# Feature Fusion Graph Consecutive-Attention Network for Skeleton-Based Tennis Action Recognition
## 8 Conclusions and Future Works  
Although the proposed model achieved state-of-the-art performance on three publicly available motion-capture datasets, several factors may currently limit its robustness. 1. Sensitivity to missing or occluded data All graph-based skeleton models implicitly assume complete and correctly connected joint graphs. Frames in which markers are either lost or merged in the 2-D projection (overlapping nodal lines) degrade the quality of the adjacency matrix and cannot be recovered solely by the attention mechanism. In such cases, the model inherits the errors of the upstream pose-estimation pipeline, rather than compensating for them. Figure 11: Grad-CAMs for volley forehand stroke. Left column shows ST-GCN, right shows FFGCAN. Figure 10: Grad-CAMs for volley backhand stroke. Left column shows ST-GCN, right shows FFGCAN. 2. Dataset-specific topology inconsistencies The FFGCAN uses an additional graph node representing the racket (available in 3 DTennisDS), whereas THETIS and Tennis-Mocap lack this information. Although the fusion strategy learns channel-specific topologies, cross-dataset transfer still requires retraining or domain adaptation, because the underlying joint sets differ. 3. Limited kinematic diversity The training corpora mainly include amateur and sub-elite players performing a short list of strokes under controlled conditions. Movement variations unseen in the training process may therefore lie outside the training distribution and elicit unpredictable model behaviors. 4. Computational constraints on edge hardware Despite the relatively small trainable features (1.33 M parameters, 2.7 GFLOPs per \(200\times 17\) sequence), real-time deployment on low-power devices still demands further optimization (pruning, quantization, or knowledge-distillation). The latency measurements presented in Section 6 correspond to a high-end RTX 4090 GPU and do not directly translate to mobile or embedded platforms. 5. Explaining decisions beyond coarse Grad-CAM maps Current interpretability relies on sequence-level Grad-CAM heatmaps, which highlight joint clusters but offer limited temporal resolution. Single-frame visualizations may appear misaligned with the heatmap because the latter integrates evidence across the entire stroke. Finer-grained temporal roll-out or attention-flow analyses may reveal how the network reasons over consecutive motion phases. In conclusion, the proposed FFGCAN model showed significant promise for tennis action recognition, outperforming existing models. Future work should focus on enhancing data diversity, computational efficiency, and interpretability, to fully unlock its potential in real-world applications. Despite demonstrating promising results, this study acknowledges several inherent limitations. The datasets used, THETIS, Tennis-Mocap, and 3 DTennisDS, have very limited variability in the player skill levels, conditions of the environment, and scenarios of the game. Such restricted diversity might limit the generalization capability of the model regarding tennis scenarios at large. In addition, the simplification procedures used to align the Tennis-Mocap and 3 DTennisDS datasets with the THETIS dataset framework inevitably reduced some detailed features. The absence of analysis related to inference times, model sizes, or computational resource requirements could affect its practical deployment in real-time or resource constrained applications. To address these limitations, future studies could explore enhancing the dataset diversity by including a broader selection of player data, as well as testing the model on other motion capture datasets. Investigating methods to optimize computational efficiency through pruning, quantization, or knowledge distillation could also substantially enhance real-world applicability. Furthermore, enhancing interpretability beyond Grad-CAM by employing attention visualization methods would deepen our understanding of the underlying decision mechanisms.