Automated feature assessment in instrumented gait analysis

Abstract

A methodological modular framework is presented for automated assessment of gait patterns. The processing steps of data selection, gait parameter calculation and evaluation are not limited to a specific field of application and are largely independent of case-based clinical expert knowledge. For these steps, a variety of mathematical methods was used and the validity of the approach to assess gait parameters tested by applying it to the clinical problem of Botulinum Toxin A (BTX-A) treatment of the spastic equinus foot. A set of 3670 parameters was ranked by relevance for classification of a group of 42 diplegic cerebral palsy (CP) patients and an age-matched reference group. The same procedure was performed for pre- and post-therapeutic data sets of these patients. Gait parameters of high relevance coincided well with results of previous studies based on partly manual and more subjective parameter selection. A norm distance measure is introduced to facilitate the quantification of deviations from a normal walking pattern and can be used as an overall scalar measure to evaluate differences in gait patterns or as a set of measures attributing each joint angle separately.