Movement Based Classification of People with Stroke Through Automated Analysis of Three-Dimensional Motion Data

  • John W. Kelly
  • Steve Leigh
  • Carol Giuliani
  • Rachael Brady
  • Martin J. McKeown
  • Edward GrantEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 867)


Active recovery of motor function following a stroke requires intense and repetitive physical therapy. The effectiveness of such therapy can be greatly improved if it is fully customized for each patient. Motion tracking and machine learning algorithms can assist therapists in designing the therapy regimens, thereby saving valuable time. In this study, three-dimensional upper body movements both of people who had suffered a stroke and of healthy subjects were recorded as they performed a reaching task. A support vector machine with a five-dimensional feature space was used to automatically distinguish between the movements of people with stroke and those of healthy subjects. The success rate for this task peaked at over 95%. While this specific task is trivial for a clinician, it provides proof of concept, and a foundation for further work in developing classifiers that can locate more specific problems. Such a classifier may help clinicians treat the root cause of a complicated movement deficiency with a patient specific rehabilitation program. The results indicate that using machine learning approaches for analyzing stroke patient movement data shows potential for reducing clinician’s workloads while providing improved treatment to specific subjects.


Rehabilitation Stroke Physical therapy Upper extremity Pattern recognition 


Acknowledgements and Funding

Funding for this work was provided by the Park Foundation, the Tau Beta Pi Association, and the Whitaker Foundation (MJM). Thanks to David Zielinski for technical expertise with the virtual reality systems.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • John W. Kelly
    • 1
  • Steve Leigh
    • 2
  • Carol Giuliani
    • 2
  • Rachael Brady
    • 3
  • Martin J. McKeown
    • 4
  • Edward Grant
    • 1
    Email author
  1. 1.Center for Robotics and Intelligent Machines, Department of Electrical and Computer EngineeringNorth Carolina State UniversityRaleighUSA
  2. 2.Center for Human Movement Science, Division of Physical TherapyThe University of North Carolina at Chapel HillChapel HillUSA
  3. 3.Visualization Technology Group, Pratt School of EngineeringDuke UniversityDurhamUSA
  4. 4.Pacific Parkinson’s Research Centre, Department of Medicine (Neurology)The University of British ColumbiaVancouverCanada

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