###### Abstract  
Early recognition of abnormal gait enables physicians to determine a prompt rehabilitation plan for patients for the most effective treatment and care. The Kinect depth sensor can easily collect skeleton data describing the position of joints in the human body. However, the default human skeleton model of Kinect includes an excessive number of many joints, which limits the accuracy of the gait recognition methods and increases the computational resources required. In this study, we propose an optimized human skeleton model for the Kinect system and streamline the joints using a center-of-mass calculation. We integrate several techniques to propose an end-to-end, spatial-temporal, joint attention graph convolutional network (STJA-GCN) architecture. We conducted experiments with a fivefold cross-validation on two common datasets of information on abnormal gaits to evaluate the performance of the proposed method. The results show that the STJA-GCN achieved 93.17 and 92.08% accuracy on the two datasets, and compared to the original spatial-temporal graph convolutional network (ST-GCN), the recognition accuracy increases by 9.22 and 20.65%, respectively. Overall, the results demonstrate that the STJA-GCN can accurately recognize abnormal gaits and, thus, can support low-cost rehabilitation assessments at community hospitals or in patients' homes. spatial temporal graph convolution; abnormal gait recognition; early multi-branch fusion; attention mechanism +
Footnote †: journal: Medical  
attaching sensors to the body. The accuracy of data captured by the Kinect sensor has been shown in several studies [8; 9; 10]. The Azure Kinect DK is more accurate than the Kinect v2 and Kinect v1 [11]. A software development kit (SDK) is also provided for Kinect, which can be used to obtain a digital skeleton to represent information on human poses. In combination with the rapid development of artificial intelligence algorithms, this SDK provides a robust technical basis with suitable hardware and software for vision-based gait analysis and abnormal gait recognition. The emergence of depth sensors like those used in the Kinect system considerably facilitates the acquisition of 3D skeleton data on the human body and has enabled the development of many gait analysis methods based on digital skeletons. In very early research, Bayesian classifiers [12; 13] and artificial neural network (ANN) models [14] were used to extract skeleton data from Kinect V1 and compute gait features to identify [PERSON]'s disease. Subsequently, [PERSON] et al. [15] compared the accuracy of support-vector machine (SVM) classifiers and a long short-term memory (LSTM) architecture in abnormal gait recognition. They found that gaits can be more accurately classified by directly importing skeleton data into the LSTM. [PERSON] et al. [16] fused manual and depth features extracted by a convolutional neural network (CNN)-LSTM network, and classified gaits using an SVM model. Using an autoencoder based on a recurrent neural network (RNN), [PERSON] et al. [17] extracted features from 3D skeleton data and identified abnormal gaits using a classifier. Because the human skeleton can be modeled as a series of non-Euclidean graphs, these methods cannot effectively learn the underlying spatial relationships between skeleton joints. The advancement of graph convolutional networks (GCNs) allows for a more reasonable interpretation of the natural graph structure of the human skeleton. Consequently, skeleton-based action recognition algorithms have been studied more intensively from the perspective of spatial-temporal graph convolution. As shown in Table 1, the spatial-temporal graph convolutional network (ST-GCN) proposed by [PERSON] et al. [18] provided the earliest feature extraction model for skeleton series in time and space, providing a skeleton-based action recognition model. ST-GCN models have inspired many spatial-temporal graph convolutional networks. [PERSON] et al.