## 4 Discussion and Conclusions  
There is always a problem of insufficient samples in the recognition of PD. Similar to other studies of abnormal gait, the number of subjects with different disease grades of PD is usually very limited, and the samples are commonly unbalanced. These factors suggest that the grade recognition of PD is a few-shot learning problem. In this paper, the common GRF dataset was used, which can show the walking pattern of PD patients well. However, from the point of view of the learning effect, the training accuracy curve of the model trained by GRF data showed a U-shape with an increase of the number of samples. The reason for this phenomenon is that the feature discrimination of untreated GRF data was not significant, and contained many irrelevant features/interferences. When the number of samples increased, the learner could not fit the new irrelevant information well, which led to the reduction of training accuracy. This indicates that the GRF data contained too much disturbing information. In another way, some characteristics did not change much among classes. To solve this problem, we processed the original GRF sample data. The GRF sample data was first divided according to the gait cycle. Considering the existence of minority classes (such as the abnormal gait class), if the time span of GRF data used to calculate gait characteristics was too long, it could cause the loss of key information, and it would not be able to clearly characterize the abnormal gait problem. After the data partition, GRF was used to calculate potential gait features that may affect the classification of the disease grade. For the selection of gait features, we referred to the relevant research and the previous research [22; 41], and selected a series of gait features that could be calculated from GRF data for further analysis. After the potential gait features were obtained, we measured the importance of gait features. From the experiment results, we observed that the aggravation of PD was directly reflected in walking speed. When the severity of the disease worsened, serious gait disorders hindered the patient from normal speed walking, resulting in a slow movement. This conclusion was strongly supported by the experiments. In addition, walking speed was observed to also affect the stride time of patients, which is also reflected in the results. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **[PERSON]** & **[PERSON]** & [PERSON][PERSON]** & **[PERSON]** & **[PERSON]** & **Present Method** \\  & **et al. [26]** & **et al. [22]** & **et al. [19]** & **et al. [20]** & **et al. [23]** & \\ \hline
**Accuracy** & 87.10\% & 94.14\% & 98.70\% & 98.80\% & 99.00\% & **99.23\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison with other methods. In other gait features with significant influence, we found that the proportion of gait phase in the classification of disease grade had a high degree of differentiation. There were great differences in the proportion of support-phase and swing-phase time in patients with different disease grades, which also reflects the walking mode of patients with different severity levels. When abnormal gaits such as freezing gait and panic gait occurred, the proportion of gait phase changed significantly. In frozen gait, the proportion of support phase increased significantly. The frequency of the abnormal gait increased significantly when the disease grade was aggravated, and the change of the proportion of gait phase was more clear. For instance, the left-foot gait-phase ratio of PD patients to control subjects is shown in Figure 9.  
At the same time, we demonstrated that the RMS of coordinates CoP velocity, CoP efficiency, and sample entropy have significant discrimination in the \(Y\)-axis direction. This result indicated that the component of the gait characteristic in the walking direction could significantly influence the classification of disease grade of PD.