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Journal of Sensors
Volume 2016 (2016), Article ID 5036857, 8 pages
Research Article

Autonomous Gait Event Detection with Portable Single-Camera Gait Kinematics Analysis System

1Department of Electronic and Electrical Engineering, University of Strathclyde, Royal College Building, 204 George Street, Glasgow G1 1XW, UK
2Biomechanics Laboratory, School of Science and Sport, Institute of Clinical Exercise & Health Science, University of the West of Scotland, Hamilton Campus, Almada Street, Hamilton ML30JB, UK
3Department of Biomedical Engineering, University of Strathclyde, Wolfson Building, 106 Rottenrow, Glasgow G4 0NW, UK
4Faculty of Health, Life & Social Sciences, School of Life Sciences, Edinburgh Napier University, Sighthill Campus, Edinburgh EH11 4BN, UK

Received 13 July 2015; Accepted 9 December 2015

Academic Editor: Sara Casciati

Copyright © 2016 Cheng Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Laboratory-based nonwearable motion analysis systems have significantly advanced with robust objective measurement of the limb motion, resulting in quantified, standardized, and reliable outcome measures compared with traditional, semisubjective, observational gait analysis. However, the requirement for large laboratory space and operational expertise makes these systems impractical for gait analysis at local clinics and homes. In this paper, we focus on autonomous gait event detection with our bespoke, relatively inexpensive, and portable, single-camera gait kinematics analysis system. Our proposed system includes video acquisition with camera calibration, Kalman filter + Structural-Similarity-based marker tracking, autonomous knee angle calculation, video-frame-identification-based autonomous gait event detection, and result visualization. The only operational effort required is the marker-template selection for tracking initialization, aided by an easy-to-use graphic user interface. The knee angle validation on 10 stroke patients and 5 healthy volunteers against a gold standard optical motion analysis system indicates very good agreement. The autonomous gait event detection shows high detection rates for all gait events. Experimental results demonstrate that the proposed system can automatically measure the knee angle and detect gait events with good accuracy and thus offer an alternative, cost-effective, and convenient solution for clinical gait kinematics analysis.