We present how to profitably approximate swimming trajectories leveraging Radial Basis Functions (RBFs). The data of these trajectories were obtained by recording athletes of the Deaf Olympic Italian National Team while swimming. In particular, collected videos were processed by U-NET, a deep learning model architecture, resulting in some sets of two-coordinates points of virtual targets. The obtained sets of points describe trajectories that are approximated with RBFs
Approximating Swimming Trajectories with RBFs / De Santis, Giulia; Giulietti, Nicola; Caputo, Alessia; Castellini, Paolo; Maponi, Pierluigi. - 3094:(2024). (Intervento presentato al convegno International Conference of Numerical Analysis and Applied Mathematics 2022, ICNAAM 2022 tenutosi a Heraklion, Greece nel 19–25 September 2022) [10.1063/5.0210475].
Approximating Swimming Trajectories with RBFs
De Santis Giulia
;Giulietti Nicola;Caputo Alessia;Castellini Paolo;
2024-01-01
Abstract
We present how to profitably approximate swimming trajectories leveraging Radial Basis Functions (RBFs). The data of these trajectories were obtained by recording athletes of the Deaf Olympic Italian National Team while swimming. In particular, collected videos were processed by U-NET, a deep learning model architecture, resulting in some sets of two-coordinates points of virtual targets. The obtained sets of points describe trajectories that are approximated with RBFs| File | Dimensione | Formato | |
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