## 4 Experiments
### Datasets and Metrics
#### 4.3.3 Ablation Studies  
In this subsection, we will first perform an ablation study on the FPHA and H2O datasets to verify the effect of image features on action recognition accuracy. Table 4 reports the results of decimation experiments on these two datasets. Subsequently, the impact of different loss weights for parameters \(\alpha_{1}\), \(\alpha_{2}\), \(\alpha_{3}\) is explored on the FPHA dataset, as shown in Table 5.  
We investigated the effect of the Spatially Detailed Feature Extraction Part on the accuracy of action recognition. Specifically, if only the global context features \(f_{glo}(t)\) obtained from the transformer Encoder Block processing are used for action recognition, the model achieves 94.43% and 87.04% accuracy on the FPHA and H2O datasets, respectively, and the accuracy of pose estimation for both hands on the H2O dataset is 35.26 mm and 38.87 mm, respectively. These results show that modelling based on global contextual features \(f_{glo}(t)\) is able to achieve relatively excellent action recognition performance, but there is still room for further optimization. To further enhance the model's ability to capture detailed spatial information, we added the Spatially Detailed Feature Extraction Part in the pose estimator for enhancing the perception of the local structure and spatial distribution of the hand. The Spatially Detailed Features \(f_{det}(t)\) are concatenated and fused with the global contextual features \(f_{glo}(t)\) extracted by the transformer Encoder Block through a dual residual network structure, resulting in a more expressive joint feature representation. The experimental results show that the inclusion of Spatially Detailed Features improves the model's action recognition accuracy by 0.39% and 0.88% on the two datasets and the left- and right-hand pose estimation accuracy by 4.1 mm and 3.66 mm on the H2O dataset, both of which achieve the best performance. This enhancement am  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **w/o\(f_{det}\)(t)** & **w/\(f_{det}\)(t)** \\ \hline FPHA & Acc. & 94.43 & **94.82** \\ \hline \multirow{3}{*}{H20} & Acc. & 87.04 & **87.92** \\  & hand.Left & 35.26 & **31.16** \\ \cline{1-1}  & hand.Right & 38.87 & **35.21** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study of hand estimator in HPEM module with respect to Spatially Detailed Feature Extraction Part.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Modality** & **Acc.** \\ \hline H2O w/ST-GCN [30] & Skeleton & 73.86 \\ H2O w/TA-GCN [2] & RGB + Depth & 79.25 \\ PoseConv3D [53] & Skeleton & 83.47 \\ H+O [3] & RGB & 68.88 \\ SlowFast [25] & RGB & 77.69 \\ C2D [52] & RGB & 70.66 \\ I3D [24] & RGB & 75.21 \\ HTT [4] & RGB & 86.36 \\
**Ours** & RGB & **87.92** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of action recognition accuracy on H2O set.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**\(\alpha_{3}\)** & **Acc.** \\ \hline
0.5 & 92.47 \\
0.75 & 93.88 \\
**1.0** & **94.82** \\
1.25 & 94.09 \\ \hline \hline \end{tabular}
\end{table}
Table 5: A study on parameters \(\alpha_{3}\) on the FPHA dataset.  
our proposed design, i.e., Spatially Detailed Feature Extraction Part incorporating a dual residual network structure, is significantly effective in improving model performance. In addition, when the Spatially Detailed Feature Extraction Part is introduced, our frame parameter size is 113.8 MB, the inference time is 0.178 s, and we are able to achieve a frame rate of 5.65 FPS. This shows that our model performs better in balancing computational complexity as well as recognition accuracy.  
We investigated the effect of different loss weights for the parameter \(\alpha_{3}\) on the recognition performance. The results in Table 5 show that our model reaches the highest accuracy when \(\alpha_{3}\) is set to 1.0, indicating that the current setting is the optimal configuration.