Search

Efficient brain strain estimation is critical for routine application of a head injury model. Lately, a convolutional neural network (CNN) has been successfully developed to estimate spatially detailed brain strains instantly and accurately in contact sports. Here, we extend its application to automotive head impacts, where impact profiles are typically more complex with longer durations. Head impact kinematics (N=458) from two public databases were used to generate augmented impacts (N=2694). They were simulated using the anisotropic Worcester Head Injury Model (WHIM) V1.0, which provided baseline elementwise peak maximum principal strain (MPS). For each augmented impact, rotational velocity ( v rot ) and the corresponding rotational acceleration ( a rot ) profiles were concatenated as static images to serve as CNN input. Three training strategies were evaluated: 1) "baseline", using random initial weights; 2) "transfer learning", using weight transfer from a previous CNN model trained on head impacts drawn from contact sports; and 3) "combined training", combining previous training data from contact sports (N=5661) for training. The combined training achieved the best performances. For peak MPS, the CNN achieved a coefficient of determination (R 2 ) of 0.932 and root mean squared error (RMSE) of 0.031 for the real-world testing dataset. It also achieved a success rate of 60.5% and 94.8% for elementwise MPS, where the linear regression slope, k, and correlation coefficient, r, between estimated and simulated MPS did not deviate from 1.0 (when identical) by more than 0.1 and 0.2, respectively. Cumulative strain damage measure (CSDM) from the CNN estimation was also highly accurate compared to those from direct simulation across a range of thresholds (R 2 of 0.899-0.943 with RMSE of 0.054-0.069). Finally, the CNN achieved an average k and r of 0.98±0.12 and 0.90±0.07, respectively, for six reconstructed car crash impacts drawn from two other sources independent of the training dataset. Importantly, the CNN is able to efficiently estimate elementwise MPS with sufficient accuracy while conventional kinematic injury metrics cannot. Therefore, the CNN has the potential to supersede current kinematic injury metrics that can only approximate a global peak MPS or CSDM. The CNN technique developed here may offer enhanced utility in the design and development of head protective countermeasures, including in the automotive industry. This is the first study aimed at instantly estimating spatially detailed brain strains for automotive head impacts, which employs >8.8 thousand impact simulations generated from ~1.5 years of nonstop computations on a high-performance computing platform.

The objective of this study is to develop a method that uses a combination of field data analysis, naturalistic driving data analysis, and computational simulations to explore the potential injury reduction capabilities of integrating passive and active safety systems in frontal impact conditions. For the purposes of this study, the active safety system is actually a driver assist (DA) feature that has the potential to reduce delta-V prior to a crash, in frontal or other crash scenarios. A field data analysis was first conducted to estimate the delta-V distribution change based on an assumption of 20% crash avoidance resulting from a pre-crash braking DA feature. Analysis of changes in driver head location during 470 hard braking events in a naturalistic driving study found that drivers' head positions were mostly in the center position before the braking onset, while the percentage of time drivers leaning forward or backward increased significantly after the braking onset. Parametric studies with a total of 4800 MADYMO simulations showed that both delta-V and occupant pre-crash posture had pronounced effects on occupant injury risks and on the optimal restraint designs. By combining the results for the delta-V and head position distribution changes, a weighted average of injury risk reduction of 17% and 48% was predicted by the 50th percentile Anthropomorphic Test Device (ATD) model and human body model, respectively, with the assumption that the restraint system can adapt to the specific delta-V and pre-crash posture. This study demonstrated the potential for further reducing occupant injury risk in frontal crashes by the integration of a passive safety system with a DA feature. Future analyses considering more vehicle models, various crash conditions, and variations of occupant characteristics, such as age, gender, weight, and height, are necessary to further investigate the potential capability of integrating passive and DA or active safety systems.

Changes in vehicle safety design technology and the increasing use of seat-belts and airbag restraint systems have gradually changed the relative proportion of lower extremity injuries. These changes in real world injuries have renewed interest and the need of further investigation into occupant injury mechanisms and biomechanical impact responses of the knee-thigh-hip complex during frontal impacts. This study uses a detailed finite element model of the human body to simulate occupant knee impacts experienced in frontal crashes. The human body model includes detailed anatomical features of the head, neck, shoulder, chest, thoracic and lumbar spine, abdomen, pelvis, and lower and upper extremities. The material properties used in the model for each anatomic part of the human body were obtained from test data reported in the literature. The human body model used in the current study has been previously validated in frontal and side impacts. It was further validated with cadaver knee-thigh-hip impact tests in the current study. The effects of impactor configuration and flexion angle of the knee on biomechanical impact responses of the knee-thigh-hip complex were studied using the validated human body finite element model. This study showed that the knee flexion angle and the impact direction and shape of the impactors affected the injury outcomes of the knee-thigh-hip complex significantly. The 60 degrees flexed knee impact showed the least impact force, knee pressure, femoral von Mises stress, and pelvic von Mises stress but largest relative displacements of the Posterior Cruciate Ligament (PCL) and Anterior Cruciate Ligament (ACL). The 90 degrees flexed knee impact resulted in a higher impact force, knee pressure, femoral von Mises stress, and pelvic von Mises stress; but smaller PCL and ACL displacements. Stress distributions of the patella, femur, and pelvis were also given for all the simulated conditions.

