Multibody muscle driven model of an instrumented prosthetic knee during squat and toe rise motions.

摘要: Detailed knowledge of joint kinematics and loading is essential for improving the design surgical outcomes total knee replacement surgeries tissue engineering applications. Instrumented prosthetics that are capable measuring during ambulatory activities have been implanted in patients [1–3], but implementation these devices expensive number using instrumented limited. Experimentally measured often augmented with traditional gait laboratory measurements including motion capture, ground reaction forces, muscle activations through electromyography (EMG). Computational models can enhance experimental by providing detailed information on contact mechanics addition to loading. Dynamic a contributing factor progression osteoarthritis [4] equally important artificial wear [5,6]. A dynamic computational model which muscle, ligament, articular surface forces predicted concurrently would be ideal tool implant objective planning treatments. The most hurdle clinic validation estimated vivo contact, forces. The majority published three-dimensional multibody simulations included quasi-static [7–13], prevents prediction ligament alongside pressures conditions. Over decade ago, Piazza Delp [14] produced forward-dynamic simulation step-up task combined from 13 EMG driven muscles crossing prosthetic knee, collateral ligaments modeled as nonlinear elastic springs, rigid contacts defined between tibio-femoral patella-femoral component geometries. Since publication paper 2001, combine geometries rare. Although need concurrent link motion, has recognized [14–16], body-level movement combines muscles, ligaments, does not exist recent literature. Several net inverse static optimization at level developed predict tibia forces. These represent hinge body level, resulting load predictions generally agreed well measurements. For example, Kim et al. compared values an [17]. In this modeling scheme, moments dynamics were used (minimizing sum squares activations). along fluoroscopy fed into subject specific model. Deformable then Recently, Lundberg four subjects [18]. parametric was find solution space parametrically varied solving equilibrium equations discrete time steps cycle. Net analysis provided external [19]. The finite element method widely relationship replacements. Static flexion angles applied determine stress [6]. simulators [20] [16,21,22] provide boundaries Zelle recently simulated weight-bearing squatting applying distal incrementally releasing constrained quadriceps tendon achieve [23]. Explicit typically include Halloran found rigid-body analyses nearly identical deformable loaded simulator [16]. addition, area close fraction cost. Good agreement also demonstrated foundation theory femoral tibial components [24]. Body-level do literature. data “Grand Challenge Competition Predict In-Vivo Knee Loads” 2012 American Society Mechanical Engineers Summer Bioengineering Conference [1] opportunity create validate such present study develop full body, driven, dynamic, squat toe-rise motions framework estimation patches insert. chosen before attempting more complicated trials. anatomically correct lower extremities anthropometric female implant. force against acquired patient. Moreover, activation patterns kinematic accuracy evaluated. achieved discretizing tray multiple hexahedral elements.