# Feature Fusion Graph Consecutive-Attention Network for Skeleton-Based Tennis Action Recognition
## 7 Discussion  
In this study, a new model, called the Feature Fusion Graph Consecutive-Attention Network, was proposed for tennis movement recognition. The basic strokes were detected, such as forehand, backhand, volley forehand, and volley backhand. The model incorporates seven basic blocks that are combined with two types of module: an Adaptive Consecutive Attention module, and Graph Self-Attention module. They are employed to extract joint information at different scales from motion capture data. The consecutive attention module allows the network to focus on relevant components. This enriches the network's comprehension of tennis motion data representation and allows for a more invested representation. The second module captures and extracts spatial and long-term temporal information from skeletal joints. The temporal convolutional network is applied to identify short-term temporal dependencies. The FFGCAN utilizes a fusion of motion capture data that generates a channel-specific topology map for each output channel, reflecting how the joints are connected when the tennis player is moving. The proposed model was verified using three well-known motion capture datasets containing basic tennis moves. They contain the positioning of the player's silhouette while performing strokes with different representations and with various quantities of three-dimensional data. That is why the Tennis-Mocap and 3 DTennisDS data were simplified to the THETIS model. It should be noted that the 3 DTennisDS dataset contains additional data of the tennis racket, which were also incorporated in this study. The suggested Feature Fusion Graph Consecutive-Attention Network was compared with a Spatial-Temporal Graph Convolutional Network, which is often applied for identifying human actions. This network is very often used to designate motion and appearance patterns, both from video and three-dimensional data. The obtained accuracy results for the ST-GCN proved that this type of model is suitable for accurate tennis movements recognition (Table 2). It is clear that the network performance depended on the type of input data. The lowest accuracy results were obtained for motion capture data registered from the markerless system, from the THETIS dataset. The tennis stroke recognition performance ranged from 73.21% to 75.44%. Tennis stroke recognition based on data from the Tennis-Mocap dataset achieved a slightly higher efficiency, from 76.98% to 78.13%. The highest accuracy results for tennis stroke recognition for the ST-GCN were obtained for data from the 3 DTennisDS dataset, slightly above 80%. The precision results obtained for the ST-GCN proved that this solution is accurate and avoids false positives (Table 3). It also usually correctly identified positive instances from all actual positive samples (Table 4). The predictive skill of the ST-GCN model was high, exceeding 74%, given by the F1-score results (Table 5). The proposed new FFCGAN network was characterized by a very high efficiency in recognizing tennis strokes. The obtained accuracy results exceeded 90% for each analyzed dataset, regardless of the type of input data (Table 6). The proposed classifier recognized tennis strokes proficiently. It should be noted that both the ST-GCN network and the new model had very low standard deviation values, which confirms their stability. The FFCGAN model obtained very high precision results, above 91%, which proves that the model dealt excellently with false positive samples (Table 7). The recall results for the proposed model also reached very high values, exceeding 89.68% (Table 8). This indicates that the network correctly recognized the positive samples. The obaitned harmonic mean values of precision and recall for the FFCGAN were higher than 91%, which shows that the network has excellent predictive skills (Table 9). The confusion matrices obtained for the ST-GCN and the FFCGAN provide a more comprehensive insight into the performance of the compared models.