Human upper limb motion analysis for post-stroke impairment assessment using video analytics

Yang, Cheng and Kerr, Andrew and Stankovic, Vladimir and Stankovic, Lina and Rowe, Philip and Cheng, Samuel (2016) Human upper limb motion analysis for post-stroke impairment assessment using video analytics. IEEE Access, 4. pp. 650-659. (https://doi.org/10.1109/ACCESS.2016.2523803)

[thumbnail of Yang-IEEEA2016-human-upper-limb-motion-analysis-for-post-stroke-impairment-assessment]
Preview
 Text. Filename: Yang_IEEEA2016_human_upper_limb_motion_analysis_for_post_stroke_impairment_assessment.pdf
Final Published Version
Download (28MB)| Preview

Abstract

Stroke is a worldwide healthcare problem which often causes long-term motor impairment, handicap, and disability. Optical motion analysis systems are commonly used for impairment assessment due to high accuracy. However, the requirement of equipment-heavy and large laboratory space together with operational expertise, makes these systems impractical for local clinic and home use. We propose an alternative, cost-effective and portable, decision support system for optical motion analysis, using a single camera. The system relies on detecting and tracking markers attached to subject's joints, data analytics for calculating relevant rehabilitation parameters, visualization, and robust classification based on graph-based signal processing. Experimental results show that the proposed decision support system has the potential to offer stroke survivors and clinicians an alternative, affordable, accurate and convenient impairment assessment option suitable for home healthcare and tele-rehabilitation.

ORCID iDs

Yang, Cheng ORCID logoORCID: https://orcid.org/0000-0002-3540-1598, Kerr, Andrew ORCID logoORCID: https://orcid.org/0000-0002-7666-9283, Stankovic, Vladimir ORCID logoORCID: https://orcid.org/0000-0002-1075-2420, Stankovic, Lina ORCID logoORCID: https://orcid.org/0000-0002-8112-1976, Rowe, Philip ORCID logoORCID: https://orcid.org/0000-0002-4877-8466 and Cheng, Samuel;
  • Item type:Article
    ID code:55526
    Dates:
    Date
    Event
    10 March 2016
    Published
    1 February 2016
    Published Online
    18 January 2016
    Accepted
    Notes:© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Keywords:rehabilitation, video analytics, graph-based signal processing, Engineering (General). Civil engineering (General), Engineering(all)
    Subjects:Technology > Engineering (General). Civil engineering (General)
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Faculty of Engineering > Biomedical Engineering
    Depositing user: Pure Administrator
    Date deposited:11 Feb 2016 10:00
    Last modified:17 Jun 2022 05:18
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/55526
CORE (COnnecting REpositories)