Host Institution: La Trobe University

Seminar Abstract:

Recently attention in the field of gait analysis has been devoted to defining so-called “kinematics normalcy indices”. These indices have several aims. First, to define a metric that allows a comparison between an arbitrary gait pattern of the particular patient of interest and a nominally “normal” gait pattern from the general population. Second, to detect and measure changes in the gait pattern of an individual before and after an intervention or over time. Such metrics are important in clinical cohort studies where investigators need to evaluate the effect of a treatment on the gait pattern. The majority of the indices commonly used in practice are constructed point by point, which ignores the functional nature of the data generated by gait analysis.

In this study, a typical dataset was collected by motion sensors connected to the hips, knees and ankles of the examined individual. At each of these three joints, the sensor records the joint movement in three-dimensions over time normalized by gait cycle. The nature of the dataset suggests a functional approach to the analysis.

This talk presents the new proposed “functional” index, based on data depth notion, and compares its performance to other commonly used indices. All illustrations presented use real data collected by the researchers at the gait analysis laboratory. We show that the index created from functional analysis tools takes into account the shape of the waveform of the gait pattern and, therefore, provides a more meaningful tool than the existing approaches.

Seminar Convenor: Andriy Olenko

AGR IT support: Darren Condon

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