## 3 Experiment Results
### Experiment Setup  
The hardware device for conducting the experiment in this article is an x86 architecture computer, with a CPU of CoRE(tm)I7-12700 processor produced by Intel, RAM memory of 32 GB, and a graphic computing card of GeForce RTX 3090 produced by Nvidia. The software system is Linux Ubuntu 18.04, equipped with the Python deep learning framework and CUDA 10.0. In the implementation process, firstly, for videos containing humans, a human skeleton recognition algorithm is used to extract the human skeleton. Subsequently, the skeleton data of all actions will be extracted according to the method in this article, including prior knowledge, features, and node regions labeled with significant actions; then, skeleton data are input into a deep neural network for training. The deep neural network used in this article is similar to ST-GCN [43], which consists of nine layers of spatio-temporal graph convolution operators. The first three layers have 64 channels for output. The three layers below have 128 channels for output. The last three layers have 256 output channels. The applied structure of the graph convolution layers is shown in Figure 8. The model adopts a batch size of 64, random gradient descent as the optimization strategy, and cross entropy as the loss function of gradient back-propagation, and the initial value of weight attenuation is 0.0005. The algorithms compared in this article include the original ST-GCN [43] and slowfast [41], both of which use default parameters. Figure 7: Example of ‘tie’ step. **Detected significant joints for each action**: For the four actions involved in the datasets collected in this paper, the significant joints identified for each action are shown in Table 2. The second column shows significant joints (body parts) obtained through the prior detection and statistics method in Section 2. The third column shows manually designed significant joints of the specific action. It can be seen that all movements are closely related to the hand joints, which is also closely related to the standard operation design of each process, because in terms of process production, the effective operation of humans is mainly with the hands, while the extension or bending of legs is mainly used to move the position of human operations. Meanwhile, the prior detection and statistics method relies on diverse datasets, and it can also discover hidden relationships with other joints that have not been found manually. In order to better compare the effects based on statistics with those specified manually, we also tested the recognition results using our method in the case of manually specifying significant joints. **Pre-detection of human skeleton and body area**: Figure 9 shows some intermediate results of human skeleton detection and human body area detection using methods provided by Detectron2 [30] from raw image sequences, called the pre-detection step. Then, the human skeleton data are used as inputs for the GCN in this paper and in [36]. The human body area data are used as the inputs of the method in [34]. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Actions**} & **Joints Marked as \(\boldsymbol{1}^{\prime}\)** & **Joints Marked as \(\boldsymbol{1}^{\prime}\)** \\  & **(Detected Using Our Method)** & **(Human-Designed)** \\ \hline ‘pendulum’ & 2,3,4;5,6,7,0,2,5,8,11; & 2,3,4;5,6,7; \\ \hline ‘stay wire’ & 2,3,4;5,6,7,0,1; & 2,3,4;5,6,7; \\ \hline ‘threading’ & 2,3,4;5,6,7,0,1,0,2,5,8,11 & 2,3,4;5,6,7; \\ \hline ‘tie’ & 2,3,4;5,6,7,0,2,5,8,11 & 2,3,4;5,6,7; \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The significant joints. Figure 8: The applied structure of the graph convolution layers. It should be noted that due to the limitations of perspective, image quality, and the algorithm used for human area detection and skeleton detection, there are certain errors in extracting human skeletons and mask regions from the original video.