Physics-based simulations of walking have the theoretical potential to support clinical decision-making by predicting the functional outcome of treatments in terms of walking performance. Yet before using such simulations in clinical practice, their ability to identify the main treatment targets in specific patients needs to be demonstrated. In this study, we generated predictive simulations of walking with a medical imaging based neuro-musculoskeletal model of a child with cerebral palsy presenting crouch gait. We explored the influence of altered muscle-tendon properties, reduced neuromuscular control complexity, and spasticity on gait dysfunction in terms of joint kinematics, kinetics, muscle activity, and metabolic cost of transport. We modeled altered muscle-tendon properties by personalizing Hill-type muscle-tendon parameters based on data collected during functional movements, simpler neuromuscular control by reducing the number of independent muscle synergies, and spasticity through delayed muscle activity feedback from muscle force and force rate. Our simulations revealed that, in the presence of aberrant musculoskeletal geometries, altered muscle-tendon properties rather than reduced neuromuscular control complexity and spasticity were the primary cause of the crouch gait pattern observed for this child, which is in agreement with the clinical examination. These results suggest that muscle-tendon properties should be the primary target of interventions aiming to restore an upright gait pattern for this child. This suggestion is in line with the gait analysis following muscle-tendon property and bone deformity corrections. Future work should extend this single case analysis to more patients in order to validate the ability of our physics-based simulations to capture the gait patterns of individual patients pre- and post-treatment. Such validation would open the door for identifying targeted treatment strategies with the aim of designing optimized interventions for neuro-musculoskeletal disorders.

For product developers that design near-body products, virtual mannequins that represent realistic body shapes, are valuable tools. With statistical shape modelling, the variability of such body shapes can be described. Shape variation captured by statistical shape models (SSMs) is often polluted by posture variations, leading to less compact models. In this paper, we propose a framework that has low computational complexity to build a posture invariant SSM, by capturing and correcting the posture of an instance. The posture-normalised SSM is shown to be substantially more compact than the non-posture-normalised SSM. Practitioner summary: Statistical shape modelling is a technique to map out the variability of (body) shapes. This variability is often polluted by variations in posture. In this paper, we propose a framework to build a posture invariant statistical shape model. Abbreviations: SSM: statistical shape model; 1D: one-dimensional; 3D: three-dimensional; DHM: digital human model; LBS: linear blend skinning; PCA: princial component analysis; PC: principal component; TTR: thumb tip reach.