Disease Estimation Using Gait Videos by Separating Individual Features Based on Disentangled Representation Learning

Shiori Furukawa, Noriko Takemura

2025

Abstract

With the aging of society, the number of patients with gait disturbance is increasing. Lumbar spinal canal stenosis (LCS) and cervical spondylotic myelopathy (CSM) are representative diseases that cause gait disturbance. However, diagnosing these diseases takes a long time because of the wide variety of medical departments and lack of screening tests. In this study, we propose a method to recognize LCS and CSM using patients’ walking videos. However, the gait images of patients contain not only disease features but also individual features, such as body shape and hairstyle. Such individual features may reduce the accuracy of disease estimation. Therefore, we aim to achieve highly accurate disease estimation by separating and removing individual features from disease features using a deep learning model based on a disentangled representation learning approach. In evaluation experiments, we confirmed the usefulness of the proposed method by verifying the accuracy of different model structures and different diagnostic tasks to be estimated.

Paper Citation

in Harvard Style

Furukawa S. and Takemura N. (2025). Disease Estimation Using Gait Videos by Separating Individual Features Based on Disentangled Representation Learning. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 919-925. DOI: 10.5220/0013168200003912

in Bibtex Style

@conference{visapp25,
author={Shiori Furukawa and Noriko Takemura},
title={Disease Estimation Using Gait Videos by Separating Individual Features Based on Disentangled Representation Learning},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2025},
pages={919-925},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013168200003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - Disease Estimation Using Gait Videos by Separating Individual Features Based on Disentangled Representation Learning
SN - 978-989-758-728-3
AU - Furukawa S.
AU - Takemura N.
PY - 2025
SP - 919
EP - 925
DO - 10.5220/0013168200003912
PB - SciTePress