Estimating skeleton-based gait abnormality index by sparse deep auto-encoder
Trong-Nguyen Nguyen, Huu-Hung Huynh, and Jean Meunier
IEEE International Conference on Communications and Electronics (ICCE), Vietnam, 2018
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.
