Markerless Gait Analysis Based on a Single RGB Camera

Gu, X., Deligianni, F. , Lo, B., Chen, W. and Yang, G.Z. (2018) Markerless Gait Analysis Based on a Single RGB Camera. In: 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Las Vegas, NV, USA, 04-07 Mar 2018, pp. 42-45. ISBN 9781538611098 (doi: 10.1109/BSN.2018.8329654)

Abstract

Gait analysis is an important tool for monitoring and preventing injuries as well as to quantify functional decline in neurological diseases and elderly people. In most cases, it is more meaningful to monitor patients in natural living environments with low-end equipment such as cameras and wearable sensors. However, inertial sensors cannot provide enough details on angular dynamics. This paper presents a method that uses a single RGB camera to track the 2D joint coordinates with state-of-the-art vision algorithms. Reconstruction of the 3D trajectories uses sparse representation of an active shape model. Subsequently, we extract gait features and validate our results in comparison with a state-of-the-art commercial multi-camera tracking system. Our results are comparable to those from the current literature based on depth cameras and optical markers to extract gait characteristics.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Deligianni, Dr Fani
• Authors: Gu, X., Deligianni, F., Lo, B., Chen, W., and Yang, G.Z.
• College/School: College of Science and Engineering > School of Computing Science
• ISSN: 2376-8894
• ISBN: 9781538611098
• Published Online: 05 April 2018
• Copyright Holders: Copyright © 2018 IEEE
• First Published: First published in 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN): 42-45
• Publisher Policy: Reproduced in accordance with the publisher copyright policy