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Human motion recognition through fuzzy Hidden Markov Model

Conference Paper


Abstract


  • A new type of Hidden Markov Model (HMM) developed based on the fuzzy clustering result is proposed for identification of human motion. By associating the human continuous movements with a series of human motion primitives, the complex human motion could be analysed as the same process as recognizing a word by alphabet. However, because the human movements can be multi-paths and inherently stochastic, it is indisputable that a more sophisticated framework must be applied to reveal the statistic relationships among the different human motion primitives. Hence, based on the human motion recognition results derived from the fuzzy clustering function, HMM is modified by changing the formulation of the emission and transition matrices to analyse the human wrist motion. According to the experimental results, the complex human wrist motion sequence can be identified by the novel HMM holistically and efficiently. © 2005 IEEE.

Publication Date


  • 2005

Citation


  • Zhang, X., & Naghdy, F. (2005). Human motion recognition through fuzzy Hidden Markov Model. In Proceedings - International Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Vol. 2 (pp. 450-456).

Scopus Eid


  • 2-s2.0-33847214215

Web Of Science Accession Number


Start Page


  • 450

End Page


  • 456

Volume


  • 2

Abstract


  • A new type of Hidden Markov Model (HMM) developed based on the fuzzy clustering result is proposed for identification of human motion. By associating the human continuous movements with a series of human motion primitives, the complex human motion could be analysed as the same process as recognizing a word by alphabet. However, because the human movements can be multi-paths and inherently stochastic, it is indisputable that a more sophisticated framework must be applied to reveal the statistic relationships among the different human motion primitives. Hence, based on the human motion recognition results derived from the fuzzy clustering function, HMM is modified by changing the formulation of the emission and transition matrices to analyse the human wrist motion. According to the experimental results, the complex human wrist motion sequence can be identified by the novel HMM holistically and efficiently. © 2005 IEEE.

Publication Date


  • 2005

Citation


  • Zhang, X., & Naghdy, F. (2005). Human motion recognition through fuzzy Hidden Markov Model. In Proceedings - International Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Vol. 2 (pp. 450-456).

Scopus Eid


  • 2-s2.0-33847214215

Web Of Science Accession Number


Start Page


  • 450

End Page


  • 456

Volume


  • 2