The generation of subject-specific musculoskeletal models of the lower limb has become a feasible task thanks to improvements in medical imaging technology and musculoskeletal modelling software. Nevertheless, clinical use of these models in paediatric applications is still limited for what concerns the estimation of muscle and joint contact forces. Aiming to improve the current state of the art, a methodology to generate highly personalized subject-specific musculoskeletal models of the lower limb based on magnetic resonance imaging (MRI) scans was codified as a step-by-step procedure and applied to data from eight juvenile individuals. The generated musculoskeletal models were used to simulate 107 gait trials using stereophotogrammetric and force platform data as input. To ensure completeness of the modelling procedure, muscles' architecture needs to be estimated. Four methods to estimate muscles' maximum isometric force and two methods to estimate musculotendon parameters (optimal fiber length and tendon slack length) were assessed and compared, in order to quantify their influence on the models' output. Reported results represent the first comprehensive subject-specific model-based characterization of juvenile gait biomechanics, including profiles of joint kinematics and kinetics, muscle forces and joint contact forces. Our findings suggest that, when musculotendon parameters were linearly scaled from a reference model and the muscle force-length-velocity relationship was accounted for in the simulations, realistic knee contact forces could be estimated and these forces were not sensitive the method used to compute muscle maximum isometric force.

The ability of muscles to produce force depends, among others, on their anatomical features and it is altered by ageing-associated weakening. However, a clear characterisation of these features, highly relevant for older individuals, is still lacking. This study hence aimed at characterising muscle volume, length, and physiological cross-sectional area (PCSA) and their variability, between body sides and between individuals, in a group of post-menopausal women. Lower-limb magnetic resonance images were acquired from eleven participants (69 (7) y. o., 66.9 (7.7) kg, 159 (3) cm). Twenty-three muscles were manually segmented from the images and muscle volume, length and PCSA were calculated from this dataset. Personalised maximal isometric force was then calculated using the latter information. The percentage difference between the muscles of the two lower limbs was up to 89% and 22% for volume and length, respectively, and up to 84% for PCSA, with no recognisable pattern associated with limb dominance. Between-subject coefficients of variation reached 36% and 13% for muscle volume and length, respectively. Generally, muscle parameters were similar to previous literature, but volumes were smaller than those from in-vivo young adults and slightly higher than ex-vivo ones. Maximal isometric force was found to be on average smaller than those obtained from estimates based on linear scaling of ex-vivo-based literature values. In conclusion, this study quantified for the first time anatomical asymmetry of lower-limb muscles in older women, suggesting that symmetry should not be assumed in this population. Furthermore, we showed that a scaling approach, widely used in musculoskeletal modelling, leads to an overestimation of the maximal isometric force for most muscles. This heavily questions the validity of this approach for older populations. As a solution, the unique dataset of muscle segmentation made available with this paper could support the development of alternative population-based scaling approaches, together with that of automatic tools for muscle segmentation.

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