A multivariate gait data analysis technique: application to knee osteoarthritis.

Abstract

Modern gait analysis is a powerful non-invasive tool for calculating the mechanical factors involved in pathological processes such as knee osteoarthritis (OA). Although very accurate measurements can be made, the clinical applicability and widespread use of gait analysis have been hindered by a lack of appropriate data analysis techniques for reducing and analysing the resulting large volumes of highly correlated gait data. This paper introduces a multidimensional gait data analysis technique that simultaneously considers multiple time-varying and discrete measures, exploiting the correlation structure between and within the measures. The multidimensional analysis technique was used to detect discriminatory mechanical features of knee OA gait patterns that involved interacting changes in several gait measures, at specific time portions of the gait cycle. The two most discriminatory features described a dynamic alignment difference and a loading response difference with knee OA.