A computer assisted image analysis system for diagnosing movement disorders

  • R. Chang
  • L. Guant
  • J. A. Burne
Learning and Machine Vision
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1342)


Video image analysis is able to provide quantitative data on postural and movement abnormalities and thus has an important application in neurological diagnosis and management. The conventional techniques require patients to be videoed while wearing markers in a highly structured laboratory environment. This restricts the utility of video in routine clinical practice. We have begun development of intelligent software able to extract complete human profiles from video frames, to fit skeletal frameworks to the profiles and derive joint angles and local curvatures. By this means a given posture is reduced to a set of basic parameters that can provide input to a neural network classifier.

To test the system's performance, we videoed patients with dopa-responsive Parkinson's and age matched normals during several gait cycles, to yield 61 patient and 49 normal postures. These postures were reduced to their basic parameters and fed to the neural network classifier in various combinations. The optimal parameter sets (consisting of both swing distances and joint angles) yielded successful classification of normals and patients with an accuracy above 90%. This result demonstrated the feasibility of the approach. The technique has the potential to guide clinicians on the relative sensitivity of specific postural /gait features in diagnosis.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • R. Chang
    • 1
  • L. Guant
    • 1
  • J. A. Burne
    • 2
  1. 1.Department of Electrical EngineeringUniversity of SydneyAustralia
  2. 2.Department of Biomedical ScienceUniversity of SydneyAustralia

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