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  • Research Article
  • Open Access

Video Analysis of Human Gait and Posture to Determine Neurological Disorders

EURASIP Journal on Image and Video Processing20082008:380867

  • Received: 15 January 2007
  • Accepted: 7 March 2008
  • Published:


This paper investigates the application of digital image processing techniques to the detection of neurological disorder. Visual information extracted from the postures and movements of a human gait cycle can be used by an experienced neurologist to determine the mental health of the person. However, the current visual assessment of diagnosing neurological disorder is based very much on subjective observation, and hence the accuracy of diagnosis heavily relies on experience. Other diagnostic techniques employed involve the use of imaging systems which can only be operated under highly constructed environment. A prototype has been developed in this work that is able to capture the subject's gait on video in a relatively simple setup, and from which to process the selected frames of the gait in a computer. Based on the static visual features such as swing distances and joint angles of human limbs, the system identifies patients with Parkinsonism from the test subjects. To our knowledge, it is the first time swing distances are utilized and identified as an effective means for characterizing human gait. The experimental results have shown a promising potential in medical application to assist the clinicians in diagnosing Parkinsonism.


  • Neurological Disorder
  • Joint Angle
  • Visual Feature
  • Gait Cycle
  • Digital Image Processing

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Authors’ Affiliations

C Management Services Pty Ltd, CQU Melbourne International Campus, Melbourne, Australia
Department of Electrical and Computer Engineering, Ryerson University, Toronto, Ontario, Canada, M5B 2K3
Department of Computer and Information Science, University of South Australia, South Australia, Australia


© Howard Lee et al. 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.