An Artificial Neural Network Approach for Predicting Kinematics in Handball Throws
American Journal of Sports Science
Volume 5, Issue 5, September 2017, Pages: 35-39
Received: Feb. 26, 2017;
Accepted: Apr. 19, 2017;
Published: Oct. 26, 2017
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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
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.
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.
Wagner, H., et al., Individual and team performance in team-handball: a review. J Sports Sci Med, 2014. 13 (4): p. 808-16.
Wagner, H., S. Kainrath, and E. Müller, Coordinative and tactical parameters of team-handball jump throw. The correlation of level of performance, throwing quality and selected techniquetactical parameters. Leistungssport, 2008. 38: p. 35-41.
Gorostiaga, E. M., et al., Differences in physical fitness and throwing velocity among elite and amateur male handball players. International Journal of Sports Medicine 2005. 26 (3): p. 225-232.
Wagner, H., et al., Kinematic description of elite vs. Low level players in team-handball jump throw. J Sports Sci Med, 2010. 9 (1): p. 15-23.
McKinon, W., et al., The agreement between reaction-board measurements and kinematic estimation of adult male human whole body centre of mass location during running. Physiological Measurement, 2004. 25 (6): p. 1339–1354.
Mapelli, A., et al., Validation of a protocol for the estimation of three-dimensional body center of mass kinematics in sport. Gait Posture, 2014. 39 (1): p. 460-5.
Payton, C. J. and R. M. Bartlett, Biomechanical Evaluation of Movement in Sport and Exercise. 2008, New York: Routledge.
Zatsiorsky, V. M., Kinetics of Human Motion. 2002, Champaign: Human Kinetics.
Multon, F., et al., Computer Animation of Human Walking: a Survey. J Visual Comp Animat, 1999. 10 (1): p. 39-54.
Zeltzer, D., Motor control techniques for figure animation. IEEE Computer Graphics and Applications, 1982. 2 (9): p. 53-59.
Arnaldi, B., et al. Animation control with dynamics. in In Proc. of Computer Animation. 1989.
Witkin, A. and Z. Popovic. Motion warping. in Computer Graphics Proceedings, Annual Conference Series. 1995. Los Angeles: CMU.
Ibarra, F. J. R., et al., Design of a Biomechanical Model and a Set of Neural Networks for Monitoring of Weightlifting. Research in Computing Science 2014. 80: p. 31–42.
Saber-Sheikh, K., et al., Feasibility of using inertial sensors to assess human movement. Manual Ther, 2010. 15 (1): p. 122–125.
Chau, T., A review of analytical techniques for gait data. Part 2: neural network and wavelet methods. Gait Posture, 2001. 13 (2): p. 102-20.
Lapham, A. C. and R. M. Bartlett, The use of artificial intelligence in the analysis of sports performance: a review of applications in human gait analysis and future directions for sports biomechanics. J Sports Sci, 1995. 13 (3): p. 229-37.
Verma, B. and C. Lane, Vertical jump height prediction using EMG characteristics and neural networks. Journal of Cognitive Systems Research, 2000. 1: p. 135–141.
Favre, J., et al., A neural network model to predict knee adduction moment during walking based on ground reaction force and anthropometric measurements. J Biomech, 2012. 45 (4): p. 692-8.
Rouhani, H., et al., Ambulatory assessment of 3D ground reaction force using plantar pressure distribution. Gait Posture, 2010. 32 (3): p. 311–316.
Bishop, C. M., Pattern Recognition and Machine Learning, ed. n. edition. 2007, New York: Springer. 738.
Goulermas, J. Y., et al., Regression techniques for the prediction of lower limb kinematics. J Biomech Eng, 2005. 127 (6): p. 1020-4.
Duda, R. O., P. E. Hart, and D. G. Stork, Pattern Classification, ed. n. edition. 2001, Hoboken: John Wiley and Sons. 654.
Hassan, A., The Use of Modular Feed Forward Neural Networks in Anticipating the Results of Handball Championship 2015. American Journal of Sports Science, 2015. 3 (5): p. 93-97.
Galajdová, A., D. Šimšík, and L. Madarász. Possibilities of gait parameters prediction from EMG data by Neural Networks in Computational intelligence: Proceedings of the 3nd international symposium of Hungarian researchers. 2002. Budapest: Polytechnic.
Hahn, M. E., Feasibility of estimating isokinetic knee torque using a neural network model. J Biomech, 2007. 40 (5): p. 1107–1111.
Holzreiter, S., Autolabeling 3D tracks using neural networks. Clin Biomech, 2005. 20 (1): p. 1–8.
Schollhorn, W. I., Applications of artificial neural nets in clinical biomechanics. Clin Biomech, 2004. 19 (9): p. 876-98.
Begg, R. and J. Kamruzzaman, A machine learning approach for automated recognition of movement patterns using basic, kinetic and kinematic gait data. J Biomech, 2004. 38 (3): p. 401–408.
Wang, L. and T. S. Buchanan, Prediction of joint moments using a neural network model of muscle activations from EMG signals. IEEE Translations on Neural Systems and Rehabilitation Engineering 2002. 10 (1): p. 30–37.
Liu, M. M., W. Herzog, and H. H. C. M. Savelberg, Dynamic muscle force prediction from EMG: an artificial neural network approach. J Electromyogr Kinesiol, 1999. 9 (6): p. 391–400.
Oliver, G. D., H. A. Plummer, and D. W. Keeley, Muscle activation patterns of the upper and lower extremity during the windmill softball pitch. J Strength Cond Res, 2011. 25 (6): p. 1653-8.
Rojas, I. L., et al., Biceps activity during windmill softball pitching: injury implications and comparison with overhand throwing. Am J Sports Med, 2009. 37 (3): p. 558-65.