Abstract
Experimental knee simulators for component evaluation or in vitro testing provide valuable insight into the mechanics of the implanted joint. The Kansas knee simulator (KKS) is an electro-hydraulic whole joint knee simulator, with five actuators at the hip, ankle and quadriceps muscle used to simulate a variety of dynamic activities in cadaveric specimens. However, the number and type of experimental tests which can feasibly be performed is limited by the need to make physical component parts, obtain cadaveric specimens and the substantial time required to carry out each test. Computational simulations provide a complementary toolset to experimental testing; experimental data can be used to validate the computational model which can subsequently be used for early evaluation and ranking of component designs. The objective of this study was to explore potential improvements to loading and boundary conditions in current computational/experimental models, specifically the KKS, in order to develop representations of several activities of daily living (ADLs) which reproduce in vivo knee joint loading measurements.
An existing finite element model of the KKS was modified to extend the capability, and improve the fidelity, of the computational model beyond the experimental setup. An actuator to allow anterior-posterior (A-P) motion of the hip was included and used to prescribe relative hip-ankle A-P kinematics during the simulations. The quadriceps muscle, which in the experimental simulator consisted of a single quadriceps bundle with a point-to-point line of action, was divided into four heads of the quadriceps with physiological muscle paths. The hamstrings muscle, which was not present in the experiment, was represented by point-to-point actuators in four bundles. A flexible control system was developed which allowed control of the quadriceps and hamstrings actuators to match a knee flexion profile, similar to actuation of the experimental KKS, but also allowed control of the compressive tibiofemoral (TF) joint force, medial-lateral (M-L) load distribution, internal-external (I-E) torque and A-P load at the joint. A series of sensors, measuring all six load components on the medial and lateral compartments of the tibial insert, as well as knee flexion angle, were incorporated into the simulation. Instantaneous measurements from the sensors were fed to a control system, implemented within an Abaqus/Explicit user subroutine (Figure 1). The controller was used to drive actuators in the FE model to match target in vivo joint loading profiles, measured from telemetric patient data. The control system was applied to recreate in vivo loading conditions at the knee joint during three ADLs for three different subjects (Figure 2), with excellent agreement between simulation joint loading conditions and the target profiles; RMS differences were less than 1°, 80N, 2.5%, and 0.8Nm for knee flexion angle, compressive joint load, M-L load split and I-E torque, respectively, throughout the cycle for all three activities (Figure 3). The flexible nature of the control system ensures that it can be used to evaluate an expansive variety of ‘effect of’ studies, as well as to determine advanced loading profiles for the experimental simulator.
