## 4 Discussion  
In this study, we propose a novel method for abnormal gait recognition based on graph neural networks. After extracting the skeleton data of walking humans with depth  
Figure 6: Comparison of confusion matrices. (**a1,a2**) are the training models with maximum and minimum accuracies on Kinect25, respectively, (**b1,b2**) are the training models with maximum and minimum accuracies on Azure32. The gait labels: 1-normal gait; 2-antalgic gait; 3-lurching gait; 4-steppage gait; 5-stiff-legged gait; 6-Trendelenburg gait.  
sensors, the body joints are streamlined by replacing unimportant joints in hands and head with fusion averages. This streamlining reduces the redundant joints and enhances the extraction of key walking features while reducing the computational load and training speed of the model. Then, a multi-branch model structure is constructed for feature fusion in the early stage. The original joint connections are divided into three feature branches, including those for joint positions, motion velocities, and skeleton features, to enhance the extraction of information between joints during walking. Finally, we also introduced the ST-JointAtt module, which is combined with spatial-temporal graph convolution to form a spatial-temporal joint attention graph convolution module. The ST-JointAtt can jointly process temporal attention and spatial attention. Thus, the most important joints can be identified in the whole skeleton series to focus more attention on the main features of abnormal gaits. In addition, the results of a numerical evaluation show that the proposed spatial-temporal joint attention graph convolution method, which fuses multi-branch features early, performed accurately on two public Kinect gait classification datasets. The experimental results show that the proposed method achieved improved efficiency and accuracy on an abnormal gait recognition task compared to existing methods.  
The effectiveness of the proposed method was thoroughly verified through comparative experiments. The results show that joint streamlining does not affect the accuracy of the model for gait recognition, but does reduce the number of parameters required and the computational load. Compared with other attention mechanisms, the ST-jointAtt can significantly improve the accuracy of the model, indicating that it can help to discriminate abnormal gaits in both time and space.  
It may also be observed from the confusion matrices that the STJA-GCN model is not sufficiently accurate to classify some gaits on the two datasets, but the model is generally able to distinguish abnormal gaits well on both datasets. Furthermore, the Trendelenburg gait was sometimes incorrectly classified as the steppage gate owing to the clinical similarity between the two gaits. The Trendelenburg gait is caused by an abnormal hip abduction: during walking, the weak glutael muscle causes the pelvis to sag to the opposite side, shifting the center of gravity to the supporting leg. The same is true for the steppage gait: due to the paralysis of the calf extensor group, the patient needs to raise their leg while walking to keep their center of gravity balanced. Therefore, the center of gravity shifts to one side during the walking cycle in both gait patterns, leading to classification errors. Future research may consider capturing walking gait features from other angles or exploring more suitable attention modules for abnormal gaits. Due to data limitations, in this study, we only identified a few classes of abnormal gaits. In the future, we plan to consider further collaboration with clinical hospitals and rehabilitation centers to collect gait data from real patients and train a model to identify more classes of abnormal gaits.