Movement related disorders have treatments (e.g., corrective surgeries, rehabilitation) generally characterized by variable outcomes as a result of subjective decisions based on qualitative analyses using a one-size-fits-all healthcare approach. Imagine the benefit to the healthcare provider and, more importantly, the patient, if certain clinical parameters may be evaluated pre-treatment in order to predict the post-treatment outcome. Using patient-specific models, movement related disorders may be treated with reliable outcomes as a result of objective decisions based on quantitative analyses using a patient-specific approach.

The general objective of the current work is to predict post-treatment outcome using patient-specific models given pre-treatment data. The specific objective is to develop a four-phase optimization approach to identify patient-specific model parameters and utilize the calibrated model to predict functional outcome. Phase one involves identifying joint parameters describing the positions and orientations of joints within adjacent body segments. Phase two involves identifying inertial parameters defining the mass, centers of mass, and moments of inertia for each body segment. Phase three involves identifying control parameters representing weighted components of joint torque inherent in different walking movements. Phase four involves an inverse dynamics optimization to predict function outcome. This work comprises a computational framework to create and apply patient-specific models to predict clinically significant outcomes.