Janusz Iskra

Estimation of hurdle clearance parameters using a monocular human motion tracking method

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.

Regression shrinkage and neural models in predicting the results of 400-metres hurdles races.

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.

Predictive Modeling in 400-Metres Hurdles Races

The paper presents the use of linear and nonlinear multivariable models as tools to predict the results of 400-metres hurdles races in two different time frames. The constructed models predict the results obtained by a competitor with suggested training loads for a selected training phase or for an annual training cycle. All the models were constructed using the training data of 21 athletes from the Polish National Team. The athletes were characterized by a high level of performance (score for 400 metre hurdles: 51.26±1.24 s). The linear methods of analysis include: classical model of ordinary least squares (OLS) regression and regularized methods such as ridge regression, LASSO regression. The nonlinear methods include: artificial neural networks as multilayer perceptron (MLP) and radial basis function (RBF) network. In order to compare and choose the best model leave-one-out cross-validation (LOOCV) is used. The outcome of the studies shows that Lasso shrinkage regression is the best linear model for predicting the results in both analysed time frames. The prediction error for a training period was at the level of 0.69 s, whereas for the annual training cycle was at the level of 0.39 s. Application of artificial neural network methods failed to correct the prediction error. The best neural network predicted the result with an error of 0.72 s for training periods and 0.74 for annual training cycle. Additionally, for both training frames the optimal set of predictors was calculated.

Planning Training Loads for The 400 M Hurdles in Three-Month Mesocycles Using Artificial Neural Networks

Abstract This paper presents a novel approach to planning training loads in hurdling using artificial neural networks. The neural models performed the task of generating loads for athletes’ training for the 400 meters hurdles. All the models were calculated based on the training data of 21 Polish National Team hurdlers, aged 22.25 ± 1.96, competing between 1989 and 2012. The analysis included 144 training plans that represented different stages in the annual training cycle. The main contribution of this paper is to develop neural models for planning training loads for the entire career of a typical hurdler. In the models, 29 variables were used, where four characterized the runner and 25 described the training process. Two artificial neural networks were used: a multi-layer perceptron and a network with radial basis functions. To assess the quality of the models, the leave-one-out cross-validation method was used in which the Normalized Root Mean Squared Error was calculated. The analysis shows that the method generating the smallest error was the radial basis function network with nine neurons in the hidden layer. Most of the calculated training loads demonstrated a non-linear relationship across the entire competitive period. The resulting model can be used as a tool to assist a coach in planning training loads during a selected training period.

A Fuzzy-based Software Tool Used to Predict 110m Hurdles Results During the Annual Training Cycle

This paper describes a fuzzy-based software tool for predicting results in the 110m hurdles. The predictive models were built on using 40 annual training cycles completed by 18 athletes. These models include: ordinary least squares regression, ridge regression, LASSO regression, elastic net regression and nonlinear fuzzy correction of least squares regression. In order to compare them, and choose the best model, leave-one-out cross-validation was used. This showed that the fuzzy corrector proposed in this paper has the lowest prediction error. The developed software can support a coach in planning an athlete’s annual training cycle. It allows the athlete’s results to be predicted, and in this way, for the best training loads to be selected. The tool is a web-based interactive application that can be run from a computer or a mobile device. The whole system was implemented using the R programming language with additional packages.

Markerless Motion Tracking in Evaluation of Hurdle Clearance Parameters.

In this study, implementation of markerless method of human body motion tracking as a tool of measurement of hurdle clearance kinematic parameters was presented. The analysis involved 5 hurdle runners at various training levels. Recording of video sequences was carried out under simulated starting conditions of a 110 m hurdle race. Kinematic parameters were determined based on the analysis of images recorded with a 100 Hz monocular camera. The suggested method does not involve using any special clothes, markers or estimation support techniques. In the study, the basic numerical characteristics of twenty estimated parameters were presented. The accuracy of determined hurdle clearance parameters was verified by comparison of estimated poses with the ground truth pose. As the quality criterion, the MAE (Mean Absolute Error) was adopted. In the distance parameters, the least error was obtained for the distance between the center of mass (CM) and the hurdle at the first hurdle clearance phase (MAE = 22.0 mm). For the angular parameters, the least error was obtained for the leg angle at the first hurdle clearance phase (MAE = 3.1◦). The level of computed errors showed that the presented method can be used for estimation of hurdle clearance kinematic parameters.

Kinematic Analysis of the Upper Limbs in Stepping over the Hurdle - The Use of IMU-based Motion Capture.

This paper presents an analysis of the kinematic parameters of the upper limbs in stepping over the hurdle. Stepping over the hurdle is a specific exercise practised throughout the year. In this exercise, three key points were analysed in take-off, flight and landing phases. The aim of the study was to use the IMU-based (inertial measurement unit) motion capture system to evaluate the movement of the hurdlers’ upper limbs while stepping over the hurdle using both the better leg, and the worse leg. The sequences were obtained using 18 sensors working at a frequency of 120 Hz. The analysis was made using two high-achieving athletes. This paper presents the linear velocities and the trajectory of selected segments of the upper limbs. In most cases the velocities of the segments were higher for the better leg. The analysis shows that during the specific exercise of stepping over the hurdle attention should be paid to the movement of the trail arm in the landing phase.

Multiview Human Body Tracking of Hurdle Clearance: A Case Study.

This initial research is a case study that uses a multiview human body tracking method as a tool to measure hurdle clearance kinematic parameters. This study is conducted on a hurdler representing a high sport level, who is a participant in the European and World Championships and the Olympic Games. The video recordings were made under simulated starting conditions of a 110 m hurdle race. Kinematic parameters are estimated based on the analysis of images from a multicamera system. The images were recorded with a resolution of 1920x1080 and with a frequency of 100 Hz. The proposed method does not use any special clothes, markers or other estimation support techniques. The parameters of the hurdle clearance were compared with the parameters obtained from ground truth poses. Mean Absolute Error and Mean Relative Error were used as the quality criteria.

Planning Training Loads to Develop Technique and Rhythm in the 400m Hurdles using RBF Network

In this paper training loads to develop technique and rhythm in hurdles are presented. The training loads were generated using an artificial neural networks model with radial basis functions. The analysis included 21 hurdlers who were members of the Polish National Team. The calculations for the neural model were made using 48 training programmes. The evaluation of the models was carried out using the cross-validation method. Five independent variables (age, body height, body weight, current result and expected result) and four dependent variables representing the selected training loads were analyzed. The determined model generated training loads with an error of approximately 21%. Experimental results showed the training programme for a hypothetical athlete. The analysis shows that all the examined training loads are of a non-linear nature. The proposed solution can be used as a tool to support planning for selected training loads in 400 m hurdles.

Prediction of the Results in 400-Metres Hurdles in Two Different Time Intervals Using Statistical Learning Methods

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. Building of the predictive models was conducted by means of regression shrinkage and artificial neural networks. To evaluate calculated models the leave-one-out cross validation was used. The outcome of the studies shows that the best method in both analysed time intervals was LASSO regression. The prediction error for a training period was at the level of 0.67 s, whereas for the annual training cycle was at the level of 0.39 s. Additionally, for both time intervals the optimal set of predictors was calculated. In terms of training periods, the LASSO model eliminated 8 variables, whereas in terms of the annual training cycle 12 variables were eliminated.