Innovative Modeling Method in Technical Training of High Jumpers

• Authors: Ilya Y. Krivetskiy , Grigoriy I. Popov
• Languages of publication: EN

Abstracts

EN

Introduction. This essay introduces an innovative high jump technique modeling method that uses a cascaded fuzzy neural network. An interactive system for the prediction of the success of a high jump has been designed based on this method and it allows the creation of an individual model for highly skilled athletes to control the jumper's technical training. Material and methods. The research material included a video recording of 92 high jumps and analysis by 48 kinematic characteristics. The result allowed the fine tuning of the cascaded fuzzy neural network model in order to analyse successful and failed jumps. Results and conclusions. We have developed the interactive system based on the analysis of kinematic characteristics of the high jump and this allows individual performance models to be tailored for elite athletes. With the help of this instrument, which takes into account the individual biomechanical features of an athlete's jumping style, we can analyze all stages of a jump in detail, improve the technique through the targeted correction of specific motions and achieve the optimal combination of kinematic values for the best possible result.

Keywords

EN

high jump; biomechanics; modeling; cascaded fuzzy neural network

• Publisher: De Gruyter Open
• Journal: Polish Journal of Sport and Tourism
• Year: 2012
• Volume: 19
• Issue: 4
• Pages: 253-255

Dates

published

1 - 12 - 2012

online

20 - 04 - 2013

Contributors

author

Ilya Y. Krivetskiy

• Russian State University of Physical Education, Sport, Youth and Tourism, Department of Natural Sciences, Sirenevyi Boulevard 4, Moscow, Russia, tel.: [phone], fax [phone]

author

Grigoriy I. Popov

• Russian State University of Physical Education, Sport, Youth and Tourism in Moscow Department of Natural Sciences

References

• 1. Lysenko, V.V. & Romanov D.A. (2004). Managing the technical preparation of high-skill athletes based on computer video analysis of movements.Teorija i Praktika Fizicheskoj Kuľtury: Trener (an insert) 8, 30-31. [in Russian]
• 2. Krivetskiy, I.Y., Popov G.I. & Bezrukov N.S. (2011). Model- ling the success of motions in high jumps. Informatika i Sistemy Upravlenija 2,126-132. [inRussian]
• 3. Krivetskiy, I.Y., Popov G.I. & Bezrukov N.S. (2011). Optimization the training process of high jumpers with using the individual model of technique of jump based on a cascaded fuzzy neural network. Scientific Report Series Physical Education and Sport 15, 31-35.
• 4. Bezrukov, N.S., Eremin E.L., Ermakova E.V., Kolosov V.P. & Perelman J.M. (2006). Automated system “Medical Toolbox” for diagnosing bronchial asthma using rheoencephalography results. Informatika i Sistemy Upravlenija 1, 73-80. [in Russian]

Identifiers

DOI

10.2478/v10197-012-0024-z