[PERSON]  
1 Tanjin Key Laboratory for Control Theory & Applications in Complicated Systems, Tianjin University of Technology, Tianjin 300384, China; [EMAIL_ADDRESS] (J.Z. ); [EMAIL_ADDRESS] (E.D.) [PERSON]  
1 Tanjin Key Laboratory for Control Theory & Applications in Complicated Systems, Tianjin University of Technology, Tianjin 300384, China; [EMAIL_ADDRESS] (J.Z. ); [EMAIL_ADDRESS] (E.D.) [PERSON]  
1 Tanjin Key Laboratory for Control Theory & Applications in Complicated Systems, Tianjin University of Technology, Tianjin 300384, China; [EMAIL_ADDRESS] (J.Z. ); [EMAIL_ADDRESS] (E.D.) [PERSON]  
1 Tanjin Key Laboratory for Control Theory & Applications in Complicated Systems, Tianjin University of Technology, Tianjin 300384, China; [EMAIL_ADDRESS] (J.Z. ); [EMAIL_ADDRESS] (E.D. )quantitative measurement method for gait assessment [4; 5]. As an important indicator of joint movement and muscle activity, the GRF during walking can be obtained by wearable inside sensors, which can well reflect the characteristics of an abnormal gait. These sensors have the advantages of small size, low cost, non-invasiveness, a wide range of application scenarios, and low energy consumption. [PERSON] et al. [6] used the GRF to evaluate the impacts on PD from the deep brain stimulation of the subthalamic nucleus (DBS-STU) and drug therapy. [PERSON] et al. [7] studied the freezing of gait in patients with PD prediction and adjustment, in which the GRF was used as the main evaluation index and the auxiliary effect of ankle orthotics was observed. The ankle orthotics embodied in patients after auxiliary GRF captured significantly lower average vibration amplitude, which indicates that the seriousness of the PD patients is closely related to the GRF. In addition, using musculoskeletal modeling driven by depth sensors, [PERSON] et al. [8] compared the GRF among patients and healthy people, and found significant differences in the early peaks of the GRF. A large number of studies have shown that the abnormal gait patterns of PD patients are reflected in the GRF during walking, which can be an important feature of the PD research. The GRF can well reflect the stability of gait, from which we can analyze and grade the severity of PD. Machine learning can effectively solve the problem of medical data analysis, and it has been widely used in related fields. From the perspective of gait data, many related scholars have applied machine-learning methods to conduct classification studies of PD, such as logistic regression, random forest, extreme gradient boosting, radial basis, and neural network [9; 10; 11; 12; 13; 14; 15; 16; 17; 18]. In terms of the PD severity assessment using machine learning, [PERSON] et al. [19] employed the GRF as gait data to identify the seriousness level using the two-channel method of long short-term memory (LSTM) and convolutional neural network (CNN). Similarly, [PERSON] et al. [20] used neural networks and other methods for PD severity classification, in which the phase space reconstruction and empirical mode decomposition methods were used in data preprocessing. [PERSON] et al. [21] used decision tree (DT), support vector machine (SVM), ensemble classifier (EC), and Bayesian classifier (BC) methods to classify the stages of PD, and achieved an effective evaluation of the severity. In a similar way, [PERSON] et al. [22] and [PERSON] et al. [23] achieved effective PD grading by using medium Gaussian SVM and locally weighted random forest methods, respectively, with the shallow-learning method. However, in the above studies, the sample data was small and unbalanced. For instance, in [PERSON] et al. 's work [9], only 10 PD subjects were involved. In addition, compared with other shallow-learning methods, the deep-learning recognition methods based on neural network showed slightly worse results, with respect to time consumption and effectiveness in distinguishing the similar PD severity levels.