Human gait state classification using artificial neural network

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

Research output: Contribution to conferencePaper

3 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 → …

Fingerprint

artificial neural network
Neural networks
Gait analysis
computer vision
walking
Computer vision
limb
Classifiers
engineering
Industry
analysis

All Science Journal Classification (ASJC) codes

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

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
Kong, Win ; Saad, Mohamad Hanif ; Hannan, M. A. ; Hussain, Aini. / 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, .
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abstract = "{\circledC} 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{\%}.",
author = "Win Kong and Saad, {Mohamad Hanif} and Hannan, {M. A.} and Aini Hussain",
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Kong, W, Saad, MH, Hannan, MA & 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, 01/01/15, . https://doi.org/10.1109/CIMSIVP.2014.7013287

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

2015. 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, .

Research output: Contribution to conferencePaper

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N2 - © 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%.

AB - © 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%.

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Kong W, Saad MH, Hannan MA, Hussain A. Human gait state classification using artificial neural network. 2015. 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