Picture of neon light reading 'Open'

Discover open research at Strathprints as part of International Open Access Week!

23-29 October 2017 is International Open Access Week. The Strathprints institutional repository is a digital archive of Open Access research outputs, all produced by University of Strathclyde researchers.

Explore recent world leading Open Access research content this Open Access Week from across Strathclyde's many research active faculties: Engineering, Science, Humanities, Arts & Social Sciences and Strathclyde Business School.

Explore all Strathclyde Open Access research outputs...

Autonomous gait event detection with portable single-camera gait kinematics analysis system

Yang, Cheng and Ugbolue, Ukadike C. and Kerr, Andrew and Stankovic, Vladimir and Stankovic, Lina and Carse, Bruce and Kaliarntas, Konstantinos T. and Rowe, Philip J. (2015) Autonomous gait event detection with portable single-camera gait kinematics analysis system. Journal of Sensors, 2016. ISSN 1687-7268 (In Press)

[img]
Preview
Text (Yang-etal-JOS-2015-Autonomous-gait-event-detection-with-portable-single-camera-gait)
Yang_etal_JOS_2015_Autonomous_gait_event_detection_with_portable_single_camera_gait.pdf - Final Published Version
License: Creative Commons Attribution 4.0 logo

Download (3MB) | Preview

Abstract

Laboratory-based non-wearable 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, semi-subjective, observational gait analysis. However, the requirement for large laboratory space and operational expertise makes these system 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+StructuralSimilarity-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.