Journal of Biomechanical Engineering-Transactions of the Asme最新文献

Accurate occupant injury prediction in near-collision scenarios is vital in guiding intelligent vehicles to find the optimal collision condition with minimal injury risks. Existing studies focused on boosting prediction performance by introducing deep-learning models but encountered computational burdens due to the inherent high model complexity. To better balance these two traditionally contradictory factors, this study proposed a training method for pre-crash injury prediction models, namely, knowledge distillation (KD)-based training. This method was inspired by the idea of knowledge distillation, an emerging model compression method. Technically, we first trained a high-accuracy injury prediction model using informative post-crash sequence inputs (i.e., vehicle crash pulses) and a relatively complex network architecture as an experienced "teacher". Following this, a lightweight pre-crash injury prediction model ("student") learned both from the ground truth in output layers (i.e., conventional prediction loss) and its teacher in intermediate layers (i.e., distillation loss). In such a step-by-step teaching framework, the pre-crash model significantly improved the prediction accuracy of occupant's head abbreviated injury scale (AIS) (i.e., from 77.2% to 83.2%) without sacrificing computational efficiency. Multiple validation experiments proved the effectiveness of the proposed KD-based training framework. This study is expected to provide reference to balancing prediction accuracy and computational efficiency of pre-crash injury prediction models, promoting the further safety improvement of next-generation intelligent vehicles.

Variability in body shape and soft tissue geometry have the potential to affect the body's interaction with automotive safety systems. In this study, we developed a methodology to capture information on body shape, superficial soft tissue geometry, skeletal geometry, and seatbelt fit relative to the skeleton-in automotive postures-using Open Magnetic Resonance Imaging (MRI). Volunteer posture and belt fit were first measured in a vehicle and then reproduced in a custom MRI-safe seat (with an MR-visible seatbelt) placed in an Open MR scanner. Overlapping scans were performed to create registered three-dimensional reconstructions spanning from the thigh to the clavicles. Data were collected with ten volunteers (5 female, 5 male), each in their self-selected driving posture and in a reclined posture. Examination of the MRIs showed that in the males with substantial anterior abdominal adipose tissue, the abdominal adipose tissue tended to overhang the pelvis, narrowing in the region of the Anterior Superior Iliac Spine (ASIS). For the females, the adipose tissue depth around the lower abdomen and pelvis was more uniform, with a more continuous layer superficial to the ASIS. Across the volunteers, the pelvis rotated rearward by an average of 62% of the change in seatback angle during recline. In some cases, the lap belt drew nearer to the pelvis as the volunteer reclined (as the overhanging folds of adipose tissue stretched). In others, the belt-to-pelvis distance increased as the volunteer reclined. These observations highlight the importance of considering both interdemographic and intrademographic variability when developing tools to assess safety system robustness.

The injury risk in a vehicle crash can depend on occupant specific factors. Virtual crash testing using finite element human body models (HBMs) to represent occupant variability can enable the development of vehicles with improved safety for all occupants. In this study, it was investigated how many HBMs of different sizes that are needed to represent a population crash outcome through a metamodel. Rib fracture risk was used as an example occupant injury outcome. Morphed HBMs representing variability in sex, height, and weight within defined population ranges were used to calculate population variability in rib fracture risk in a frontal and a side crash. Two regression methods, regularized linear regression with second-order terms and Gaussian process regression (GPR), were used to metamodel rib fracture risk due to occupant variability. By studying metamodel predictive performance as a function of training data, it was found that constructing GPR metamodels using 25 individuals of each sex appears sufficient to model the population rib fracture risk outcome in a general crash scenario. Further, by utilizing the known outcomes in the two crashes, an optimization method selected individuals representative for population outcomes across both crash scenarios. The optimization results showed that 5-7 individuals of each sex were sufficient to create predictive GPR metamodels. The optimization method can be extended for more crashes and vehicles, which can be used to identify a family of HBMs that are generally representative of population injury outcomes in future work.

The objective of this study was to compare the kinematics of the head-neck, torso, pelvis, and lower extremities and document injuries and their patterns to small female occupants in frontal impacts with upright and reclined postures using an experimental model. Six postmortem human surrogates (PMHS) with a mean stature of 154 ± 9.0 cm and mass of 49 ± 12 kg were equally divided between upright and reclined groups (seatback: 25 deg and 45 deg), restrained by a three-point integrated belt, positioned on a semirigid seat, and exposed to low and moderate crash velocities (15 km/h and 32 km/h respectively). The response between the upright and reclined postures was similar in magnitude and curve morphology. While none of the differences were statistically significant, the thoracic spine demonstrated increased downward (+Z) displacement, and the head demonstrated an increased horizontal (+X) displacement for the reclined occupants. In contrast, the upright occupants showed a slightly increased downward (+Z) displacement at the head, but the torso displaced primarily along the +X direction. The posture angles between the two groups were similar at the pelvis and different at the thorax and head. At 32 km/h, both cohorts exhibited multiple rib failure, with upright specimens having a greater number of severe fractures. Although MAIS was the same in both groups, the upright specimens had more bi-cortical rib fractures, suggesting the potential for pneumothorax. This preliminary study may be useful in validating physical (ATDs) and computational (HBMs) surrogates.

