This paper presents a method of monocular human motion tracking for estimation of hurdle clearance kinematic parameters. The analysis involved 10 image sequences of five hurdlers at various training levels. Recording of the sequences was carried out under simulated starting conditions of a 110 m hurdle race. The parameters were estimated using the particle swarm optimization algorithm and they are based on analysis of the images recorded with a 100 Hz camera. The proposed method does not involve using any special clothes, markers, inertial sensors, etc. As the quality criteria, the mean absolute error and mean relative error were used. The level of computed errors justifies the use of this method to estimate hurdle clearance parameters.

The final result in a 400 m hurdles race (400mH) is relative to the motor preparation, technique of clearing hurdles as well as the adopted strategy of the race, including temporal aspects (split times in particular parts of the race) and spatial elements (the number of strides taken between subsequent hurdles). The objective of the study was to identify an optimal strategy for the 400mH race, including the stride pattern and split times. Data employed for this study were derived from results of 273 races held during the men’s finals of international events (Olympic Games, World and European Championships) held from 1968 to 2015. To determine the strategies in the race, three main hurdle sections were identified – 1-4H, 4-7H and 7-10H. In each part, the fast (best results), average and slow (worst results) performing groups of hurdlers were distinguished. The analysis of adopted strategies was carried out taking into account 26 variables (main, basic, temporal and spatial). Basic statistical data, correlations and analysis of variance (ANOVA) were used. Results highlight the use of a variety of strategies, of which selection depends, among others, on body composition and the level of motor abilities (speed, speed endurance and explosive strength), as well as hurdling technique. Especially, the endurance strategy appears to be the most effective one, as it is a characteristic of best performances of many hurdlers. The analysis demonstrates that at the highest sports level the strategy of 400 m hurdles should be analyzed individually.

This study presents the application of regression shrinkage and artificial neural networks in predicting the results of 400-metres hurdles races. The regression models predict the results for suggested training loads in the selected three-month training period. The material of the research was based on training data of 21 Polish hurdlers from the Polish National Athletics Team Association. The athletes were characterized by a high level of performance. To assess the predictive ability of the constructed models a method of leave-one-out cross-validation was used. The analysis showed that the method generating the smallest prediction error was the LASSO regression extended by quadratic terms. The optimal model generated the prediction error of 0.59 s. Otherwise the optimal set of input variables (by reducing 8 of the 27 predictors) was defined. The results obtained justify the use of regression shrinkage in predicting sports outcomes. The resulting model can be used as a tool to assist the coach in planning training loads in a selected training period.

This paper presents a web-oriented expert system, named iHurdling, to predict results and generate training loads for 110 and 400 m hurdles races. The database contains 40 annual training programmes for the 110 m hurdles and 48 programmes for the 400 m hurdles. The predictive models include linear regressions in the form of ordinary least squares, ridge, LASSO, elastic net and nonlinear models in the form of a radial basis function neural network and fuzzy rule-based system. The leave-one-out cross-validation method is used to compare, and choose the best model. It shows that the proposed fuzzy-based model has the lowest validation error. The developed web application can support a coach in planning training programmes for hurdles races. It allows the athlete’s results to be predicted and can generate training loads for an athlete, selected from database. The application can be run on a computer or a mobile device. The system was implemented using the R programming language with the Shiny framework and additional packages. The limitations of the presented approach are related to the lack of consideration of an athlete’s physiological and psychological parameters, but the generated training programs might be used as a suggestion for the coach.