## 1 Introduction and Related Works
### Contributions of This Paper  
Currently, human action recognition includes several public datasets [48; 49]. The commonly studied actions are classified into simple types of movements, such as walking, running, jumping, writing, etc., or some regular movements, such as playing ball, swimming, roller skating, etc. However, whether it is the difficulties in feature extraction and recognition classification mentioned above, or the vast scope for improvement in current research progress from our expected advanced motion recognition, advanced action recognition still requires a higher level of computer vision understanding and representation capabilities.  
On the other hand, in some special industrial application scenarios, human action recognition could be difficult because of a lack of training data or low action discrimination. For the actions of operational tasks in industrial operations, there are particularities mainly reflected in the facts that:  
* There could be standard actions for the operations;
* The overall pose may not vary significantly with different operations, but the distinction is reflected in the details of the hands or other parts;
* The amount of data available for training is relatively small and narrow.  
Figure 1 shows several image examples of workers' actions while installing a solar panel, wherein sub-figures (a) and (b) come from the same type of action: 'tie', and (c) and (d) come from the same type of action: 'pendulum'. We can see that the difference between the two types of actions lies only in the small differences in hand movements, regardless of whether the person is standing or not, while on the other hand, from the perspective of the overall movement, whether a person stands or not is very obvious.  
This can lead to a problem: when the number of training samples is small, if we simply and directly use the full body motion to train or calculate the action recognition framework, this will be difficult due to the large intra-class distance and the small inter-class distance, resulting in eventual training failures or recognition errors.  
In this paper, aiming at the application requirements of action recognition for workers installing solar panels, a graph convolution neural network model combining prior knowledge supervision and attention mechanisms is proposed, which extracts prior knowledge from training data and is used to improve the training effect of action recognition models and improve the recognition reliability of special actions. The main contributions of this article are as follows:  
Figure 1: Action image frames of workers installing solar panels. (**a,b**) From the same type of action: ‘tie’, (**c,d**) from the same type of action: ‘pendulum’.  
* A method based on training set statistics is proposed to achieve the correlation representation of different human joint points for a specific action, which can be used as a priori knowledge of the action;
* A graph convolution neural network model using the aforementioned prior knowledge to train attention mechanisms is proposed, which improves the training and recognition effect of the model for specific actions.  
The organization of the rest of this paper includes the following: Section 2 is an introduction to the method, Section 3 is an experimental section, and Section 4 is a summary.