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

arXiv: https://arxiv.org/abs/1908.07415 · HTML: https://mlsanity.com/paper/1908.07415/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2019.

## Built on by (most cited first, 1 of 1)

- [Evaluation Framework for Feedback Generation Methods in Skeletal Movement Assessment](https://mlsanity.com/paper/2404.09359/) (2024)

## Abstract (arXiv)

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.