and demonstrate the degree of inaccuracy inherent when the new approach is not

implemented.

 Through the enhancement of model parameter values found in the literature, the

two-level optimization approach successfully reduces the fitness errors between the

patient-specific model and the experimental motion data. More specifically, to quantify

the improvement of the current results compared to previous values found in the

literature, the mean marker distance errors were reduced by 31.53% (hip), 51.94% (knee),

and 59.76% (ankle).

 The precision of dynamic analyses made for a particular patient depends on the

accuracy of the patient-specific kinematic parameters chosen for the dynamic model.

Without expensive medical images, model parameters are only estimated from external

landmarks that have been identified in previous studies. The estimated (or nominal)

values may be improved by formulating an optimization problem using motion-capture

data. By using a two-level optimization technique, researchers may build more accurate

biomechanical models of the individual human structure. As a result, the optimal models

will provide reliable foundations for future dynamic analyses and optimizations.