# Applying Adversarial Auto-encoder for Estimating Human Walking Gait Abnormality Index

## TL;DR

This paper introduces an adversarial auto-encoder approach to estimate human gait quality from 3D point cloud sequences, providing a novel application of GANs for gait analysis with improved accuracy.

## Contribution

It presents a new method combining auto-encoders and GANs to assess gait quality directly from 3D point clouds, a novel perspective in gait analysis.

## Key findings

- Outperforms existing methods on a large dataset of nearly 100,000 point clouds.
- Effectively extracts gait quality indices from 3D postural data.
- Demonstrates the utility of adversarial auto-encoders in biomedical gait assessment.

## Abstract

This paper proposes an approach that estimates human walking gait quality index using an adversarial auto-encoder (AAE), i.e. a combination of auto-encoder and generative adversarial network (GAN). Since most GAN-based models have been employed as data generators, our work introduces another perspective of their application. This method directly works on a sequence of 3D point clouds representing the walking postures of a subject. By fitting a cylinder onto each point cloud and feeding obtained histograms to an appropriate AAE, our system is able to provide different measures that may be used as gait quality indices. The combinations of such quantities are also investigated to obtain improved indicators. The ability of our method is demonstrated by experimenting on a large dataset of nearly 100 thousands point clouds and the results outperform related approaches that employ different input data types.