By seeding one of the nodes with a relatively optimal set of initial values, the

computation time may be significantly decreased. By doubling the number of parallel

processors, the computation time declines nearly 50%. Decreasing the number of time

frames of marker data additionally reduces the computation time. For example, the mean

optimization time using experimental data for 50 time frames equals 35.94 hours, 19 time

frames equals 12.82 hours, and 13 time frames equals 11.24 hours. Further study is

necessary to establish the minimum number of marker data time frames required to

effectively determine joint axes parameters.

Multi-Cycle and One-Half-Cycle Joint Motions

 The two-level optimization results vary depending on whether marker data time

frames consist of multi-cycle or one-half-cycle joint motions. In other words, the

determination of patient-specific model parameters is significantly influenced by the

marker trajectories contained within the chosen set of data. Given a set of marker data,

the two-level optimization establishes invariable model parameters that best fit the

mathematical model to the measured experimental motion. Understandably, a model

constructed from one marker data set may not adequately represent a considerably

different marker data set. To perform accurate dynamic analyses, joint motions used to

generate the model should be consistent with those motions that will be used in the

analyses.

 The small differences between sets of two-level optimization results for the hip and

knee joint motions indicate the reliability of the model parameter values. Much larger

differences occurred between sets of model parameters determined for the ankle joint.

Two major factors contributing to these differences are the rotational ankle model

parameters pi and p3. On one hand, the model parameters may truly vary throughout the