Failed rib region prediction in a human body model during crash events with precrash braking

<p><b>Objective</b>: The objective of this study is 2-fold. We used a validated human body finite element model to study the predicted chest injury (focusing on rib fracture as a function of element strain) based on varying levels of simulated precrash braking. Furthermore, we compare deterministic and probabilistic methods of rib injury prediction in the computational model.</p> <p><b>Methods</b>: The Global Human Body Models Consortium (GHBMC) M50-O model was gravity settled in the driver position of a generic interior equipped with an advanced 3-point belt and airbag. Twelve cases were investigated with permutations for failure, precrash braking system, and crash severity. The severities used were median (17 kph), severe (34 kph), and New Car Assessment Program (NCAP; 56.4 kph). Cases with failure enabled removed rib cortical bone elements once 1.8% effective plastic strain was exceeded. Alternatively, a probabilistic framework found in the literature was used to predict rib failure. Both the probabilistic and deterministic methods take into consideration location (anterior, lateral, and posterior). The deterministic method is based on a rubric that defines failed rib regions dependent on a threshold for contiguous failed elements. The probabilistic method depends on age-based strain and failure functions.</p> <p><b>Results</b>: Kinematics between both methods were similar (peak max deviation: Δ<i>X</i>_head = 17 mm; Δ<i>Z</i>_head = 4 mm; Δ<i>X</i>_thorax = 5 mm; Δ<i>Z</i>_thorax = 1 mm). Seat belt forces at the time of probabilistic failed region initiation were lower than those at deterministic failed region initiation. The probabilistic method for rib fracture predicted more failed regions in the rib (an analog for fracture) than the deterministic method in all but 1 case where they were equal. The failed region patterns between models are similar; however, there are differences that arise due to stress reduced from element elimination that cause probabilistic failed regions to continue to rise after no deterministic failed region would be predicted.</p> <p><b>Conclusions</b>: Both the probabilistic and deterministic methods indicate similar trends with regards to the effect of precrash braking; however, there are tradeoffs. The deterministic failed region method is more spatially sensitive to failure and is more sensitive to belt loads. The probabilistic failed region method allows for increased capability in postprocessing with respect to age. The probabilistic failed region method predicted more failed regions than the deterministic failed region method due to force distribution differences.</p

Human Head Modelling Simulation Applied to Electroconvulsive Therapy

Transcranial electrical stimulation includes electrical stimulation techniques used to treat neurological conditions. Computational human head modelling has been used to investigate diverse cases of therapies and treatments. In this chapter, 3D realistic human head models constructed from magnetic resonance images are presented for applications in electroconvulsive therapy (ECT). This technique uses low frequency and applies high amplitude current for a short period. Due to the high currents used in ECT, electrical stimulation may generate heat as per the Joule effect. Therefore, the bio-heat transfer equation coupled to the Laplace equation is implemented in a computational head model to investigate the effect of temperature due to ECT electrical stimulation. Diverse thermophysical parameters and electrode configurations are considered. The results show that, from the thermal point of view, the brain is safe and no increase in temperature is detected. Temperature rises mainly in external layers of head, such as scalp and skull while the inclusion of fat layer will influence temperature behavior. Apart from that, the inclusion of thermal anisotropic conductivity does not significantly influence temperature rise; however, electrical conductivity is an important factor to consider