# Skeleton-based Gait Index Estimation with LSTMs

arXiv: https://arxiv.org/abs/1908.07416 · HTML: https://mlsanity.com/paper/1908.07416/

## Key facts

- Cited by 3 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 17 Aug 2019.

## Built on by (most cited first, 3 of 3)

- [GaitFormer: Revisiting Intrinsic Periodicity for Gait Recognition](https://mlsanity.com/paper/2307.13259/) (2023)
- [Self-supervised Gait-based Emotion Representation Learning from Selective Strongly Augmented Skeleton Sequences](https://mlsanity.com/paper/2405.04900/) (2024)
- [Evaluation Framework for Feedback Generation Methods in Skeletal Movement Assessment](https://mlsanity.com/paper/2404.09359/) (2024)

## Abstract (arXiv)

In this paper, we propose a method that estimates a gait index for a sequence of skeletons. Our system is a stack of an encoder and a decoder that are formed by Long Short-Term Memories (LSTMs). In the encoding stage, the characteristics of an input are automatically determined and are compressed into a latent space. The decoding stage then attempts to reconstruct the input according to such intermediate representation. The reconstruction error is thus considered as a weak gait index. By combining such weak indices over a long-time movement, our system can provide a good estimation for the gait index. Our experiments on a large dataset (nearly one hundred thousand skeletons) showed that the index given by the proposed method outperformed some recent works on gait analysis.