Volume 5, Issue 5, September 2017, Page: 35-39
An Artificial Neural Network Approach for Predicting Kinematics in Handball Throws
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; Institute of Sport Science, University Graz, Austria
Received: Feb. 26, 2017;       Accepted: Apr. 19, 2017;       Published: Oct. 26, 2017
DOI: 10.11648/j.ajss.20170505.13      View  1161      Downloads  112
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.
Neural Networks, Biomechanics, Prediction
To cite this article
Abdel-Rahman Ibrahim Akl, Amr Abdulfattah Hassan, An Artificial Neural Network Approach for Predicting Kinematics in Handball Throws, American Journal of Sports Science. Vol. 5, No. 5, 2017, pp. 35-39. doi: 10.11648/j.ajss.20170505.13
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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