By Jan Cabri, João Barreiros, Pedro Pezarat Correia
This publication constitutes completely revised and chosen papers from the second one foreign Congress on activities technology examine and know-how aid, icSPORTS 2014, held in Rome, Italy, in October 2014.
The eight completely revised and prolonged papers offered during this quantity have been conscientiously reviewed and chosen from initially 131 submissions.
Read Online or Download Sports Science Research and Technology Support: Second International Congress, icSPORTS 2014, Rome, Italy, October 24-26, 2014, Revised Selected Papers PDF
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Additional resources for Sports Science Research and Technology Support: Second International Congress, icSPORTS 2014, Rome, Italy, October 24-26, 2014, Revised Selected Papers
Sample text
Lett. 27(7), 773–780 (2006) Prediction of the Results in 400-Metres Hurdles in Two Different Time Intervals Using Statistical Learning Methods Krzysztof Przednowek1(B) , Janusz Iskra2 , and Karolina H. pl Abstract. This research presents the selected statistical learning methods in predicting the results of 400 m hurdles in two different time intervals. The calculated models predict results in selected training period and in annual training cycle. In the study, detailed training programs of 21 Polish hurdlers were analyzed.
Image Vis. Comput. 28(11), 1530–1547 (2010) Monocular Tracking of Human Motion in Evaluation of Hurdle Clearance 29 9. : Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks. vol. 4, pp. 1942–1948. IEEE Press, Piscataway, NJ (1995) ´ 10. : View independent human gait recognition using markerless 3D human motion capture. , Wojciechowski, K. ) ICCVG 2012. LNCS, vol. 7594, pp. 491–500. Springer, Heidelberg (2012) 11. : Realtime multi-view human motion tracking using particle swarm optimization with resampling.
Prediction of the Results in 400-Metres Hurdles in TwoDifferent Time 33 Another examined time interval was the annual training cycle, in which the training loads were considered as sums of the given training means used throughout the whole macro-cycle. In the annual training cycle, 25 variables were specified. In order to develop models for the annual training cycle, a total of 48 training plans were used. , p, and the one output Y . The input variables Xj are called predictors, whereas the output variable Y – a response.