Fall accidents among older adults represent a major public health challenge worldwide. Energy-absorbing flooring has gained increasing attention due to its high usability and robustness against fall-related hip impacts. This study proposes a novel method that integrates finite element (FE) analysis, deep learning(DL) models, and multi-objective optimization (MOO) algorithms to enhance the biomechanical protective performance of a bio-inspired energy-absorbing structure. To achieve this, 100 structural configurations were generated based on a design of experiments (DOE) framework, automatically modeled in Hypermesh, and integrated into a hip regional model in LS-DYNA. The deep neural network (DNN) models were developed to predict femoral neck force (𝐹𝑛𝑒𝑐𝑘) and energy absorption efficiency (𝑆𝐸𝐴𝑣), and were subsequently utilized in the MOO framework to construct the Pareto front, optimizing the dual objectives of minimizing 𝐹𝑛𝑒𝑐𝑘 and maximizing 𝑆𝐸𝐴𝑣 using 50,000 optimization samples. Five optimal solutions on the Pareto front were validated and demonstrated substantial performance improvements. Compared to the baseline structure, the optimized designs demonstrated up to a 23% reduction in peak femoral neck force and a 65% increase in energy absorption efficiency. This study presents a framework that ensures high accuracy, robustness, and continuity in representing biomechanical responses towards improved hip protection. The findings have practical implications for enhancing safety in high fall-risk environments and provide valuable guidance for manufacturers in designing protective devices with enhanced performance and clinical effectiveness.

Head injuries constitute an increasing public health concern. Although motor vehicle injuries have steadily declined in Sweden, the number of injuries and fatalities among unprotected vulnerable road users (VRUs) continues to rise. Inspired by playground surfaces, rubberized asphalts have recently been developed as impact-absorbing pavement (IAP), with the dual objectives of enhancing collective safety and minimizing injuries, while promoting higher-value applications within the waste hierarchy. This study aims to biomechanically evaluate IAP's head injury protection performance by conducting laboratory oblique impact tests to obtain impact kinematics and finite element (FE) simulations to estimate brain strain responses. A total of 30 impact tests were performed on five kinds of asphalt samples under three impact locations. Eleven kinematics-based and five strain-based head injury metrics were analyzed and compared. For example, the peak linear acceleration (PLA), peak angular velocity (PAV), and max principal strain (MPS) were lower than 150 g, 36 rad/s, and 0.4 during oblique impact against the IAP prototype. The results demonstrated that the IAP achieved a comparable head protection performance to helmets, indicated by both the linear-based and rotational-based head injury metrics at 6 m/s. These findings show that IAP has significant potential to reduce head injuries among unprotected VRUs and contribute to a safer traffic environment.

To combat the global fatality rates among vulnerable road users (VRUs), prioritizing research on head injury mechanisms and human tolerance levels in vehicle-to-VRU traffic collisions is imperative. A foundational step for VRU injury prevention is often to create virtual reconstructions of real-world collisions. Thus, efficient and trustworthy reconstruction tools are needed to make use of recent advances in accident data collection routines and Finite Element (FE) human body modeling. In this study, a comprehensive and streamlined reconstruction methodology, starting from a video-recorded accident, has been developed. The workflow, that includes state-of-the-art tools for personalization of human body models (HBMs) and vehicles, was evaluated and demonstrated through 20 real-world VRU collision cases. The FE models successfully replicated the vehicle damage that was observed in on-scene photographs of the post-impact vehicle, as well as impact kinematics captured in dash cam or surveillance recordings. The findings highlight how video evidence can considerably narrow down the number of plausible impact scenarios and raise the credibility of virtual reconstructions of real-world VRU collision events. More importantly, this study demonstrates how, with an efficient and systematic methodology, FE might be feasible also for large-scale VRU accident datasets.

