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- W3100485717 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." @default.
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- W3100485717 date "2018-07-01" @default.
- W3100485717 modified "2023-10-17" @default.
- W3100485717 title "Estimating skeleton-based gait abnormality index by sparse deep auto-encoder" @default.
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- W3100485717 doi "https://doi.org/10.1109/cce.2018.8465714" @default.
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