# Skeleton Motion Recognition Based on Multi-Scale Deep Spatio-Temporal Features
## 1 Introduction  
With the rapid development of deep learning technology and artificial intelligence algorithms, human motion recognition, especially motion recognition based on human bones, has attracted extensive attention from scholars all over the world [1, 2, 3]. Motion recognition plays a very important role in intelligent monitoring systems, human-computer interaction systems, intelligent robots, virtual reality technology, automatic driving, motion correction, and so on. For example, it is involved in analyzing an athletes' sports posture to improve the athlete's sports performance and in automatic driving, by judging human movements and predicting their action intention. Traditional motion recognition methods mainly used the approach of manually designing features to extract information on features, train the classifier, and output the prediction results. This recognition method not only consumes a lot of resources but also can not meet the requirements of speed and accuracy [4, 5]. In recent years, with the development of artificial intelligence technology and computer hardware, action recognition algorithms based on deep learning have been widely used because of their strong adaptability and high recognition efficiency. Action recognition based on deep learning can be divided into two categories. The first is based on the use of traditional CNN, RNN, or LSTM networks, such as two stream [6, 7], C3D [8], and LSTM [9] methods. These use end-to-end learning to effectively reduce the amounts of parameters and greatly improve the recognition accuracy. Inspired by the process of human vision and the understanding of video information, [PERSON] et al. [6] designed a dual-stream network. The two streams extracted time information and spatial information, respectively, and finally fused the two models. [PERSON] et al. [8] found that a 3D convolution network is better than a 2D convolution network in learning spatiotemporal features, and provedthrough experiments that the use of a \(3\times 3\times 3\) convolution kernel for 3D convolution was the best. [PERSON] et al. [9] proposed the long short-term memory (LSTM) network. Later generations used this network with action recognition tasks to learn effective features and model the dynamic process in the time domain to realize end-to-end action recognition and detection. The second category of the action recognition methods is those based on the human skeleton, which show better robustness to illumination changes and environmental changes. In recent years, this area has attracted extensive attention. The method uses a human posture detection algorithm and a high-precision depth camera to obtain the characteristics of a human skeleton, forms a graph through natural human connections, and gives a time series of human joint positions. With the rapid development of graph convolutional neural networks (GCNs), a large number of graph convolution-based network models have been applied to skeleton-based action recognition tasks. [PERSON] et al. [10] first applied a graph convolution network to human skeleton action recognition, establishing the spatial graph of natural human connections, added time edges between corresponding joints in continuous time frames, and proposed a spatiotemporal graph convolution neural network. [PERSON] et al. [11] defined a partition of a skeleton graph. In this partition, spatiotemporal convolution was formalized using a location-based GCN for the task of action recognition; [PERSON] et al. [12] proposed a two-stream adaptive graph convolution model to adaptively learn the graph topology of different GCN layers and skeleton samples, which was better adapted to the recognition task and GCN hierarchy, and further improved the recognition performance. At present, there are still some problems in the action recognition model based on graph convolution neural networks. (1) Different action durations are different and changeable.