# Feature Fusion Graph Consecutive-Attention Network for Skeleton-Based Tennis Action Recognition
## 6 Experiments and Results  
In order to verify the potential of the created model, a series of experiments were performed applying the ST-GCN classifier in comparison with the FFGCAN. The well-known THETIS, Tennis-Mocap, and 3 DTennisDS datasets were compared in order to verify how various types of data acquisition affected the accuracy of the human action recognition. In this study, all ONI files from THETIS, all bvh files from Tennis-Mocap, as well as all c3d data from 3 DTennisDS consisting forehand, backhand, volley forehand, and volley backhand were taken into consideration. The experiment was as follows:  
1. Create predefined classes. 2. Create simplified models of all motion capture data, divided into four strokes (corresponding to the classes). 3. Create training, validation, and test sets, containing 70%, 15%, and 15% of all data, respectively. 4. Train the network based on the training and validation sets. 5. Test the network--five trials. 6. Create a confusion matrix and compute selected measures (17)-(20) [24]. \[Acc=\frac{Number\ of\ correct\ classifications}{Total\ number\ of\ classifications} \tag{17}\]  
\[Prec=\frac{TP}{TP+FP} \tag{18}\]\[Rec=\frac{TP}{TP+FN} \tag{19}\]  
\[F1=\frac{2*Prec*Rec}{Prec+Rec} \tag{20}\]  
where \(TP\) refers to the true positive fraction, \(FP\) refers to the false positive fraction, and \(FN\) refers to the false negative fraction. To assess the robustness and generalization capability of the proposed model, five independent experiments were conducted. Each involved a random and player-independent division of the dataset into training, validation, and test subsets. Following each run, the performance metrics were recorded. The standard deviation was computed as the square root of the variance, reflecting the statistical dispersion of individual results around the mean value. This procedure quantifies the variability in model performance attributable to different random splits of the data. No uncertainty matrix based on initial or boundary conditions was employed. The evaluation of uncertainty was restricted to a statistical analysis of repeated independent experiments. Thus, the reported standard deviations characterize the consistency of the model's performance across various random samplings, rather than the uncertainty within a single experimental condition. The obtained results are gathered in Tables 2-9. Moreover, confusion matrices are depicted in Figure 6, with the course of the learning process in Figure 7.  
The proposed model was developed based on PyTorch 2.2. The training and testing were conducted on a work station with AMD Ryzen Threadripper PRO 7965 WX, as well as a single NVIDIA RTX 4090 GPU. The momentum was SGD, the Adam optimizer was set to 0.001, \(\beta 1=0.9\), \(\epsilon=10^{-8}\). The epochs were set to 60, and model warm up was the first 5 epochs. An early stop procedure was used after 5 epochs without any improvement. The learning rate was 0.1, and at epoch 40 the learning rate decayed by a factor of 0.1. The batch size was 64 data points. Figure 6: Confusion matrices for typical ST-GCN (**upper row**) and FFGCAN models (**lower row**). The first column shows the THETIS dataset, the second Tennis-Mocap, and the third 3 DTennisDS. Darker background color indicates higher value of correct classifications. The darker the color, the better the results.