Publication: Extracting a diagnostic gait signature

Abstract

This research addresses the question of the existence of prominent diagnostic signatures for human walking extracted from kinematics gait data. The proposed method is based on transforming the joint motion trajectories using wavelets to extract spatio-temporal features which are then fed as input to a vector quantiser; a self-organising map for classification of walking patterns of individuals with and without pathology. We show that our proposed algorithm is successful in extracting features that successfully discriminate between individuals with and without locomotion impairment.

This publication is referred to in the following documents and applications

2 applications

1. Identification of appropriate artificial intelligence methods and indices for the interpretation of gait data in subjects with stroke - a systematic review
2. Exploratory data analysis of gait data from patients with stroke