Movement simulations consist of models involving skeletal structure, muscle paths,

musculotendon actuation, muscle excitation-contraction coupling, and a motor task goal

(Pandy, 2001). Development of an accurate inverse dynamic model of the skeletal

structure is a significant first step toward creating a predictive patient-specific forward

dynamic model to perform movement simulations.

 The precision of dynamic analyses is fundamentally associated with the accuracy of

kinematic model parameters such as segment lengths, joint positions, and joint

orientations (Andriacchi and Strickland, 1985; Challis and Kerwin, 1996; Cappozzo et

al., 1975; Davis, 1992; Holden and Stanhope, 1998; Holden and Stanhope, 2000; Stagni

et al., 2000). Understandably, a model constructed of rigid links within a multi-link chain

and simple mechanical approximations of joints will not precisely match the human

anatomy and kinematics. The model should provide the best possible agreement to

experimental motion data within the bounds of the joint models selected (Sommer and

Miller, 1980).

 Benefits of Two-Level Optimization

 This thesis presents a nested (or two-level) system identification optimization

approach to determine patient-specific joint parameters that best fit a three-dimensional

(3D), 18 degree-of-freedom (DOF) lower-body model to an individual's movement data.

The two-level technique combines the advantages of using optimization to determine

both the position of model segments from marker data and the anatomical joint axes

linking adjacent segments. By formulating a two-level objective function to minimize

marker coordinate errors, the resulting optimum model more accurately represents

experimental marker data (or a specific patient and his or her motion) when compared to

a nominal model defined by joint axes prediction methods.