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The research programme titled "Biomechanical parameters of athletes in the individual exercises" based on the analysis of 3D motion data and EMG, realised in the Laboratory of Motion Analysis and Interface Ergonomics was approved by the Commission for Research Ethics, No. 2/2016 dated 8.04.2016. The authors would like to thank the Tennis Academy POL-STAR Student Sports Club, and KS-WINNER Club for their support.
Materiał konferencyjny:
TAK
Nazwa konferencji:
IEEE World Congress on Computational Intelligence 2022 ; IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) 2022
This paper focuses on the aggregation of two tennis
groundstrokes: Forehand and backhand, which are performed
in every match and training session. Recognition of tennis
movements is very challenging task. Therefore it is important to
correctly classify the proper patterns of various tennis players.
This kind of study may be a part of training or development
of a player’s skills. In this study, Support Vector Machines,
Multilayer Perceptron, and Spatial-Temporal Graph Convolu-
tional Neural Networks are applied to fi nd a match and put the
move trajectories into the proper classes. The images obtained
from three dimensional data recorded using the Vicon optical
motion capture system are the input data for the classifi ers.
The images containing forehand and backhand shots are divided
into two phases: The preparation and the shot together with
the racket swinging as the fi nishing element of the move. Due
to the problems with strict classifi cation of these data, the
input is also fuzzy. In order to improve the accuracy of the
forehand and backhand recognition, the non-typical Choquet-like
integral aggregation functions are applied as well as traditional
aggregation operators like median, or voting. In particular, the
so-called pre-aggregation operators with specifi c t-norms, overlap
functions and modifi cation of the shape of the function under the
integral sign give the best results, reaching accuracy at the level
of 98.88%, which is better than the above mentioned individual
classifi ers.