The biofidelity of the Ford Motor Company human body finite element (FE) model in side impact simulations was analyzed and evaluated following the procedures outlined in ISO technical report TR9790. This FE model, representing a 50th percentile adult male, was used to simulate the biomechanical impact tests described in ISO-TR9790. These laboratory tests were considered as suitable for assessing the lateral impact biofidelity of the head, neck, shoulder, thorax, abdomen, and pelvis of crash test dummies, subcomponent test devices, and math models that are used to represent a 50th percentile adult male. The simulated impact responses of the head, neck, shoulder, thorax, abdomen, and pelvis of the FE model were compared with the PMHS (Post Mortem Human Subject) data upon which the response requirements for side impact surrogates was based. An overall biofidelity rating of the human body FE model was determined using the ISO-TR9790 rating method. The resulting rating for the human body FE model was 8.5 on a 0 to 10 scale with 8.6-10 being excellent biofidelity. In addition, in order to explore whether there is a dependency of the impact responses of the FE model on different analysis codes, three commercially available analysis codes, namely, LS-DYNA, Pamcrash, and Radioss were used to run the human body FE model. Effects of these codes on biofidelity when compared with ISO-TR9790 data are discussed. Model robustness and numerical issues arising with three different code simulations are also discussed.

Human abdominal response and injury in blunt impacts was investigated through finite element simulations of cadaver tests using a full human body model of an average-sized adult male. The model was validated at various impact speeds by comparing model responses with available experimental cadaver test data in pendulum side impacts and frontal rigid bar impacts from various sources. Results of various abdominal impact simulations are presented in this paper. Model-predicted abdominal dynamic responses such as force-time and force-deflection characteristics, and injury severities, measured by organ pressures, for the simulated impact conditions are presented. Quantitative results such as impact forces, abdominal deflections, internal organ stresses have shown that the abdomen responded differently to left and right side impacts, especially in low speed impact. Results also indicated that the model exhibited speed sensitive response characteristics and the compressibility of the abdomen significantly influenced the overall impact response in the simulated impact conditions. This study demonstrates that the development of a validated finite element human body model can be useful for abdominal injury assessment. Internal organ injuries, which are difficult to detect in experimental studies with human cadavers due to the difficulty of instrumentation, may be more easily identified with a validated finite element model through stress-strain analysis.

Human thoracic dynamic responses and injuries associated with frontal impact, side impact, and belt loading were investigated and predicted using a complete human body finite element model for an average adult male. The human body model was developed to study the impact biomechanics of a vehicular occupant. Its geometry was based on the Visible Human Project (National Library of Medicine) and the topographies from human body anatomical texts. The data was then scaled to an average adult male according to available biomechanical data from the literature. The model includes details of the head, neck, ribcage, abdomen, thoracic and lumbar spine, internal organs of the chest and abdomen, pelvis, and the upper and lower extremities. The present study is focused on the dynamic response and injuries of the thorax. The model was validated at various impact speeds by comparing predicted responses with available experimental cadaver data in frontal and side pendulum impacts, as well as belt loading. Model responses were compared with similar individual cadaver tests instead of using cadaver corridors because the large differences between the upper and lower bounds of the corridors may confound the model validation. The validated model was then used to study thorax dynamic responses and injuries in various simulated impact conditions. Parameters that could induce injuries such as force, deflection, and stress were computed from model simulations and were compared with previously proposed thoracic injury criteria to assess injury potential for the thorax. It has been shown that the model exhibited speed sensitive impact characteristics, and the compressibility of the internal organs significantly influenced the overall impact response in the simulated impact conditions. This study demonstrates that the development of a validated FE human body model could be useful for injury assessment in various cadaveric impacts reported in the literature. Internal organ injuries, which are difficult to detect in experimental studies with human cadavers, can be more easily identified with a validated finite element model through stress-strain analysis, especially in conjunction with experimental studies.