A Pipeline for Life-Like in Vitro Tibiofemoral Loading: The Synergy of Computational Modeling and Robotic Testing

Introduction Experimental testing reproducing activity specific joint-level loading has the potential to quantify structure-function relationships, evaluate intervention possibilities, perform device analysis, and quantify joint kinematics. Many recent technological advancements have been made in this field and inspire this study9s aim to present a framework for the application of activity dependent tibiofemoral loading in a specific custom developed 6 degree of freedom (DOF) robotic test frame. This study demonstrates a pipeline wherein kinetic and kinematic data from subjects were collected in a gait lab, analyzed through musculoskeletal modeling techniques, and applied to cadaveric specimens in the robotic testing system in a real-time manner. This pipeline (Figure 1 blue dotted region) fits into a framework for synergistic development and refinement of arthroplasty techniques and devices. Methods Gait lab kinetic and kinematic data for walking was collected from 5 subjects. Subject-specific musculoskeletal modeling was performed to determine 6 DOF active component joint loading (OpenSim version 2.4, simtk.org). Kinetic profiles of the stance phase of gait were estimated and experimentally prescribed in a clinically relevant joint coordinate frame (as a function of time). Of note, knee flexion angle was the only kinematically applied DOF in the robotic testing system. Six fresh-frozen left cadaveric knee specimens (3 male, 3 female, age 49–70) were acquired. The specimens were rigidly secured to the robotic Universal Musculoskeletal Simulator (UMS) custom testing apparatus [1], which controlled joint loads with a real-time force feedback controller. Joint loads were scaled to 40% of predicted loads determined through modeling, because of system load capacity limitations and to prevent joint soft tissue damage potentially caused by additional loads without active muscle constraints. The loading profile for the walking activity was applied to each of the knees and the resulting kinematics were recorded. In addition, the force feedback controller performance was evaluated by calculating the root-mean-square (RMS) error between the desired and actual loads throughout these dynamic loading profiles. Results Figure 2 shows representative graphs of the applied kinetics and resulting kinematics for gait stance phase. These display how the pipeline is able to utilize gait data to drive dynamic robotic testing conditions and elucidate high fidelity joint level kinematics. The RMS error in each of the load controlled degrees of freedom were 6.3 ± 2.8 N in lateral drawer, 17.0 ± 10.3 N in anterior drawer, 68.6 ± 23.8 N in distraction force, 1.02 ± 0.61 Nm in varus torque, and 0.33 ± 0.14 Nm in external rotation torque. Conclusions The framework (Figure 1) for development of arthroplasty techniques and devices requires a robust data pipeline for handshaking between in vitro and in silico experiments. This study lays the foundation for future work and application of any desired activity dependent profiles, which may include statistically based “normal” loading (potentially specimen specific considering size, degradation, age, etc.). Further testing and analysis of simulation and experimental control parameters is required, but the pipeline demonstrates feasibility and promise.