## 2 Related Work  
With current technology, it is possible to capture skeleton data in real-time using depth sensors and pose estimation algorithms [12] with less resource and computational demand. For the realization of motion dynamics, skeleton data are the best choice since they are robust to illumination change, complex background, and scene variation. Conventional methods like hand-crafted approaches and deep-learning approaches are common ways to extract skeleton data from images or videos. The hand-crafted approach focus on capturing the dynamic motion of the joints, such as the relative position of the joints [14], the covariance matrix of the joint trajectories [15], and also the design of several view-invariant features. Among these features design, group sparsity-based class-specific dictionary coding [16], rotation and translations of body parts [15; 17], and canonical view of transformed features [18] are the common ones. Other traditional methods [19; 20; 21] combine the information from different modalities, for instance, from depth information and skeleton data to further enhance the performance. However, these methods do not capture the features needed to predict the gait parameters like deep learning methods. Recently, a deep learning approach amassed a lot of success in many fields. The approach preferred to traditional machine learning methods because it outperforms with less complicated models and without requiring extensive feature engineering. Among the deep learning methods, recurrent neural network (RNN)-based methods [13; 22; 23; 24] that are known for the application of temporal dynamic behavior, and CNN-based methods are used in cases of parameter sharing and sparse connectivity to reduce the number of parameters that need to be learned [25]. Additionally, the CNN model mapped anatomical key points to an outcome metric (e.g., cadence) [11]. To improve performance, a two-stream-based model [26] integrates CNN and RNN that operate on RGB images and coordinate vectorsof skeletons ordered in temporal form, respectively. However, both the single network and the combined two-stream model come short in understanding human movement since the spatial dependence between the correlated joints could not be captured by the methods. The main idea of a graph neural network representing the complex relationship and inter-dependencies between the data in non-Euclidean domains that is classified under recurrent graph neural network (RecGNNs) was conceived in [27] and further explained in [28; 29]. These RecGNNs methods use neighbor information iteratively to learn a target node. The training continues until a stable fixed point is attained. In general, the methods focus on learning node representations with recurrent neural architectures. Such methods demand higher computation power. As a consequence RecGNNs methods [30; 31] are proposed to address such problem. Analogous to the convolutional neural network in images and videos, a graph convolutional network is proposed to generalize the operation of convolution from grid data to graph data. Here, in graph convolutional network (ConvGNN) the node representation is obtained by aggregating its neighbor features and its own features. Unlike RecGNNs methods, ConvGNN stack multiple graph convolutional layers and also pooling layers to extract high-level node and graph representation. ConvGNNs fall into two main streams, spectral-based approaches [32; 33; 34; 35] and spatial-based approaches [36; 37; 38]. Although spectral-based approaches focus on removing noises from graph signals, spatial methods define graph convolution by information propagation, an idea inherited from RecGNNs. Recently, graph convolutional neural networks (GCNNs) [34] have been proposed to break the gap between spectral and spatial-based approaches. In addition to the flexibility, and simplicity, spatial-based methods are more efficient for graph-related data. Thus, we chose the approach in this paper. Initially, a graph convolutional neural network is proposed for spatial dependency.