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arXiv · 1908.07415

Estimating skeleton-based gait abnormality index by sparse deep auto-encoder

Trong Nguyen Nguyen·Huu Hung Huynh·Jean Meunier
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Abstract

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.

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Trong Nguyen Nguyen, Huu Hung Huynh, Jean Meunier. 2019-08-17. Estimating skeleton-based gait abnormality index by sparse deep auto-encoder. https://doi.org/10.1109/cce.2018.8465714

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