employed.

Analysis of the Frequency and Mechanism of Injury to Warfighters in the Under-body Blast Environment

During Operation Iraqi Freedom and Operation Enduring Freedom, improvised explosive devices were used strategically and with increasing frequency. To effectively design countermeasures for this environment, the Department of Defense identified the need for an under-body blast-specific Warrior Injury Assessment Manikin (WIAMan). To help with this design, information on Warfighter injuries in mounted under-body blast attacks was obtained from the Joint Trauma Analysis and Prevention of Injury in Combat program through their Request for Information interface. The events selected were evaluated by Department of the Army personnel to confirm they were representative of the loading environment expected for the WIAMan. A military case review was conducted for all AIS 2+ fractures with supporting radiology. In Warfighters whose injuries were reviewed, 79% had a foot, ankle or leg AIS 2+ fracture. Distal tibia, distal fibula, and calcaneus fractures were the most prevalent. The most common injury mechanisms were bending with probable vehicle contact (leg) and compression (foot). The most severe injuries sustained by Warfighters were to the pelvis, lumbar spine, and thoracic spine. These injuries were attributed to a compressive load from the seat pan that directly loaded the pelvis or created flexion in the lumbar spine. Rare types of injuries included severe abdominal organ injury, severe brain injury, and cervical spine injury. These typically occurred in conjunction with other fractures. Mitigating the frequently observed skeletal injuries using the WIAMan would have substantial long-term benefits for Warfighters.

Biomechanical and Injury Response of Human Foot and Ankle Under Complex Loading

Ankle and subtalar joint injuries of vehicle front seat occupants are frequently recorded during frontal and offset vehicle crashes. A few injury criteria for foot and ankle were proposed in the past; however, they addressed only certain injury mechanisms or impact loadings. The main goal of this study was to investigate numerically the tolerance of foot and ankle under complex loading which may appear during automotive crashes. A previously developed and preliminarily validated foot and leg finite element (FE) model of a 50th percentile male was employed in this study. The model was further validated against postmortem human subjects (PMHS) data in various loading conditions that generates the bony fractures and ligament failures in ankle and subtalar regions observed in traffic accidents. Then, the foot and leg model were subjected to complex loading simulated as combinations of axial, dorsiflexion, and inversion loadings. An injury surface was fitted through the points corresponding to the parameters recorded at the time of failure in the FE simulations. The compelling injury predictions of the injury surface in two crash simulations may recommend its application for interpreting the test data recorded by anthropometric test devices (ATD) during crash tests. It is believed that the methodology presented in this study may be appropriate for the development of injury criteria under complex loadings corresponding to other body regions as well.

Statistical simulations to evaluate the methods of the construction of injury risk curves

Several statistical methods are currently used to build injury risk curves in the biomechanical field. These methods include the certainty method (Mertz et al. 1996), Mertz/Weber method (Mertz and Weber 1982), logistic regression (Kuppa et al. 2003, Hosmer and Lemeshow 2000), survival analysis with Weibull distribution (Kent et al. 2004, Hosmer and Lemeshow 2000), and the consistent threshold estimate (CTE) (Nusholtz et al. 1999, Di Domenico and Nusholtz 2005). There is currently no consensus on the most accurate method to be used and no guidelines to help the user to choose the more appropriate one. Injury risk curves built for the WorldSID 50th side impact dummy with these different methods could vary significantly, depending on the sample considered (Petitjean et al. 2009). As a consequence, further investigations were needed to determine the fields of application of the different methods and to recommend the best statistical method depending on the biomechanical sample considered. This study used statistical simulations on theoretical samples to address these questions. Two different theoretical distributions of injury thresholds were utilized to assess the five different methods of constructing injury risk curves. A normal distribution and a Weibull distribution, whose shape was not similar to a normal distribution, were selected. One hundred sets of "test subjects" were randomly chosen from each theoretical distribution, with sample sizes ranging from 10 to 50. A "stimulus value" was chosen for each "test subject." The stimulus values were equally spaced, distributed tightly or loosely about the theoretical mean injury threshold, concentrated below the mean value, or concentrated above the mean value. An adaptive method was also used to assign stimulus values, based on the proportion of uninjured and injured in an early subset of the test subjects. The influences of 10%, 25%, and 50% exact data were compared to stimulus values that were either right or left censored. The test subject was considered to be uninjured if the stimulus was less than the subject's threshold or injured if the stimulus was equal to or greater than the subject's threshold. In all, 12,800 simulated data sets with both normal and Weibull distributions were used to construct injury risk curves by each of the five statistical methods. Cumulative errors of the constructed injury risk curves, compared to the theoretical curves, were calculated across the whole curve, as well as the portion of the theoretical curve up to 25% risk of injury. P-values were used to assess the significance of the differences in the errors. The CTE and the survival analysis take into account the exact data whatever the theoretical distribution of injury threshold, while the logistic regression, the Mertz/Weber and the certainty methods do not. For left and right censored data, the logistic regression and/or the survival analysis lead to the lowest error. The survival analysis leads to the lowest error whatever the sample size, the level of censoring and the theoretical distribution evaluated. Increasing the sample size generally decreased the error. However, the benefit from increasing the sample size decreased when the sample size was already high. For the survival analysis, increasing the proportion of exact data decreased the error. The same way, the benefit from increasing the proportion of exact data decreased when the proportion of exact data was already high. Survival analysis may not converge for small sample size with left and right censored data. The number of simulations for which the survival analysis did not converge highly decreases with the increase of proportion of exact data and the increase of the sample size. Therefore, it is recommended to use survival analysis with Weibull distribution to build risk curves compared to the four other statistical methods evaluated. The accuracy of survival analysis with other distributions (log-normal, log-logistic, etc) was not studied. There is no recommendation for the method to be used when survival analysis does not converge. The balance between maximal acceptable error and the need for an injury risk curve, even for a small dataset of poor quality, is not addressed.

