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Abstract
Abnormal mechanical loading is essential in the onset and progression of knee osteoarthritis. Combined musculoskeletal (MS) and finite element (FE) modeling is a typical method to estimate load distribution and tissue responses in the knee joint. However, earlier combined models mostly utilize static-optimization based MS models and muscle force driven FE models typically use elastic materials for soft tissues or analyze specific time points of gait. Therefore, here we develop an electromyography-assisted muscle force driven FE model with fibril-reinforced poro(visco)elastic cartilages and menisci to analyze knee joint loading during the stance phase of gait. Moreover, since ligament pre-strains are one of the important uncertainties in joint modeling, we conducted a sensitivity analysis on the pre-strains of anterior and posterior cruciate ligaments (ACL and PCL) as well as medial and lateral collateral ligaments (MCL and LCL). The model produced kinematics and kinetics consistent with previous experimental data. Joint contact forces and contact areas were highly sensitive to ACL and PCL pre-strains, while those changed less cartilage stresses, fibril strains, and fluid pressures. The presented workflow could be used in a wide range of applications related to the aetiology of cartilage degeneration, optimization of rehabilitation exercises, and simulation of knee surgeries.
Highlights
MS models estimate muscle forces and JCFs using kinematic and kinetic data obtained while performing an activity
Sensitivity analysis indicates that higher pre-strain in Anterior cruciate ligament (ACL), posterior cruciate ligament (PCL), and lateral collateral ligament (LCL) increased the JCF and moved both the JCF distribution and the tibiofemoral contact area to the lateral side of the joint (Figs. 4 and 5)
The aim of this study was to develop and present a multiscale modeling workflow by combining a subject-specific EMG-assisted MS model with a muscle force driven finite element (FE) model based on magnetic resonance imaging (MRI), EMG signals, and motion data of the subject
Summary
MS models estimate muscle forces and JCFs using kinematic and kinetic data obtained while performing an activity. Muscle activation and co-contraction levels could significantly vary in different activities and disorders such as KOA patients in comparison with healthy subjects despite small variations in kinematics and kinetics[33,34,35] In these scenarios, previous studies[26,36,37,38,39,40] have suggested that assisting www.nature.com/scientificreports the optimizer with EMGs improves the accuracy of the estimated muscle activations and the JCF. Some muscle force driven FE models, again without EMG-assistance, have included fibril-reinforced hyperelastic composite material models for cartilages[23,24,28,32,44] These studies[23,24,28,32,44] did not analyze a continues gait cycle but statically analyzed specific time points during the stance phase of the gait. It has been suggested that this assumption results in overestimated muscle forces and JCFs30,32
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