A similarity indicator for differentiating kinematic performance between qualified tennis players
| Autor: | Luis Gerardo Melo Betancourt, Juan Diego Pulgarin Giraldo, Santiago Ramos Bermúdez, Andrés Marino Álvarez Meza, Germán Castellanos Domínguez |
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| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Kinematics
Computer science business.industry Funciones Kernel Kernel methods Pattern recognition QKLMS 030229 sport sciences 02 engineering and technology 03 medical and health sciences 0302 clinical medicine Similarity (network science) Kernel functions 0202 electrical engineering electronic engineering information engineering Similarity indicator 020201 artificial intelligence & image processing Artificial intelligence Multi-channel data business |
| Zdroj: | Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications ISBN: 9783319522760 |
| Popis: | This paper presents a data-driven approach to estimate the kinematic performance of tennis players, using kernels to extract a dynamic model of each player from motion capture (MoCap) data. Thus, a metric is introduced in the Reproducing Kernel Hilbert Space in order to compare the similarity between models so that the built kernel enhances groups separability: the baseline reference group and the group including players developing their skills. Validation is carried out on a specially constructed database that contains two main testing actions: serve and forehand strokes (carried out on a tennis court). Besides, the classical kinematic analysis is used to compare our kernel-based approach. Results show that our approach allows better representing the performance for each player regarding the ideal group |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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