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The magnitude and location of hip contact force influence the local mechanical environment of the articular tissue, driving remodeling. We used a neuromusculoskeletal model to investigate hip contact force magnitudes and their regional loading patterns on the articular surfaces in those with femoroacetabular impingement (FAI) syndrome and controls during walking.An EMG-assisted neuromusculoskeletal model was used to estimate hip contact forces in eligible participants with FAI syndrome ( n = 41) and controls ( n = 24), walking at self-selected speed. Hip contact forces were used to determine the average and spread of regional loading for femoral and acetabular articular surfaces. Hip contact force magnitude and region of loading were compared between groups using statistical parametric mapping and independent t -tests, respectively ( P0.05).All of the following findings are reported compared with controls. Those with FAI syndrome walked with lower-magnitude hip contact forces (mean difference, -0.7 N·BW -1 ; P0.001) during first and second halves of stance, and with lower anteroposterior, vertical, and mediolateral contact force vector components. Participants with FAI syndrome also had less between-participant variation in average regional loading, which was located more anteriorly (3.8°, P = 0.035) and laterally (2.2°, P = 0.01) on the acetabulum but more posteriorly (-4.8°, P = 0.01) on the femoral head. Participants with FAI syndrome had a smaller spread of regional loading across both the acetabulum (-1.9 mm, P = 0.049) and femoral head (1 mm, P0.001) during stance.Compared with controls, participants with FAI syndrome walked with lower-magnitude hip contact forces that were constrained to smaller regions on the acetabulum and femoral head. Differences in regional loading patterns might contribute to the mechanobiological processes driving cartilage maladaptation in those with FAI syndrome.

In biomechanical simulations, generic linearly scaled musculoskeletal anatomies are commonly used to represent children, often neglecting or oversimplifying subject-specific features that may affect model estimates. Inappropriate bone sizing may influence joint angles due to erroneous joint centre identification. Alternatively, subject-specific image-based musculoskeletal models allow for more realistic representations of the skeletal system. To this end, statistical shape modelling (SSM) and morphing techniques may help to reconstruct bones rapidly and accurately. Specifically, the musculoskeletal atlas project (MAP) Client, which employs magnetic resonance imaging (MRI) and/or motion capture data to inform SSM and nonrigid morphing techniques, proved able to accurately reconstruct adult pelvis and femur bones. Nonetheless, to date, the above methods have never been applied to paediatric data. In this study, pelvis, femurs and tibiofibular bones of 18 typically developing children were reconstructed using the MAP Client. Ten different combinations of SSM and morphing techniques, i.e. pipelines, were developed. Generic bone geometries from the gait2392 OpenSim model were linearly scaled for comparisons. Jaccard index, root mean square distance error and Hausdorff distance were computed to quantify reconstruction accuracy. For the pelvis bone, colour maps were produced to identify areas prone to inaccuracies and hip joint centres (HJC) location was compared. Finally, per cent difference between MRI- and MAP-measured left-to-right HJC distances was computed. Pipelines informed by MRI data, alone or in combination with motion capture data, accurately reconstructed paediatric lower limb bones (i.e. Jaccard index > 0.8). Scaled OpenSim geometries provided the least accurate reconstructions. Principal component-based scaling methods produced size-dependent results, which were worse for smaller children. ispartof: BIOMECHANICS AND MODELING IN MECHANOBIOLOGY vol:19 issue:4 ispartof: location:Germany status: published

Many biomedical, orthopaedic, and industrial applications are emerging that will benefit from personalized neuromusculoskeletal models. Applications include refined diagnostics, prediction of treatment trajectories for neuromusculoskeletal diseases, in silico design, development, and testing of medical implants, and human-machine interfaces to support assistive technologies. This review proposes how physics-based simulation, combined with machine learning approaches from big data, can be used to develop high-fidelity personalized representations of the human neuromusculoskeletal system. The core neuromusculoskeletal model features requiring personalization are identified, and big data/machine learning approaches for implementation are presented together with recommendations for further research. ispartof: Biomech Model Mechanobiol vol:19 issue:4 pages:1169-1185 ispartof: location:Germany status: Published online

