## 4 Discussion  
Addressing the current lack of research on open surgery gesture recognition, this study draws on previous surgical gesture recognition research in robotic surgery. We established a closure of the abdomen simulation dataset to study surgical gesture recognition in open surgery. Additionally, this paper proposes a surgical gesture recognition method based on open surgery video data, named R3D-MHA. This method uses a modular structure, combining the R3D network with the multi-head attention mechanism, achieving end-to-end training and inference. Experimental results show that in experiments on the open \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fold** & **SU-001** & **SU-002** & **SU-003** & **SU-004** & **SU-005** & **Average (\%)** \\ \hline
1 & 77.9 & 83.7 & 74.4 & 69.3 & 67.1 & 74.5 \\
2 & 66.6 & 80.6 & 79.5 & 79.9 & 82.9 & 77.9 \\
3 & 67.2 & X & 80.7 & 76.3 & 81.7 & 76.5 \\ All & & & & & 76.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Online recognition results of 5 complete videos on JIGSAWS. Figure 8: Comparison of Best and Worst visualization results on JIGSAWS, Best (**a**), Worst (**b**). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Acc (\%)** & **Trained on Additional Dataset** & **Applicable Online** \\ \hline CRF (dense) [6] & 68.8 & - & \(\surd\) \\ MsM-CRF (STIP) [6] & 66.3 & - & \(\surd\) \\ MsM-CRF (dense-dense) [6] & 71.8 & - & \(\surd\) \\ CNN+LC-SC-CRF [23] & 76.6 & \(\surd\) (Sensor Values) & \(\surd\) \\ ST-GCN [24] & 67.9 & - & \(\surd\) \\ MTL-VF [25] & 82.1 & \(\surd\) (Sports-1M and ImageNet) & \(\surd\) \\
3D-CNN [5] & 84.0 & \(\surd\) (Kinetics) & \(\surd\) \\ C3 Dtrans [14] & 75.8 & - & \(\surd\) \\
**R3D-MHA** & **76.3** & - & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Accuracy comparison for surgical gesture recognition on the SU task of JIGSAWS. The best results and methods by column-wise are shown in bold. surgery dataset, this method performs excellently in both offline and online recognition tasks, achieving a gesture instance level accuracy of 92.3% for offline recognition and a frame-wise accuracy of 73.4% for online recognition. Furthermore, we conducted a more comprehensive evaluation of the R3D-MHA model on the public JIGSAWS dataset, using the Leave-One-User-Out (LOUO) cross-validation method. The results show that without using any pre-trained models or additional data, the recognition accuracy of this method improves compared to other online recognition methods. Of course, this study still has some limitations. Firstly, the improvement in results for the R3D-MHA method in the online recognition task is limited, as we have only conducted preliminary exploration on online detection of open surgery gestures without further optimization of the online recognition algorithm. Secondly, the dataset is based on simulated surgical scenarios, which differ from real surgical environments in certain aspects, such as video noise and blood interference, potentially impacting the model's generalization ability in real-world settings. Additionally, although our dataset includes data from surgeons with three different skill levels, which makes the model have certain generalization ability, it may also limit the exploration of variations in surgical skills among different surgeons. In summary, the contribution of this study lies in the construction of a dataset for open surgery gesture recognition research and the proposal of the R3D-MHA model for both offline and online gesture recognition in open surgery. We have thoroughly validated its performance in recognizing surgical gestures in open surgery. In future research, we plan to further improve the database. On one hand, we will expand it to include more types of open surgeries, such as bowel anastomosis and hepatic rupture repair. On the other hand, we will collect data that more closely resemble real-world environments to enhance the applicability and accuracy of the research.