Detection of Abnormal Gait from Skeleton Data

Meng Meng, Hassen Drira, Mohamed Daoudi, Jacques Boonaert

2016

Abstract

Human gait analysis has becomes of special interest to computer vision community in recent years. The recently developed commodity depth sensors bring new opportunities in this domain.In this paper, we study the human gait using non intrusive sensors (Kinect 2) in order to classify normal human gait and abnormal ones. We propose the evolution of inter-joints distances as spatio temporal intrinsic feature that have the advantage to be robust to location. We achieve 98% success to classify normal and abnormal gaits and show some relevant features that are able to distinguish them.

Paper Citation

in Harvard Style

Meng M., Drira H., Daoudi M. and Boonaert J. (2016). Detection of Abnormal Gait from Skeleton Data . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 131-137. DOI: 10.5220/0005722901310137

in Bibtex Style

@conference{visapp16,
author={Meng Meng and Hassen Drira and Mohamed Daoudi and Jacques Boonaert},
title={Detection of Abnormal Gait from Skeleton Data},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={131-137},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005722901310137},
isbn={978-989-758-175-5},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)
TI - Detection of Abnormal Gait from Skeleton Data
SN - 978-989-758-175-5
AU - Meng M.
AU - Drira H.
AU - Daoudi M.
AU - Boonaert J.
PY - 2016
SP - 131
EP - 137
DO - 10.5220/0005722901310137