Human gait state classification using artificial neural network

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

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%.

Original languageEnglish
Title of host publicationIEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIMSIVP 2014: 2014 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781479945047
DOIs
Publication statusPublished - 16 Jan 2015
Event2014 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing, CIMSIVP 2014 - Orlando
Duration: 9 Dec 201412 Dec 2014

Other

Other2014 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing, CIMSIVP 2014
CityOrlando
Period9/12/1412/12/14

Fingerprint

Neural networks
Gait analysis
Computer vision
Classifiers
Industry

Keywords

  • classification
  • Gait state
  • neural network

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction

Cite this

Kong, W., Md Saad, M. H., M A, H., & Hussain, A. (2015). Human gait state classification using artificial neural network. In 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 [7013287] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CIMSIVP.2014.7013287

Human gait state classification using artificial neural network. / Kong, Win; Md Saad, Mohamad Hanif; M A, Hannan; Hussain, Aini.

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. Institute of Electrical and Electronics Engineers Inc., 2015. 7013287.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Kong, W, Md Saad, MH, M A, H & Hussain, A 2015, Human gait state classification using artificial neural network. in 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., 7013287, Institute of Electrical and Electronics Engineers Inc., 2014 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing, CIMSIVP 2014, Orlando, 9/12/14. https://doi.org/10.1109/CIMSIVP.2014.7013287
Kong W, Md Saad MH, M A H, Hussain A. Human gait state classification using artificial neural network. In 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. Institute of Electrical and Electronics Engineers Inc. 2015. 7013287 https://doi.org/10.1109/CIMSIVP.2014.7013287
Kong, Win ; Md Saad, Mohamad Hanif ; M A, Hannan ; Hussain, Aini. / Human gait state classification using artificial neural network. 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. Institute of Electrical and Electronics Engineers Inc., 2015.
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