Evaluating knee replacement mechanics during ADL with PID-controlled dynamic finite element analysis.
ABSTRACT Validated computational knee simulations are valuable tools for design phase development of knee replacement devices. Recently, a dynamic finite element (FE) model of the Kansas knee simulator was kinematically validated during gait and deep flexion cycles. In order to operate the computational simulator in the same manner as the experiment, a proportional-integral-derivative (PID) controller was interfaced with the FE model to control the quadriceps actuator excursion and produce a target flexion profile regardless of implant geometry or alignment conditions. The controller was also expanded to operate multiple actuators simultaneously in order to produce in vivo loading conditions at the joint during dynamic activities. Subsequently, the fidelity of the computational model was improved through additional muscle representation and inclusion of relative hip-ankle anterior-posterior (A-P) motion. The PID-controlled model was able to successfully recreate in vivo loading conditions (flexion angle, compressive joint load, medial-lateral load distribution or varus-valgus torque, internal-external torque, A-P force) for deep knee bend, chair rise, stance-phase gait and step-down activities.
SourceAvailable from: Petro Julkunen[Show abstract] [Hide abstract]
ABSTRACT: The function of articular cartilage depends on its structure and composition, sensitively impaired in disease (e.g. osteoarthritis, OA). Responses of chondrocytes to tissue loading are modulated by the structure. Altered cell responses as an effect of OA may regulate cartilage mechanotransduction and cell biosynthesis. To be able to evaluate cell responses and factors affecting the onset and progression of OA, local tissue and cell stresses and strains in cartilage need to be characterized. This is extremely challenging with the presently available experimental techniques and therefore computational modeling is required. Modern models of articular cartilage are inhomogeneous and anisotropic, and they include many aspects of the real tissue structure and composition. In this paper, we provide an overview of the computational applications that have been developed for modeling the mechanics of articular cartilage at the tissue and cellular level. We concentrate on the use of fibril-reinforced models of cartilage. Furthermore, we introduce practical considerations for modeling applications, including also experimental tests that can be combined with the modeling approach. At the end, we discuss the prospects for patient-specific models when aiming to use finite element modeling analysis and evaluation of articular cartilage function, cellular responses, failure points, OA progression, and rehabilitation.Computational and Mathematical Methods in Medicine 04/2013; 2013:326150. DOI:10.1155/2013/326150 · 0.79 Impact Factor
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ABSTRACT: For clinically predictive testing and design-phase evaluation of prospective total knee replacement (TKR) implants, devices should ideally be evaluated under physiological loading conditions which incorporate population-level variability. A challenge exists for experimental and computational researchers in determining appropriate loading conditions for wear and kinematic knee simulators which reflect in vivo joint loading conditions. There is a great deal of kinematic data available from fluoroscopy studies. The purpose of this work was to develop computational methods to derive anterior-posterior (A-P) and internal-external (I-E) tibiofemoral (TF) joint loading conditions from in vivo kinematic data. Two computational models were developed, a simple TF model, and a more complex lower limb model. These models were driven through external loads applied to the tibia and femur in the TF model, and applied to the hip, ankle and muscles in the lower limb model. A custom feedback controller was integrated with the finite element environment and used to determine the external loads required to reproduce target kinematics at the TF joint. The computational platform was evaluated using in vivo kinematic data from four fluoroscopy patients, and reproduced in vivo A-P and I-E motions and compressive force with a root-mean-square (RMS) accuracy of less than 1mm, 0.1°, and 40N in the TF model and in vivo A-P and I-E motions, TF flexion, and compressive loads with a RMS accuracy of less than 1mm, 0.1°, 1.4°, and 48N in the lower limb model. The external loading conditions derived from these models can ultimately be used to establish population variability in loading conditions, for eventual use in computational as well as experimental activity simulations.Journal of Biomechanics 04/2014; 47(10). DOI:10.1016/j.jbiomech.2014.04.024 · 2.66 Impact Factor
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ABSTRACT: The interrelationship that exists between multiple degrees of freedom to produce a net constraint across the range of passive motion of the knee is not fully understood. Manual joint laxity assessments were performed on 28 cadaveric specimens and used to develop a unified description of the passive laxity envelope that incorporated multiple degrees of freedom into a single analysis using radial basis functions. The unified envelopes were then included in a principal component analysis to identify the primary modes of variation. The first three modes of variation constituted 82% of the variation. The first principal component (36.5% explained variation) correlated with changes to the relationship between varus-valgus and internal-external rotation and had the largest impact on internal-external laxity. The second principal component (27.2% explained variation) correlated with a shift in the internal-external envelope. The third principal component (18.3% explained variation) correlated with a shift in the varus-valgus envelope and a change in varus-valgus laxity. This research presents a novel methodology for quantifying complex changes to passive knee constraint, which may be used as a means for objectively scoring joint laxity and evaluating complex relationships between degrees of freedom in a single analysis.04/2014; 228(5). DOI:10.1177/0954411914530274