This study investigated the predictive ability of the skeletal muscle force model presented by Knodel et al. [Knodel NB, Lawson LB, Nauman EA, “An emg-based constitutive law for force generation in skeletal muscle-part i: Model development,” J Biomech Eng (in press), doi: 10.1115/1.4053568] on the knee joint. It has previously been validated on the ankle joint [Knodel NB, Calvert LB, Bywater EA, Lamia JP, Patel SN, Nauman EA, “An emg-based constitutive law for force generation in skeletal muscle-part ii: Model validation on the ankle joint complex,” Submitted for Publication] and this paper aimed to identify how well it, and the solution process, performed on a more complex articulation. The knee joint’s surrounding musculoskeletal tissue loading was also identified. Ten subjects (five male and five female) performed six exercises targeting the muscles that cross the knee joint. Motion capture, electromyography, and force plate data was collected during the exercises for use in the analysis program written in MATLAB and magnetic resonance images were used to observe subject-specific ligament and tendon data at the knee articulation. OpenSim [Delp, SL, Anderson FC, Arnold AS, Loan P, Habib A, John CT, Guendelman E, Thelen DG, “Opensim: Open-source software to create and analyze dynamic simulations of movement,” IEEE Trans Biomed Eng 54(11):1940–1950, 2007, doi: 10.1109/TBME.2007.901024] was used for scaling a generic lower extremity anatomical model of each subject. Five of the six exercises were used to calculate each muscle’s constant, Km [Knodel NB, Lawson LB, Nauman EA, “An emg-based constitutive law for force generation in skeletal muscle-part i: Model development,” J Biomech Eng (in press), doi: 10.1115/1.4053568; Knodel NB, Calvert LB, Bywater EA, Lamia JP, Patel SN, Nauman EA, “An emg-based constitutive law for force generation in skeletal muscle-part ii: Model validation on the ankle joint complex,” Submitted for Publication], and the sixth was used as a testing set to identify the model’s predictive ability. Average percent errors ranged from 9.4% to 26.5% and the average across all subjects was 20.6%. The solution process produced physiologically relevant muscle forces and the surrounding tissue loading behaved as expected between the various exercises without approaching respective tensile strength values.
