American Journal of Sports Science

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An Artificial Neural Network Approach for Predicting Kinematics in Handball Throws

Received: 26 February 2017    Accepted: 19 April 2017    Published: 26 October 2017
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Abstract

The purpose of this study was to test a new method to predict the kinematics of center of mass (COM) during the take-off phase of the handball shot by mean of multilayer perceptron neural networks (MLPs) based on data from only the force platform. Ten trials’ of handball jump shot data from the force platform were obtained. The kinetic data of jump shot trials (force, impulse, and work) were used to feed the net and the data from the force platform kinematics (acceleration, velocity, and displacement) was used to evaluate the production data of the MLP neural network model. A commercial artificial neural network software was used to predict the target kinematic parameters (NeuroDimension, 2014®). The Pearson correlations of all Kinetics parameters between the original and production data was >0.99. The MLPs model successfully predicted the target kinematics depending on kinetics in the handball jump shot under the conditions of this study.

DOI 10.11648/j.ajss.20170505.13
Published in American Journal of Sports Science (Volume 5, Issue 5, September 2017)
Page(s) 35-39
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Neural Networks, Biomechanics, Prediction

References
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Author Information
  • Faculty of Physical Education (Abo Qir), Alexandria University, Alexandria, Egypt

  • Department of Sports Training, Faculty of Sports Education, Mansoura University, Mansoura, Egypt; Institute of Sport Science, University Graz, Austria

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  • APA Style

    Abdel-Rahman Ibrahim Akl, Amr Abdulfattah Hassan. (2017). An Artificial Neural Network Approach for Predicting Kinematics in Handball Throws. American Journal of Sports Science, 5(5), 35-39. https://doi.org/10.11648/j.ajss.20170505.13

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    ACS Style

    Abdel-Rahman Ibrahim Akl; Amr Abdulfattah Hassan. An Artificial Neural Network Approach for Predicting Kinematics in Handball Throws. Am. J. Sports Sci. 2017, 5(5), 35-39. doi: 10.11648/j.ajss.20170505.13

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    AMA Style

    Abdel-Rahman Ibrahim Akl, Amr Abdulfattah Hassan. An Artificial Neural Network Approach for Predicting Kinematics in Handball Throws. Am J Sports Sci. 2017;5(5):35-39. doi: 10.11648/j.ajss.20170505.13

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  • @article{10.11648/j.ajss.20170505.13,
      author = {Abdel-Rahman Ibrahim Akl and Amr Abdulfattah Hassan},
      title = {An Artificial Neural Network Approach for Predicting Kinematics in Handball Throws},
      journal = {American Journal of Sports Science},
      volume = {5},
      number = {5},
      pages = {35-39},
      doi = {10.11648/j.ajss.20170505.13},
      url = {https://doi.org/10.11648/j.ajss.20170505.13},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajss.20170505.13},
      abstract = {The purpose of this study was to test a new method to predict the kinematics of center of mass (COM) during the take-off phase of the handball shot by mean of multilayer perceptron neural networks (MLPs) based on data from only the force platform. Ten trials’ of handball jump shot data from the force platform were obtained. The kinetic data of jump shot trials (force, impulse, and work) were used to feed the net and the data from the force platform kinematics (acceleration, velocity, and displacement) was used to evaluate the production data of the MLP neural network model. A commercial artificial neural network software was used to predict the target kinematic parameters (NeuroDimension, 2014®). The Pearson correlations of all Kinetics parameters between the original and production data was >0.99. The MLPs model successfully predicted the target kinematics depending on kinetics in the handball jump shot under the conditions of this study.},
     year = {2017}
    }
    

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    T1  - An Artificial Neural Network Approach for Predicting Kinematics in Handball Throws
    AU  - Abdel-Rahman Ibrahim Akl
    AU  - Amr Abdulfattah Hassan
    Y1  - 2017/10/26
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    DO  - 10.11648/j.ajss.20170505.13
    T2  - American Journal of Sports Science
    JF  - American Journal of Sports Science
    JO  - American Journal of Sports Science
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    PB  - Science Publishing Group
    SN  - 2330-8540
    UR  - https://doi.org/10.11648/j.ajss.20170505.13
    AB  - The purpose of this study was to test a new method to predict the kinematics of center of mass (COM) during the take-off phase of the handball shot by mean of multilayer perceptron neural networks (MLPs) based on data from only the force platform. Ten trials’ of handball jump shot data from the force platform were obtained. The kinetic data of jump shot trials (force, impulse, and work) were used to feed the net and the data from the force platform kinematics (acceleration, velocity, and displacement) was used to evaluate the production data of the MLP neural network model. A commercial artificial neural network software was used to predict the target kinematic parameters (NeuroDimension, 2014®). The Pearson correlations of all Kinetics parameters between the original and production data was >0.99. The MLPs model successfully predicted the target kinematics depending on kinetics in the handball jump shot under the conditions of this study.
    VL  - 5
    IS  - 5
    ER  - 

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