Developing vehicle finite element (FE) models that match real accident-involved vehicles is challenging. This is related to the intricate variety of geometric features and components. The current study proposes a novel method to efficiently and accurately generate case-specific buck models for car-to-pedestrian simulations. To achieve this, we implemented the vehicle side-view images to detect the horizontal position and roundness of two wheels to rectify distortions and deviations and then extracted the mid-section profiles for comparative calculations against baseline vehicle models to obtain the transformation matrices. Based on the generic buck model which consists of six key components and corresponding matrices, the case-specific buck model was generated semi-automatically based on the transformation metrics. Utilizing this image-based method, a total of 12 vehicle models representing four vehicle categories including family car (FCR), Roadster (RDS), small Sport Utility Vehicle (SUV), and large SUV were generated for car-to-pedestrian collision FE simulations in this study. The pedestrian head trajectories, total contact forces, head injury criterion (HIC), and brain injury criterion (BrIC) were analyzed comparatively. We found that, even within the same vehicle category and initial conditions, the variation in wrap around distance (WAD) spans 84–165 mm, in HIC ranges from 98 to 336, and in BrIC fluctuates between 1.25 and 1.46. These findings highlight the significant influence of vehicle frontal shape and underscore the necessity of using case-specific vehicle models in crash simulations. The proposed method provides a new approach for further vehicle structure optimization aiming at reducing pedestrian head injury and increasing traffic safety.

Falls among the elderly cause a huge number of hip fractures worldwide. Energy absorbing floors (EAFs) represent a promising strategy to decrease impact force and hip fracture risk during falls. Femoral neck force is an effective predictor of hip injury. However, the biomechanical effectiveness of EAFs in terms of mitigating femoral neck force remains largely unknown. To address this, a whole-body computational model representing a small-size elderly woman with a biofidelic representation of the soft tissue near the hip region was employed in this study, to measure the attenuation in femoral neck force provided by four commercially available EAFs (Igelkott, Kradal, SmartCells, and OmniSports). The body was positioned with the highest hip force with a -10 degrees trunk angle and +10 degrees degrees anterior pelvis rotation. At a pelvis impact velocity of 3 m/s, the peak force attenuation provided by four EAFs ranged from 5% to 19%. The risk of hip fractures also demonstrates a similar attenuation range. The results also exhibited that floors had more energy transferred to their internal energy demonstrated greater force attenuation during sideways falls. By comparing the biomechanical effectiveness of existing EAFs, these results can improve the floor design that offers better protection performance in high-fall-risk environments for the elderly.

Featured Application: The results proposed a new approach to evaluate the protection effectiveness of energy-absorbing floors for fall-related injury prevention. Also, it could help to reduce the huge associated costs related to fall-related injuries among the children and elderly population. Energy-absorbing floor (EAF) has been proposed as one of several biomechanically effective strategies to mitigate the risk of fall-related injuries by decreasing peak loads and enhancing system energy absorption. This study aims to compare the protective capacity of four commercially available EAF products (Igelkott Floor, Kradal, SmartCells, and OmniSports) in terms of head impacts using the finite element (FE) method. The stress–strain curves acquired from mechanical tests were applied to material models in LS-Dyna. The established FE models were then validated using Hybrid III or hemispheric drop tests to compare the acceleration–time curves between experiments and simulations. Finally, the validated FE models were utilized to simulate a typical pedestrian fall accident scenario. It was demonstrated that EAFs can substantially reduce the peak forces, acceleration, and velocity changes during fall-related head impacts. Specifically, in the accident reconstruction scenario, SmartCells provided the largest reduction in peak linear acceleration and skull fracture risk, while Igelkott Floor provided the largest reduction in peak angular velocity and concussion risk. This performance was caused by different energy absorption mechanisms. Consequently, the results can contribute to supporting the implementation of EAFs and determine the effectiveness of various protective strategies for fall-related head injury prevention.

Finite element human body models (HBMs) are becoming increasingly important numerical tools for traffic safety. Developing a validated and reliable HBM from the start requires integrated efforts and continues to be a challenging task. Mesh morphing is an efficient technique to generate personalized HBMs accounting for individual anatomy once a baseline model has been developed. This study presents a new image registration-based mesh morphing method to generate personalized HBMs. The method is demonstrated by morphing four baseline HBMs (SAFER, THUMS, and VIVA+ in both seated and standing postures) into ten subjects with varying heights, body mass indices (BMIs), and sex. The resulting personalized HBMs show comparable element quality to the baseline models. This method enables the comparison of HBMs by morphing them into the same subject, eliminating geometric differences. The method also shows superior geometry correction capabilities, which facilitates converting a seated HBM to a standing one, combined with additional positioning tools. Furthermore, this method can be extended to personalize other models, and the feasibility of morphing vehicle models has been illustrated. In conclusion, this new image registration-based mesh morphing method allows rapid and robust personalization of HBMs, facilitating personalized simulations.