Upright trunk and lateral or slight anterior rotation of the pelvis cause the highest proximal femur forces during sideways falls

Finite element analysis of hip fracture risk in elderly female: The effects of soft tissue shape, fall direction, and interventions

This study investigates the effects of fall configurations on hip fracture risk with a focus on pelvic soft tissue shape. This was done by employing a whole-body finite element (FE) model. Soft tissue thickness around the pelvis was measured using a standing CT system, revealing a trend of increased trochanteric soft tissue thickness with higher BMI and younger age. In the lateroposterior region from the greater trochanter, the soft tissues of elderly females were thin with a concave shape. Based on the THUMS 5F model, an elderly female FE model with a low BMI was developed by morphing the soft tissue shape around the pelvis based on the CT data. FE simulation results indicated that the lateroposterior fall led to a higher femoral neck force for the elderly female model compared to the lateral fall. One reason may be related to the thin soft tissue of the pelvis in the lateroposterior region. Additionally, the effectiveness of interventions that can help mitigating hip fractures in lateroposterior falls on the thigh-hip and hip region was assessed using the elderly female model. The attenuation rate of the femoral neck force by the hip protector was close to zero in the thigh-hip fall and high in the hip fall, whereas the attenuation rate of the compliant floor was high in both falls. This study highlights age-related changes in the soft tissue shape of the pelvis in females, particularly in the lateroposterior regions, which may influence force mitigation for the hip joint during lateroposterior falls.

Forward dynamics computational modelling of a cyclist fall with the inclusion of protective response using deep learning-based human pose estimation

Single bicycle crashes, i.e., falls and impacts not involving a collision with another road user, are a significantly underestimated road safety problem. The motions and behaviours of falling people, or fall kinematics, are often investigated in the injury biomechanics research field. Understanding the mechanics of a fall can help researchers develop better protective gear and safety measures to reduce the risk of injury. However, little is known about cyclist fall kinematics or dynamics. Therefore, in this study, a video analysis of cyclist falls is performed to investigate common kinematic forms and impact patterns. Furthermore, a pipeline involving deep learning-based human pose estimation and inverse kinematics optimisation is created for extracting human motion from real-world footage of falls to initialise forward dynamics computational human body models. A bracing active response is then optimised for using a genetic algorithm. This is then applied to a case study of a cyclist fall. The kinematic forms characterised in this study can be used to inform initial conditions for computational modelling and injury estimation in cyclist falls. Findings indicate that protective response is an important consideration in fall kinematics and dynamics, and should be included in computational modelling. Furthermore, the novel reconstruction pipeline proposed here can be applied more broadly for traumatic injury biomechanics tasks. The tool developed in this study is available at https://kevgildea.github.io/KinePose/.