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%.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Human gait state classification using artificial neural network


    Contributors:


    Publication date :

    2014-12-01


    Size :

    242486 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Gait Biometric Recognition Using Direct Classification, TSVM, SVM and Neural Network

    Senthil Kumar, S. / Kathiresan, V. | BASE | 2017

    Free access

    Gait phase detection by using a portable system and artificial neural network

    Song-Hua Yan / Yan-Cheng Liu / Wei Li et al. | DOAJ | 2021

    Free access

    Biological image classification using rough-fuzzy artificial neural network

    Affonso, Carlos / Sassi, Renato Jose / Barreiros, Ricardo Marques | BASE | 2015

    Free access

    Radar-based Object Classification Using An Artificial Neural Network

    Lee, Dajung / Cheung, Colman / Pritsker, Dan | IEEE | 2019