  Analysing human gait has found considerable interest in recent computer
vision research. So far, however, contributions to this topic exclusively dealt
with the tasks of person identification or activity recognition. In this paper,
we consider a different application for gait analysis and examine its use as a
means of deducing the physical well-being of people. 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.
