An Artificial Neural Network Approach for Predicting Kinematics in Handball Throws

Abdel-Rahman Ibrahim Akl; Amr Abdulfattah Hassan

doi:doi:10.11648/j.ajss.20170505.13

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An Artificial Neural Network Approach for Predicting Kinematics in Handball Throws

Abdel-Rahman Ibrahim Akl, Amr Abdulfattah Hassan

Published in American Journal of Sports Science (Volume 5, Issue 5)

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.

Published in	American Journal of Sports Science (Volume 5, Issue 5)
DOI	10.11648/j.ajss.20170505.13
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), 2017. Published by Science Publishing Group

Keywords

Neural Networks, Biomechanics, Prediction

References

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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://article.sciencepublishinggroup.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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TY  - JOUR
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
PY  - 2017
N1  - https://doi.org/10.11648/j.ajss.20170505.13
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
SP  - 35
EP  - 39
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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Author Information

Abdel-Rahman Ibrahim Akl

Faculty of Physical Education (Abo Qir), Alexandria University, Alexandria, Egypt
Amr Abdulfattah Hassan

Department of Sports Training, Faculty of Sports Education, Mansoura University, Mansoura, Egypt

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Plain Text BibTeX RIS

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

Copy | Download

@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://article.sciencepublishinggroup.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}
}

Copy | Download

TY  - JOUR
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
PY  - 2017
N1  - https://doi.org/10.11648/j.ajss.20170505.13
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
SP  - 35
EP  - 39
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  -

Copy | Download