Determination of Patient-specific Functional Axes through Two-level Optimization

INTRODUCTION Innovative patient-specific models and simulations would be valuable for addressing problems in orthopedics and sports medicine, as well as for evaluating and enhancing corrective surgical procedures [1]. For example, a patient-specific dynamic model may be useful for planning intended surgical parameters and predicting the outcome of high tibial osteotomy (HTO). Development of an accurate inverse dynamic model is a significant first step toward creating a predictive patient-specific forward dynamic model. The precision of inverse dynamic analyses is fundamentally associated with the accuracy of kinematic model parameters such as segment lengths, joint positions, and joint orientations. Understandably, a model constructed of rigid links within a multi-link chain [2] and simple mechanical approximations of joints [3] will not precisely match the human anatomy and kinematics. The model should provide the best possible assessment within the bounds of the joint models selected [3]. Earlier studies describe optimization methods to discover a set of model parameters for three-dimensional (3D), 2 degree-of-freedom (DOF) models by decreasing the error between the motion of the model and experimental data [3, 4]. In this paper, we present a nested, or two-level, system identification optimization approach to determine patient-specific joint parameters that best fit an 18 DOF lower-body model to movement data.