## 1 Introduction
Although this model performs well in processing local adjacent information, it is generally less effective at capturing global dependencies. On the other hand, [PERSON] et al. utilized 3D convolutions (3D-CNNs) [5] to extract spatiotemporal features from video segments and recognize and classify surgical gestures at the segment level. While 3D convolutions effectively extract spatiotemporal features, they lack the ability to model long-term dependencies in sequential data. [PERSON] et al. [13] proposed a symmetric dilated convolution method combined with an attention mechanism, achieving significant success in robotic gesture segmentation. However, this method's input and output are both complete surgical video information, limiting its application in online gesture recognition to retrospective detection post-surgery. To overcome these limitations, [PERSON] et al. [14] introduced the C3 Dtrans network, which combines 3D convolution with a Transformer for online surgical gesture recognition. They employed the I3D model architecture as a feature extractor and used the Transformer architecture to model sequences of extracted deep features. However, due to the large number of parameters, this framework cannot be trained end-to-end. Considering the current state of research, this paper proposes a method for recognizing open surgery gestures based on a combination of R3D [15] and multi-head attention mechanisms [16], named R3D-MHA. We use an unpre-trained R3D model to extract fine-grained surgical gesture features and combine it with a multi-head attention mechanism to capture the complex dependencies between long sequences, addressing the limitations of previous methods in capturing long-distance dependencies. Our method not only focuses on fine-grained operations but also captures long-distance dependencies. It also has some online recognition capabilities and allows for end-to-end training of the model. Due to the lack of datasets for surgical gesture recognition research in the field of open surgery, we constructed a database simulating open surgery and named it AEDCSSAD. It includesthree open surgical procedures: bowel anastomosis, hepatic rupture repair and closure of the abdomen surgeries. Currently, the research team has completed the definition and annotation of surgical gestures for the closure of the abdomen simulation surgery, which is a pioneering achievement. Aiming at the problem of surgical gesture recognition in open surgery, we used the R3D-MHA model to conduct studies on both offline and online recognition tasks. The offline recognition task aims to classify manually extracted fine-grained gesture segments, while the online recognition task processes the input video stream to recognize surgical gestures in real time for each frame using a sliding window approach. Finally, we validated our method on the public dataset JIGSAWS to further test its ability to recognize robotic gestures. In summary, our contributions are as follows:  
* We constructed a simulated open surgery dataset and, with the assistance of professional surgeons, defined a surgical gesture vocabulary suitable for suture-based open surgeries, completing all annotations for the dataset. * We proposed an open surgery gesture recognition method combining R3D and multi-head attention mechanisms. The applicability of this method for recognizing open surgery gestures was validated through offline and online recognition tasks. * In addition to evaluating and analyzing the method on the open surgery dataset, we also tested the proposed model on the public JIGSAWS dataset for online recognition tasks and compared it with other online recognition models used for robotic gestures. This experiment demonstrated the potential application of our model in the field of surgical gesture recognition.