Article thumbnail

Human Gait Classification Based on Hidden Markov Models

By Dorthe Meyer

Abstract

This paper describes a system for automatic gait analysis. In most clinical systems markers are used to determine the trajectories. We use a system for object recognition without segmentation to track body parts. From these trajectories periodic features are extracted. Another method to determine feature vectors is based on the optical flow computed by monotony operators. Both methods do not presume any markers. They are used here to produce sequences of feature vectors. These sequences of feature vectors are regarded as random variables. They are used to train hidden Markov models for different kinds of gait. The models will be used for gait classification. 1 Introduction Application of gait analysis can be found in several fields, for example medical diagnosis, physical therapy and sports. It is used to receive information about gait disorders of patients with knee or hip pain, or tumors. It is also possible to control cycles of motion for rehabilitation or training. To analyze huma..

Publisher: Infix
Year: 1997
OAI identifier: oai:CiteSeerX.psu:10.1.1.18.7879
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • http://www5.informatik.uni-erl... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.