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Childbirth is a complex physiological process in which a foetal neuromusculoskeletal model is of great importance to develop realistic delivery simulations and associated complication analyses. However, the estimation of hip joint centre (HJC) in foetuses remains a challenging issue. Thus, this paper aims to propose and evaluate a new approach to locate the HJC in foetuses. Hip CT-scans from 25 children (F = 11, age = 5.5 ± 2.6 years, height = 117 ± 21 cm, mass = 26 kg ± 9.5 kg) were used to propose and evaluate the novel acetabulum sphere fitting process to locate the HJC. This new approach using the acetabulum surface was applied to a population of 57 post-mortem foetal CT scans to locate the HJC as well as to determine associated regression equations using multiple linear regression. As results, the average distance between the HJC located using acetabulum sphere fitting and femoral head sphere fitting in children was 1.5 ± 0.7 mm. The average prediction error using our developed foetal HJC regression equations was 3.0 ± 1.5 mm, even though the equation for the x coordinate had a poor value of R2 (R2 for the x coordinate = 0.488). The present study suggests that the use of the acetabulum sphere fitting approach is a valid and accurate method to locate the HJC in children, and then can be extrapolated to get an estimation of the HJC in foetuses with incomplete bone ossification. Therefore, the present paper can be used as a guideline for foetus specific neuromusculoskeletal modelling. [ABSTRACT FROM AUTHOR]

The elucidation of human locomotion strategies has potential applications in the prevention of sarcopenia and in the reduction of falls. Given the diverse biochemical, mechanical and functional age-related changes seen in the neuro-musculoskeletal system, the decline in motor function is difficult to study experimentally. In this study, we use transfer testing and coupled simulation strategies within a deep reinforcement learning environment to better understand the complex problem of motor control adaptation to age-related changes. Using transfer testing, a 3D musculoskeletal model is separately trained on parameters of the young adult model (Y) for either forward or backward falls after completing two steps forward, and tested using a 30% age-related reduction for all parameters (M_all). This strategy produces a backward fall for a forwardly trained simulation, showing potential sensitivity of these parameters to a given fall direction. Second, a coupled simulation solution is used to simulate recovery from falls by considering the center-of-mass position relative to the base of support. Results for the M_all trained model showed a longer simulation time and a greater vertical pelvis velocity with a maximal value of 4.26 m/s. In particular, the results of the coupled simulations clearly show that both the young and M_all condition models respond with a step back and stronger leg extensor activations to propel the model forward to recover from the simulated fall. We developed a novel coupling between transfer testing and coupled simulation strategies to improve upon muscle models for characterizing muscle function, and also to begin testing different hypotheses, such as the strategy and force required to avoid a fall at different limits. This opens new avenues for precision rehabilitation with patient-specific muscle-driven recovery exercises. [ABSTRACT FROM AUTHOR]

Finite element models of the second-stage labor system have been commonly developed for providing objective and quantitative indicators as well as innovative therapeutic solutions for decision supports. However, the reliability of the simulation outcomes remains a challenging issue due to uncertainties in input data and model complexity as well as the lack of validation. The objective of this study was to perform uncertainty quantification (UQ) on the material properties of the pelvis soft tissue with a focus on the uterus tissue during the second labor simulation leading to explore more plausible outcome space for reliable decision support making. The developed modeling and simulation workflow includes an image-based finite element model of the fetal body and pelvis soft tissues (floor, vagina and uterus), an uncertainty modeling procedure using precise and imprecise probabilities and an uncertainty propagation process based on the Monte Carlo method with and without parameter dependency. Obtained results showed that hyperelastic properties of the uterus tissue are very sensitive during the second stage of labor simulation. Moreover, the use of imprecise probability and parameter dependency lead to a more consistent range of values for uterus tissue stress analysis. This study performed, for the first time, an UQ on the hyperelastic properties of the uterus tissue from the in silico simulation of the second-stage labor. This opens new avenues for providing reliable indicators for clinical decision support. As a perspective, the active uterus behavior will be integrated into a more realistic second-stage labor model and simulation. Then, UQ will be conducted for more reliable decision support. [ABSTRACT FROM AUTHOR]