Injury tolerance and response of the ankle joint in dynamic dorsiflexion

Forced dorsiflexion in frontal vehicle crashes is considered a common cause of injury to the ankle joint. Although a few studies have been published on the dynamic fracture tolerance of the ankle in dorsiflexion, this work reexamines the topic with increased statistical power, adds an evaluation of articular cartilage injury, and utilizes methods to detect the true time of fracture. The objective of this study was to measure the response and injury tolerance of the human ankle in a loading condition similar to that found in a vehicle crash with toepan intrusion. A test fixture was constructed to apply forefoot impacts to twenty cadaveric lower limbs, that were anatomically intact distal to the femur mid-diaphysis. Specimen instrumentation included implanted tibial and fibular load cells, accelerometers, angular rate sensors, and an acoustic sensor. Following the tests, specimens were radiographed and dissected to determine the extent of injury. Eleven of the twenty specimens sustained fracture of the ankle joint. Fractures of the medial malleolus were the most common, while two specimens sustained bimalleolar fractures, and two a talar neck fracture. Other injuries included ligament tears, osteochondral fractures, and cartilage abrasions. Analysis of the acoustic emission indicated that fracture did not always occur at the peak ankle moment. Based on the results of this study, an ankle joint moment of 59 N-m represents a 25% risk of ankle fracture in dorsiflexion for a 50(th) percentile male. When applied to the Thor-Lx dummy, the 25% risk of injury occurs at 36 degrees of dorsiflexion as measured by the ankle potentiometer.

Lateral and posterior dynamic bending of the mid-shaft femur: fracture risk curves for the adult population

The purpose of this study was to develop injury risk functions for dynamic bending of the human femur in the lateral-to-medial and posterior-to-anterior loading directions. A total of 45 experiments were performed on human cadaver femurs using a dynamic three-point drop test setup. An impactor of 9.8 kg was dropped from 2.2 m for an impact velocity of 5 m/s. Five-axis load cells measured the impactor and support loads, while an in situ strain gage measured the failure strain and subsequent strain rate. All 45 tests resulted in mid-shaft femur fractures with comminuted wedge and oblique fractures as the most common fracture patterns. In the lateral-to-medial bending tests the reaction loads were 4180 +/- 764 N, and the impactor loads were 4780 +/- 792 N. In the posterior-to-anterior bending tests the reaction loads were 3780 +/- 930 N, and the impactor loads were 4310 +/- 1040 N. The difference between the sum of the reaction forces and the applied load is due to inertial effects. The reaction loads were used to estimate the mid-shaft bending moments at failure since there was insufficient data to include the inertial effects in the calculations. The resulting moments are conservative estimates (lower bounds) of the mid-shaft bending moments at failure and are appropriate for use in the assessment of knee restraints and pedestrian impacts with ATD measurements. Regression analysis was used to identify significant parameters, and parametric survival analysis was used to estimate risk functions. Femur cross-sectional area, area moment of inertia (I), maximum distance to the neutral axis (c), I/c, occupant gender, and occupant mass are shown to be significant predictors of fracture tolerance, while no significant difference is shown for loading direction, bone mineral density, leg aspect and age. Risk functions are presented for femur cross-sectional area and I/c as they offer the highest correlation to peak bending moment. The risk function that utilizes the most highly correlated (R2 = 0.82) and significant (p = 0.0001) variable, cross-sectional area, predicts a 50 percent risk of femur fracture of 240 Nm, 395 Nm, and 562 Nm for equivalent cross-sectional area of the 5(th) percentile female, 50(th) percentile male, and 95(th) percentile male respectively.