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Quantitative analysis of swimming technique
Technique models are used in various sports for technique training and learning specific target techniques. They represent optimal, person-independent movement sequences that are intended to enable athletes to solve a sporting task. The derivation of such technique models is usually theoretical (qualitative) based on biomechanical findings on technique execution.
The aim of this service research project in the Training & Movement department, funded by the Federal Institute of Sports Science (BISp) in cooperation with the Chair of Machine Learning and Machine Vision at the University of Augsburg, was to develop empirically(quantitatively) derived technique models for the sport of swimming and to use these for performance diagnostics and forecasting.
Trained neural networks were used to determine the swimming technique of top athletes, as movement analysis with reflective markers at prominent body points is not an option in performance diagnostics for top swimmers.
The detected joint positions were then analyzed for common characteristics using a factor analysis method (Principal Component Analysis, PCA). Based on the movement patterns obtained from the PCA, a mathematical-statistical description of a technique model for the individual types of swimming was created.