Automation enhancement and accuracy investigation of a portable single-camera gait analysis system

C. Yang, U. C. Ugbolue, D. McNicol, V. Stankovic, L. Stankovic, A. Kerr, B. Carse, K. Kaliarntas, P. J. Rowe

Research output: Contribution to journalArticle

2 Citations (Scopus)
17 Downloads (Pure)

Abstract

While optical motion analysis systems can provide high-fidelity gait parameters, they are usually impractical for local clinics and home use, due to high cost, requirement for large space, and lack of portability. In this study, the authors focus on a cost-effective and portable, single-camera gait analysis solution, based on video acquisition with calibration, autonomous detection of frames-of-interest, Kalman-filter + structural-similarity-based marker tracking, and autonomous knee angle calculation. The proposed system is tested using 15 participants, including 10 stroke patients and 5 healthy volunteers. The evaluation of autonomous frames-of-interest detection shows only 0.2% difference between the frame number of the detected frame compared to the frame number of the manually labelled ground truth frame, and thus can replace manual labelling. The system is validated against a gold standard optical motion analysis system, using knee angle accuracy as metric of assessment. The accuracy investigation between the RGB- and the greyscale-video marker tracking schemes shows that the greyscale system suffers from negligible accuracy loss with a significant processing speed advantage. Experimental results demonstrate that the proposed system can automatically estimate the knee angle, with R-squared value larger than 0.95 and Bland-Altman plot results smaller than 3.0127° mean error.

Original languageEnglish
Pages (from-to)563-571
Number of pages9
JournalIET Science, Measurement and Technology
Volume13
Issue number4
Early online date21 Jan 2019
DOIs
Publication statusPublished - 30 Jun 2019

Keywords

  • gait analysis
  • optical motion analysis
  • portable gait assessment

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  • Student Theses

    Multimedia motion analysis for remote health monitoring

    Author: Yang, C., 7 May 2017

    Supervisor: Stankovic, V. (Supervisor) & Stankovic, L. (Supervisor)

    Student thesis: Doctoral Thesis

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