  This paper proposes an approach estimating a gait abnormality index based on
skeletal information provided by a depth camera. Differently from related works
where the extraction of hand-crafted features is required to describe gait
characteristics, our method automatically performs that stage with the support
of a deep auto-encoder. In order to get visually interpretable features, we
embedded a constraint of sparsity into the model. Similarly to most
gait-related studies, the temporal factor is also considered as a
post-processing in our system. This method provided promising results when
experimenting on a dataset containing nearly one hundred thousand skeleton
samples.