Competency-based grading (CBG) can take different forms in different subject areas. We present a method for implementing CBG in a biomechanics course with nine primary learning objectives. Competency in each learning objective is measured by the student's ability to correctly answer knowledge questions and solve analytical problems in the field of biomechanics. The primary goal of implementing CBG was to provide more opportunities for lower-performing students to learn the material and to demonstrate that learning. To determine the efficacy of CBG to improve student learning, the primary measure was course grade distribution before and after implementation of CBG. The course grade distribution data indicated that CBG has primarily helped midperforming students to improve their grades. Because of the limitations of course grades as a measure of learning, we also performed analysis of student performance on successive attempts which indicated initial and secondary attempts are best, with student success declining on subsequent attempts. Anecdotally, many students improved performance, and thus their grade, on the (optional) final exam attempts. Limitations of the study include the limited course offerings with CBG (three), and that effects of COVID-19 may be confounding CBG data. Also, the approach places nearly all the grade on quizzes or exams. However, the approach could be modified to include homework grades, projects, and the like. Overall, the student learning in this course and implementation appears to be only positively affected, so this approach appears to have benefits in a biomechanics course.

Generative artificial intelligence (AI) tools such as ChatGPT, Bard, and Claude have recently become a concern in the delivery of engineering education. For courses focused on computer coding, such tools are capable for creating working computer code across a range of computer languages and computing platforms. In a course for mechanical engineers focused on C++ coding for the Arduino microcontroller and coding engineering problems in Matlab, a new approach to assessment of programing homework assignments was developed. This assessment moved the focus of assigned points from the correctness of the code to the effort and understanding of the code demonstrated by the student during in-person grading. Students who participated fully in in-person grading did significantly better on a midterm exam. Relative to a previous semester, where grading was focused on correct code, students had a slightly higher average midterm exam score. This approach appears to be effective in supporting computational learning in the face of evolving tools that could be used to circumvent learning. Future work should examine how to also encourage responsible use of generative AI in computational learning.

Behind armor blunt trauma (BABT), resulting from dynamic deformation of protective ballistic armor into the thorax, is currently assessed assuming a constant threshold of maximum backface deformation (BFDs) (44 mm). Although assessed for multiple impacts on the same armor, testing is focused on armor performance (shot-to-edge and shot-to-shot) without consideration of the underlying location on the thorax. Previous studies identified the importance of impacts on organs of animal surrogates wearing soft armor. However, the effect of impact location was not quantified outside the threshold of 44 mm. In the present study, a validated biofidelic advanced human thorax model (50th percentile male) was utilized to assess the BABT outcome from varying impact location. The thorax model was dynamically loaded using a method developed for recreating BABT impacts, and BABT events within the range of real-world impact severities and locations were simulated. It was found that thorax injury depended on impact location for the same BFDs. Generally, impacts over high compliance locations (anterolateral rib cage) yielded increased thoracic compression and loading on the lungs leading to pulmonary lung contusion (PLC). Impacts at low compliance locations (top of sternum) yielded hard tissue fractures. Injuries to the sternum, ribs, and lungs were predicted at BFDs lower than 44 mm for low compliance locations. Location-based injury risk curves demonstrated greater accuracy in injury prediction. This study quantifies the importance of impact location on BABT injury severity and demonstrates the need for consideration of location in future armor design and assessment.

The topic of kinematics is fundamental to engineering and has a significant bearing on clinical evaluations of human movement. For those studying biomechanics, this topic is often overlooked in importance. The degree to which kinematic fundamentals are included in Biomedical engineering (BmE) curriculums is not consistent across programs and often foundational understandings are gained only after reading literature if a research or development project requires that knowledge. The purpose of this paper is to present the important theories and methods of kinematic analysis and synthesis that should be in the "toolbox" of students of biomechanics. Each topic is briefly presented accompanied by an example or two. Deeper learning of each topic is left to the reader, with the help of some sample references to begin that journey.

Head impacts in bicycle accidents are typically oblique to the impact surface and transmit both normal and tangential forces to the head, causing linear and rotational head kinematics, respectively. Traditional expanded polystyrene (EPS) foam bicycle helmets are effective at preventing many head injuries, especially skull fractures and severe traumatic brain injuries (TBIs) (primarily from normal contact forces). However, the incidence of concussion from collisions (primarily from rotational head motion) remains high, indicating need for enhanced protection. An elastomeric honeycomb helmet design is proposed herein as an alternative to EPS foam to improve TBI protection and be potentially reusable for multiple impacts, and tested using a twin-wire drop tower. Small-scale normal and oblique impact tests showed honeycomb had lower oblique strength than EPS foam, beneficial for diffuse TBI protection by permitting greater shear deformation and had the potential to be reusable. Honeycomb helmets were developed based on the geometry of an existing EPS foam helmet, prototypes were three-dimensional-printed with thermoplastic polyurethane and full-scale flat and oblique drop tests were performed. In flat impacts, honeycomb helmets resulted in a 34% higher peak linear acceleration and 7% lower head injury criteria (HIC15) than EPS foam helmets. In oblique tests, honeycomb helmets resulted in a 30% lower HIC15 and 40% lower peak rotational acceleration compared to EPS foam helmets. This new helmet design has the potential to reduce the risk of TBI in a bicycle accident, and as such, reduce its social and economic burden. Also, the honeycomb design showed potential to be effective for repetitive impact events without the need for replacement, offering benefits to consumers.