CHAPTER 4
RESULTS
Synthetic Marker Data without Noise
For synthetic motions without noise, each two-level optimization precisely
recovered the original marker trajectories to within an arbitrarily tight tolerance (on the
order of le-13 cm), as illustrated in Figure 3-9. At the termination of each optimization,
the optimum model parameters for the hip, knee, and ankle were recovered with mean
rotational errors less than or equal to 0.0450 and mean translational errors less than or
equal to 0.0077 cm (Appendix C).
Synthetic Marker Data with Noise
For synthetic motions with noise, the two-level optimization of the hip, knee, and
ankle resulted in mean marker distance errors equal to 0.46 cm, which is of the same
order of magnitude as the selected random continuous noise model (Table 4-1). The
two-level approach determined the original model parameters with mean rotational errors
less than or equal to 3.730 and mean translational errors less than or equal to 0.92 cm
(Appendix D). The outer-level fitness history converged rapidly (Figure 4-1) and the hip,
knee, and ankle optimizations terminated with a mean wall clock time of 41.02 hours.
Experimental Marker Data
For multi-cycle experimental motions, the mean marker distance error of the
optimal hip, knee, and ankle solutions was 0.41 cm, which is a 0.43 cm improvement
over the mean nominal error of 0.84 cm (Table 4-2). For each joint complex, the
optimum model parameters improved upon the nominal parameter data (or values found