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Markerless gait analysis based on a single RGB camera

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Title: Markerless gait analysis based on a single RGB camera
Authors: Gu, X
Deligianni, F
Lo, B
Chen, W
Yang, G
Item Type: Conference Paper
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.
Issue Date: 5-Apr-2018
Date of Acceptance: 10-Dec-2017
URI: http://hdl.handle.net/10044/1/56158
DOI: https://doi.org/10.1109/BSN.2018.8329654
ISBN: 9781538611104
ISSN: 2376-8894
Publisher: IEEE
Journal / Book Title: 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN)
Copyright Statement: © 2018 IEEE.
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
Funder's Grant Number: EP/L014149/1
Conference Name: International Conference on Wearable and Implantable Body Sensor Networks
Publication Status: Published
Start Date: 2018-03-04
Finish Date: 2018-03-07
Conference Place: Las Vegas, NV, USA
Online Publication Date: 2018-04-05
Appears in Collections:Department of Surgery and Cancer
Computing
Faculty of Engineering