## 2 Literature Review  
### Human Action Recognition  
The main target of human action recognition is to recognize the category of human behavior over a period of time. According to different data sources, this category can be divided into image-based methods and methods based on other sensor data. Most of the studies about human action recognition focus on image sources, which generally use ordinary RGB (red-green-blue) cameras to record videos of human actions. Prior to 2013, the methods [6; 7; 8; 9; 10; 11; 12; 13] of action recognition mainly relied on algorithms to extract specific hand-crafted features in each frame, and then exploited pre-trained classifiers (e.g., support vector machine, SVM) to get the category of the frame, and finally, recognized actions by comparing the changes of actions between adjacent images. The quality of the hand-crafted features greatly affects the performance of this kind of method. Some scholars have tried other modeling methods. [PERSON] and [PERSON] [14] leveraged a hidden Markov model (HMM) method to model temporal and causal relations between the different poses and activities, and used multi-class AdaBoost to classify the category. [PERSON] et al. [15] also built an HMM-based model, in which the number of hidden states was set to the number of action classes. The improved dense trajectories (iDT) used by [7; 8; 13] is the method with the best effect, greatest stability, and highest reliability before deep learning enters the field of action recognition. The basic idea of iDT is to use the optical flow field to obtain some trajectories in video sequences, and extract features, such as the histogram of gray (HOF), histogram of oriented gradient (HOG), motion boundary histograms (MBH), and trajectory features along the trajectory. However, since the cost of calculation of optical flow is particularly high, the analysis efficiency of iDT remains extremely low (0.06 s for a pair of frames), and it would still introduce a bottleneck if done on-the-fly [16]. The latter methods [17; 18; 19; 20; 21] are closely related to the development of deep learning. The deep-learning-based method is far superior to the hand-crafted feature-based method in the recognition accuracy and processing speed of image and video. The mainstream deep-learning methods can be divided into CNN-based, RNN-based, and GCN-based methods according to the category of the model. CNN-based methods have significant advantages in extracting spatial information from videos, and are easier for training, which has led many scholars to conduct research. [PERSON] et al. [22] proposed a human-related multi-stream CNN (HR-MSCNN) architecture, which encodes appearance, action, and the captured tubes of the human-related regions to recognize human action. [PERSON] et al. [23] introduced a CNN-based method combined with an optimized deep autoencoder (DAE) to learn temporal changes of the actions, and classify actions based on SVM. [PERSON] et al. [24] proposed a novel fusion network that combines temporal poses, spatial features, and action feature maps for classification by bridging the gap between the size differences between 3D and 2D CNN feature maps. RNN-based methods have unique advantages at finding the time relationship between different frames in the video, and can find the temporal and spatial connection of human actions. [PERSON] et al. [25] combined a spatio-temporal attention mechanism and semantic graph modeling to propose a novel attention semantic recurrent neural network for understanding human activities and individual behaviors in videos. [PERSON] et al. [26] proposed a hybrid RNN-HMM approach with a hierarchical structure to solve the problem of weakly supervised learning of human actions from ordered action labels by constructing recognition in a rough to fine manner. [PERSON] and [PERSON] [27] proposed an extended version of the Long Short-Term Memory (LSTM) unit, which can perceive motion data as well as spatial features and time dependencies in the LSTM.