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The research programme titled "Biomechanical parameters of athletes in individual exercises" and 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.
IEEE World Congress on Computational Intelligence 2022 ; IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) 2022
Action classifi cation makes signifi cant contributions
to sport analysis, especially for athletes’ training evaluations. In
this study, the identifi cation of two basic tennis groundstrokes
were taken into consideration, because these types of movements
are performed in every match or training. As the input dataset,
the recorded images presenting the registered movements with
the optical motion capture system were applied. They show
a three dimensional model of the tennis player together with
a tennis racket. The Attention Temporal Graph Convolutional
Network was used as a classifi cation tool for this sophisticated
data. This type of classifi er has an advantage that each graph
(joint) in the image corresponds to the human topology. The
presented approach is the fi rst application for this type of data,
according to the authors’ knowledge.
In this study, each forehand and backhand shot from input
data was divided into the following phases: The preparation
and the point of impact together with the racket swinging.
Additionally, no shot was also taken into consideration.
Due to the fact that the analysed groundstrokes are very fast
and dynamically performed, the strict boundaries of these phases
are hard to indicate. Fuzzifi cation of the input data was applied
in order to reduce this disadvantage. This approach improved
the accuracy with respect to the non-fuzzy input. The obtained
accuracy with fuzzifi cation was achieved at the level of 86.9%
— 93.82%, while without fuzzy inputs at 74.22% – 81.95%.