# Feature Fusion Graph Consecutive-Attention Network for Skeleton-Based Tennis Action Recognition
## 6 Experiments and Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Stroke** & **Mean** & **Max** & **Min** & **\(\pm\)SD** \\ \hline \multirow{4}{*}{THETIS} & Forehand & 73.21\% & 80.00\% & 68.39\% & 3.26\% \\ \cline{2-6}  & Backhand & 73.47\% & 79.37\% & 68.02\% & 3.49\% \\ \cline{2-6}  & Volley forehand & 75.44\% & 79.85\% & 70.14\% & 3.51\% \\ \cline{2-6}  & Volley backhand & 74.08\% & 80.00\% & 68.46\% & 3.57\% \\ \hline \multirow{4}{*}{Tennis-Mocap} & Forehand & 76.98\% & 85.50\% & 73.05\% & 2.47\% \\ \cline{2-6}  & Backhand & 77.10\% & 81.61\% & 73.23\% & 3.01\% \\ \cline{2-6}  & Volley forehand & 78.13\% & 81.98\% & 73.30\% & 3.10\% \\ \cline{2-6}  & Volley backhand & 77.79\% & 81.55\% & 73.11\% & 2.47\% \\ \hline \multirow{4}{*}{3 DTennisDS} & Forehand & 80.91\% & 85.93\% & 76.43\% & 2.78\% \\ \cline{2-6}  & Backhand & 80.39\% & 85.81\% & 77.05\% & 3.20\% \\ \cline{2-6}  & Volley forehand & 81.58\% & 85.94\% & 76.44\% & 2.93\% \\ \cline{2-6}  & Volley backhand & 81.09\% & 85.22\% & 76.17\% & 2.98\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Obtained accuracy results for individual strokes based on a typical spatial-temporal graph convolutional network. Figure 7: Validation accuracy and loss comparison for typical ST-GCN (**upper row**) and FFGCAN models (**lower row**). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Stroke** & **Mean** & **Max** & **Min** & \(\pm\)**SD** \\ \hline \multirow{4}{*}{THETIS} & Forehand & 91.82\% & 94.29\% & 85.44\% & 4.08\% \\ \cline{2-6}  & Backhand & 90.66\% & 96.66\% & 84.19\% & 3.87\% \\ \cline{2-6}  & Volley forehand & 90.04\% & 97.87\% & 88.29\% & 4.04\% \\ \cline{2-6}  & Volley backhand & 96.84\% & 98.52\% & 87.17\% & 4.87\% \\ \hline \multirow{4}{*}{Tennis-Mocap} & Forehand & 92.64\% & 97.63\% & 90.15\% & 2.92\% \\ \cline{2-6}  & Backhand & 93.17\% & 95.39\% & 88.51\% & 3.50\% \\ \cline{2-6}  & Volley forehand & 91.27\% & 93.75\% & 89.46\% & 2.13\% \\ \cline{2-6}  & Volley backhand & 92.22\% & 95.28\% & 85.51\% & 2.10\% \\ \hline \multirow{4}{*}{3 DTennisDS} & Forehand & 95.64\% & 97.31\% & 86.69\% & 3.38\% \\ \cline{2-6}  & Backhand & 95.77\% & 98.14\% & 91.70\% & 3.17\% \\ \cline{2-6}  & Volley forehand & 94.93\% & 96.19\% & 93.50\% & 2.27\% \\ \cline{2-6}  & Volley backhand & 94.26\% & 98.79\% & 87.51\% & 2.94\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Obtained accuracy results for individual strokes based on the proposed model. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Stroke** & **Mean** & **Max** & **Min** & \(\pm\)**SD** \\ \hline \multirow{4}{*}{THETIS} & Forehand & 94.43\% & 96.51\% & 91.93\% & 3.43\% \\ \cline{2-6}  & Backhand & 93.26\% & 97.48\% & 92.35\% & 2.17\% \\ \cline{2-6}  & Volley forehand & 91.37\% & 94.87\% & 87.58\% & 3.93\% \\ \cline{2-6}  & Volley backhand & 94.27\% & 97.45\% & 85.25\% & 4.03\% \\ \hline \multirow{4}{*}{Tennis-Mocap} & Forehand & 92.40\% & 96.30\% & 90.43\% & 3.38\% \\ \cline{2-6}  & Backhand & 91.92\% & 97.93\% & 89.40\% & 3.77\% \\ \cline{2-6}  & Volley forehand & 93.46\% & 96.97\% & 86.31\% & 3.94\% \\ \cline{2-6}  & Volley backhand & 92.08\% & 95.91\% & 90.54\% & 3.56\% \\ \hline \multirow{4}{*}{3 DTennisDS} & Forehand & 94.98\% & 98.61\% & 93.09\% & 2.66\% \\ \cline{2-6}  & Backhand & 92.62\% & 94.97\% & 89.41\% & 3.84\% \\ \cline{2-6}  & Volley forehand & 94.39\% & 97.07\% & 90.18\% & 2.98\% \\ \cline{2-6}  & Volley backhand & 92.77\% & 95.07\% & 90.86\% & 4.03\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Obtained precision results for individual strokes based on the proposed model.