Human gait state classification using artificial neural network

Win Kong, Mohamad Hanif Saad, M. A. Hannan, Aini Hussain

Research output: Contribution to conferencePaper

5 Citations (Scopus)

Abstract

© 2014 IEEE. This paper describes an artificial neural network (ANN) based classification of human gait state. ANN is a well known classifier which is widely applied in many field of applications such as medical, business, computer vision and engineering. This study employs the understanding and knowledge of the human gait analysis. Human gait refers to one's walking pattern. In most cases, gait is used to identify individual due to its unique characteristics. In this work, the most significant gait features is the gait cycle which comprises six states. The six states are classified based on the similarity of the lower limbs' figure and the state of gait is beneficial to real time human tracking and occlusion handling. The state gait classification is performed using an ANN model and presented a performance accuracy of 89%.

Conference

ConferenceIEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIMSIVP 2014: 2014 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing, Proceedings
Period01/01/15 → …

All Science Journal Classification (ASJC) codes

  • Building and Construction
  • Energy(all)
  • Mechanical Engineering
  • Management, Monitoring, Policy and Law

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  • Cite this

    Kong, W., Saad, M. H., Hannan, M. A., & Hussain, A. (2015). Human gait state classification using artificial neural network. Paper presented at IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIMSIVP 2014: 2014 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing, Proceedings, . https://doi.org/10.1109/CIMSIVP.2014.7013287