No abstract

The large changes in the moment arms of the hamstrings, when activated in a model with deformable ligaments, resulted in changes to flexion torque. When simulating human motion, the inclusion of a deformable joint in a multi-scale musculoskeletal finite element model of the lower limb may preserve the realistic interaction of muscle force with knee kinematics and torque.

A key strength of computational modeling is that it can provide estimates of muscle, ligament, and joint loads, stresses, and strains through non-invasive means. However, simulations that can predict the forces in the muscles during activity while maintaining sufficient complexity to realistically represent the muscles and joint structures can be computationally challenging. For this reason, the current state of the art is to apply separate rigid-body dynamic and finite-element (FE) analyses in series. However, the use of two or more disconnected models often fails to capture key interactions between the joint-level and whole-body scales. Single framework MSFE models have the potential to overcome the limitations associated with disconnected models in series. The objectives of the current study were to create a multi-scale FE model of the human lower extremity that combines optimization, dynamic muscle modeling, and structural FE analysis in a single framework and to apply this framework to evaluate the mechanics of healthy knee specimens during two activities. Two subject-specific FE models (Model 1, Model 2) of the lower extremity were developed in ABAQUS/Explicit including detailed representations of the muscles. Muscle forces, knee joint loading, and articular contact were calculated for two activities using an inverse dynamics approach and static optimization. Quadriceps muscle forces peaked at the onset of chair rise (2174N, 1962N) and in early stance phase (510N, 525N), while gait saw peak forces in the hamstrings (851N, 868N) in midstance. Joint forces were similar in magnitude to available telemetric patient data. This study demonstrates the feasibility of detailed quasi-static, muscledriven simulations in a FE framework.

Pivoting and step descending during walking had greater internal-external rotation and anterior-posterior translation than normal gait. Internal-external rotation and anterior-posterior translation were shown to have greater activity dependence, whereas varus-valgus rotation was consistent across activities. These results were similar to prior measurements in younger cohorts, though a trend toward reduced range of motion in the older adults was observed.

Movement of the marker positions relative to the body segments obscures in vivo joint level motion. Alternatively, tracking bones from radiography images can provide precise motion of the bones at the knee but is impracticable for measurement of body segment motion. Consequently, researchers have combined marker-based knee flexion with kinematic splines to approximate the translations and rotations of the tibia relative to the femur. Yet, the accuracy of predicting six degree-of-freedom joint kinematics using kinematic splines has not been evaluated. The objectives of this study were to (1) compare knee kinematics measured with a marker-based motion capture system to kinematics acquired with high speed stereo radiography (HSSR) and describe the accuracy of marker-based motion to improve interpretation of results from these methods, and (2) use HSSR to define and evaluate a new set of knee joint kinematic splines based on the in vivo kinematics of a knee extension activity. Simultaneous measurements were recorded from eight healthy subjects using HSSR and marker-based motion capture. The marker positions were applied to three models of the lower extremity to calculate tibiofemoral kinematics and compared to kinematics acquired with HSSR. As demonstrated by normalized RMSE above 1.0, varus-valgus rotation (1.26), medial-lateral (1.26), anterior-posterior (2.03), and superior-inferior translations (4.39) were not accurately measured. Using kinematic splines improved predictions in varus-valgus (0.81) rotation, and medial-lateral (0.73), anterior-posterior (0.69), and superior-inferior (0.49) translations. Using splines to predict tibiofemoral kinematics as a function knee flexion can lead to improved accuracy over marker-based motion capture alone, however this technique was limited in reproducing subject-specific kinematics.