Mobile application for predictive modelling in hurdles race

This paper presents a mobile expert system for Android platform, named R-tificial Trainer, to support the work of a hurdles coach in planning training programmes. The main feature of the developed application is the ability to generate training loads and predict results for an athlete. It includes a database of players and allows the user to generate training plan in PDF format. The application has been tested on a dataset of athletes practising hurdles on the 110 metres. The database contains 120 training programmes made by 18 athletes. The application uses the Predictive Model Markup Language standard. The predictive models include linear models in the form of ordinary least squares and LASSO regressions and nonlinear model in the form of a multilayer perceptron with exponential function. To choose the best method, the leave-one-out cross-validation is used. The lowest validation error was achieved by multilayer perceptron.

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