Advertisement

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)

Abstract

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.

Keywords

Rehabilitation Stroke Physical therapy Upper extremity Pattern recognition 

Notes

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.

References

  1. 1.
    Roger, V.L., et al.: Heart disease and stroke statistics—2011 update: a report from the american heart association. Circulation (2010) Google Scholar
  2. 2.
    Economic Impact of Acute Ischemic Stroke. Medical News Today, 26 February 2006Google Scholar
  3. 3.
    Krebs, H.I., Volpe, B.T., Aisen, M.L., Hogan, N.: Increasing productivity and quality of care: robot-aided neuro-rehabilitation. J. Rehabil. Res. Dev. 37(6), 639–652 (2000)Google Scholar
  4. 4.
    Krebs, H.I., Hogan, N., Aisen, M.L., Volpe, B.T.: Robot-aided neurorehabilitation. IEEE Trans. Rehabil. Eng. 6(1), 75–87 (1998)CrossRefGoogle Scholar
  5. 5.
    Wolfe, C.: The burden of stroke. In: Wolfe, C., Rudd, T., Beech, R. (eds.) Stroke Services and Research. The Stroke Association, London (1996)Google Scholar
  6. 6.
    Taylor, T.N., Davis, P.H., Torner, J.C., Holmes, J., Meyer, J.W., Jacobson, M.F.: Lifetime cost of stroke in the United States. Stroke 27(9), 1459–1466 (1996)CrossRefGoogle Scholar
  7. 7.
    Richards, L., Pohl, P.: Theraputic interventions to improve upper extremity recovery and function. Clin. Geriatr. Med. 5, 819–832 (1999)CrossRefGoogle Scholar
  8. 8.
    Merritt, C.: A pneumatically actuated brace designed for upper extremity stroke rehabilitation. M.S. thesis, Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, USA (2003)Google Scholar
  9. 9.
    Davoodi, R., Brown, I.E., Loeb, G.E.: Advanced modeling environment for developing & testing FES control systems. Med. Eng. Phys. 25, 3–9 (2003)CrossRefGoogle Scholar
  10. 10.
    Davoodi, R., Urata, C., Hauschild, M., Khachani, M., Loeb, G.: Model-based development of neural prostheses for movement. IEEE Trans. Bio. Eng. 54(11), 1909–1918 (2007)CrossRefGoogle Scholar
  11. 11.
    Abbott, J.J., Hager, G.D., Okamura, A.M.: Steady-hand teleoperation with virtual fixtures. In: Proceedings of 12th IEEE International Workshop Robot and Human Interactive Communication (2003)Google Scholar
  12. 12.
    Li, J., Wang, Z.J., Eng, J.J., McKeown, M.J.: Bayesian network modeling for discovering “dependent synergies” among muscles in reaching movements. IEEE Trans. Biomed. Eng. 55(1), 298–310 (2008)CrossRefGoogle Scholar
  13. 13.
    Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)zbMATHGoogle Scholar
  14. 14.
    Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. Wiley, New York (2001)zbMATHGoogle Scholar
  15. 15.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)zbMATHGoogle Scholar
  16. 16.
    Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B (Methodol.) 58(1), 267–288 (1996)MathSciNetzbMATHGoogle Scholar
  17. 17.
    Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, 1st edn. Cambridge University Press, Cambridge (2000)CrossRefGoogle Scholar
  18. 18.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar
  19. 19.
    Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, San Francisco, pp. 1137–1143 (1995)Google Scholar

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

Personalised recommendations