Validierung prädiktiver und funktionaler Methoden zur Lokalisierung ...

9. Anhang
9.2 Abstract
The location of the hip joint centre (HJC) is essential for calculations of hip moments,
orientation of the femur and muscular lever arms. In clinical gait analysis,
the HJC is normally estimated using predictive methods based on regression equations
and anthropometric data. However, predictive methods are known to have
limited accuracy with errors up to 3 cm. Functional methods for estimation of HJC
are shown to be theoretically more accurate than predictive methods. In simulation
tests errors in HJC location were smaller than 1 mm using functional methods.
The accuracy of functional methods applied in realistic testing conditions has not
been investigated systematically.
Therefore the aims of the study were (1) comparing accuracy of functional methods
in simulation tests and realistic testing conditions, (2) comparing accuracy
of functional and predictive methods and (3) quantifying how mislocation errors
propagate to gait analysis results.
Eight predictive and two functional methods were tested: a quartic sphere-fitmethod
(S4) and a two-sided transformation technique (SCoRE). HJC estimation
was validated by using MRI of 27 subjects and a mechanical linkage. Gait analysis
was performed at different speeds.
Errors of functional methods applied to mechanical linkage were smaller than
1 mm. Errors of functional methods applied to subjects were 11 - 13 mm (SCo-
RE) and 19 - 22 mm (S4). Errors of predivtive methods were between 11 and 22
mm. Kinetic and kinematic parameters of hip and knee were influenced by HJC
mislocation.
The main findings of this study were that functional methods are more accurate
in simulation tests than in realistic testing conditions. This seems to be caused by
soft tissue artefacts. In this study functional methods were not significantly more
accurate than all of the predictive methods. Functional methods may be improved
by implementing optimization methods to reduce soft tissue artefacts.
This study can be used to establish applications of functional methods in movement
analyses and therefore to improve estimations of kinetic and kinematic
parameters.
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