A multidimensional analysis technique for discriminating between normal and knee osteoarthritic gait patterns

Knee osteoarthritis (OA) is a complex disease process that involves multiple, correlated mechanical factors.

The major objective of this thesis was to develop a multidimensional gait analysis technique to discriminate between the gait patterns of groups of subjects. This technique would simultaneously consider multiple time varying and constant gait measures.

The multidimensional technique was applied to detect and interpret gait pattern differences between 63 normal subjects and 50 subjects with severe knee OA.

The discriminatory features involved the interaction of a number of gait measures throughout the gait cycle and therefore represented multidimensional gait phenomena, indistinguishable with visual gait observation, and undetectable with univariate data analysis techniques. The results of this thesis indicated that multidimensional, correlated gait data could be reduced to an interpretable difference measure, sensitive to gait pattern changes associated with knee OA. (Abstract shortened by UMI.)