BACKGROUND Studies of walking in those with femoroacetabular impingement syndrome have found altered pelvis and hip biomechanics. But a whole body, time-contiuous, assessment of biomechanical parameters has not been reported. Additionally, larger cam morphology has been associated with more pain, faster progression to end-stage osteoarthritis and increased cartilage damage but differences in walking biomechanics between large compared to small cam morphologies have not been assessed. RESEARCH QUESTION Are trunk, pelvis and lower limb biomechanics different between healthy pain-free controls and individuals with FAI syndrome and are those biomechanics different between those with larger, compared to smaller, cam morphologies? METHODS Twenty four pain-free controls were compared against 41 participants with FAI syndrome who were stratified into two groups according to their maximum alpha angle. Participants underwent three-dimensional motion capture during walking. Trunk, pelvis, and lower limb biomechanics were compared between groups using statistical parametric mapping corrected for walking speed and pain. RESULTS Compared to pain-free controls, participants with FAI syndrome walked with more trunk anterior tilt (mean difference 7.6°, p < 0.001) as well as less pelvic rise (3°, p < 0.001), hip abduction (-4.6°, p < 0.05) and external rotation (-6.5°, p < 0.05). They also had lower hip flexion (-0.06Nm⋅kg-1, p < 0.05), abduction (-0.07Nm⋅kg-1, p < 0.05) and ankle plantarflexion moments (-0.19Nm⋅kg-1, p < 0.001). These biomechanical differences occurred throughout the gait cycle. There were no differences in walking biomechanics according to cam morphology size. SIGNIFICANCE Results do not support the hypothesis that larger cam morphology is associated with larger differences in walking biomechanics but did demonstrate general differences in trunk, pelvis and lower limb biomechanics between those with FAI sydrome and pain-free controls. Altered external biomechanics are likely the result of complex sensory-motor strategy resulting from pain inhibition or impingement avoidance. Future studies should examine internal loading in those with FAI sydnrome.

Accurate representation of subject-specific bone anatomy in lower-limb musculoskeletal models is important for human movement analyses and simulations. Mathematical methods can reconstruct geometric bone models using incomplete imaging of bone by morphing bone model templates, but the validity of these methods has not been fully explored. The purpose of this study was to determine the minimal imaging requirements for accurate reconstruction of geometric bone models. Complete geometric pelvis and femur models of 14 healthy adults were reconstructed from magnetic resonance imaging through segmentation. From each complete bone segmentation, three sets of incomplete segmentations (set 1 being the most incomplete) were created to test the effect of imaging incompleteness on reconstruction accuracy. Geometric bone models were reconstructed from complete sets, three incomplete sets, and two motion capture-based methods. Reconstructions from (in)complete sets were generated using statistical shape modelling, followed by host-mesh and local-mesh fitting through the Musculoskeletal Atlas Project Client. Reconstructions from motion capture-based methods used positional data from skin surface markers placed atop anatomic landmarks and estimated joint centre locations as target points for statistical shape modelling and linear scaling. Accuracy was evaluated with distance error (mm) and overlapping volume similarity (%) between complete bone segmentation and reconstructed bone models, and statistically compared using a repeated measure analysis of variance (p 80% compared to complete segmented bone models. These findings have implications for improving movement analysis and simulation with subject-specific musculoskeletal models.

Accurate representation of subject-specific bone anatomy in lower-limb musculoskeletal models is important for human movement analyses and simulations. Mathematical methods can reconstruct geometric bone models using incomplete imaging of bone by morphing bone model templates, but the validity of these methods has not been fully explored. The purpose of this study was to determine the minimal imaging requirements for accurate reconstruction of geometric bone models. Complete geometric pelvis and femur models of 14 healthy adults were reconstructed from magnetic resonance imaging through segmentation. From each complete bone segmentation, three sets of incomplete segmentations (set 1 being the most incomplete) were created to test the effect of imaging incompleteness on reconstruction accuracy. Geometric bone models were reconstructed from complete sets, three incomplete sets, and two motion capture-based methods. Reconstructions from (in)complete sets were generated using statistical shape modelling, followed by host-mesh and local-mesh fitting through the Musculoskeletal Atlas Project Client. Reconstructions from motion capture-based methods used positional data from skin surface markers placed atop anatomic landmarks and estimated joint centre locations as target points for statistical shape modelling and linear scaling. Accuracy was evaluated with distance error (mm) and overlapping volume similarity (%) between complete bone segmentation and reconstructed bone models, and statistically compared using a repeated measure analysis of variance (p80% compared to complete segmented bone models, and improve analyses and simulation over current standard practice of linear scaling musculoskeletal models. ispartof: PLOS ONE vol:14 issue:2 ispartof: location:United States status: published