The reason for this phenomenon is that deep learning is a supervised learning method based on big data, which relies heavily on a large number of high-quality labeled data. When the data is insufficient, problems such as overfitting occur in the model, thus reducing the recognition rate. Large sample sizes from a limited number of PD patients are associated with high clinical risks, uncontrollable repeatability, and high costs. These reasons make it difficult for many deep-learning models to be applied to PD research. In addition, some gait disorders such as panic gait, short gait, and frozen gait were contingencies in PD patients, which resulted in a small number of negative samples in the body-sensing data. Considering all kinds of factors, the identification of severity level by machine-learning methods is mainly based on small sample datasets, so few-shot learning with a lower sample-data requirement becomes the key to solve the problem. In addition to few-shot learning, the undersampling and oversampling methods are the common means of balancing a dataset. For a PD disease dataset with too few samples, oversampling is used to expand the dataset, and noise data can be added to enhance the robustness of classifiers. When the model cannot effectively extract features from the existing data, the data can first be processed to find the most important parameters that dominate the differences among classes, or deeper feature analysis of the data can be carried out to make the classification and recognition effect more obvious. [PERSON] et al. [24] studied the dominant factors of stability during treadmill walking, using the random forest (RF) method [25] to measure the importance of the variables, while relatively insignificant variables were removed, which made the analysis more effective. [PERSON] et al. [22] extracted only step time, stance time swing and footstrike profile from the GRF data to analyze and identify the diseases. In addition, in the study of [PERSON] et al. [26], the GRF data were reconstructed in a phase space, after mapping the data to the high-dimensional phase space for topological motion analysis, to study the gait fluctuation, and the extracted topological features were applied. The random forest (RF) method has shown a good performance in permutation-variable importance (PVI) and is widely used [27]. Therefore, RF ranking of the importance of gait characteristics can be used to obtain the main factors influencing the different severity levels in PD patients. The walking process has strong nonlinear characteristics and can be regarded as a nonlinear dynamic system. Extracting deeper features of gait can enhance the differentiation of samples, which is beneficial to the classification of machine learning. The common method is to use topological data analysis (TDA) to obtain the topological imprint for further feature extraction [20; 26]. In the classification of machine learning, traditional classifiers are commonly designed based on balanced datasets, and the losses of classifiers are biased toward the majority of classes [28]. Therefore, the imbalance of sample data may cause the insensitivity of the learning model to a minority of classes. However, in the study of abnormal gait patterns, usually only a small number of samples are available. In the method of balancing samples, the sampling method is often used to balance data, including oversampling [29], undersampling [30], and mixed sampling. In machine learning with a small sample size, the method of oversampling is usually adopted to balance the datasets. Among the oversampling methods, the synthetic minority oversampling technique (SMOTE) is considered to be the most effective [31]. The SMOTE method balances the number of minority classes by interpolation between the adjacent minority class samples, which increases the number of minority class samples and improves the classifier performance [32].