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

Cheng Yang, Ukadike Ugbolue, Andrew Kerr, Vladimir Stankovic, Lina Stankovic, Bruce Carse, Konstantinos T. Kaliarntas, Philip J. Rowe

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1-8
Number of pages8
JournalJournal of Sensors
Volume2016
DOIs
Publication statusPublished - 5 Jan 2016

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