# A Parametric Identification Method of Human Gait Differences and its Application in Rehabilitation
## 5 Conclusions  
This article proposed a parametric description method of mathematical models based on the GE-Bezier curve for the angle variations of three lower extremity joints in the sagittal plane, which can help to distinguish normal and abnormal gait, and assess the rehabilitation status and walking postures of patients by shape parameters. The main conclusions can be summarized as follows:  
1. The mathematical models were provided for the gait data of each participant, which not only contain the individual gait personality but also present the general gait regularity. In the models, the gait curve shapes of different participants in the same joint can be controlled by one group of commonality points, and the differences among local details of curves were reflected by different local shape parameters.
2. For the normal ranges of shape parameters summarized from the normal group in this paper, they can be used as references for distinguishing normal and abnormal gait. The gait could be considered normal if shape parameters in the patient group belonged to these ranges, and if they were exceeded, especially for parameters with larger weights, the gait tended to be abnormal.
3. The excess degrees of abnormal parameters can play a certain role in assisting the evaluation of the recovery status of patients. In this context, the degree of abnormal parameters of patients in the second month recovery period significantly exceeded those in the recovery period of six months, indicating that the former has a poor recovery status to a large extent.
4. Shape parameters can help analyze abnormal postures during walking. For the patients with an ankle fracture in this paper, it could be estimated that the flexion movements of three lower joints were limited to various degrees corresponding to the abnormal parameters, and these abnormal gait characteristics can in return provide references for rehabilitation guidance.  
In future works, we aim to continue to expand the sample size of the same population and calculate the shape parameters to obtain sufficient data to further support the results of the study. Subjects in other ages and genders will also be recruited and measured to acquire the adjustment ranges of shape parameters and to make the method more adaptable in clinical practice. In addition, for the process of judging whether the gait is normal by shape parameters, the possibility of machine learning can be explored. With the support of sufficient data, more accurate weighted parameters can be calculated through a machine learning algorithm, so that the judgment has more experimental evidences  
Conceptualization, [PERSON], Y.C. and X.J.; methodology, J.G., G.H. and F.L.; software, J.G. and F.L.; validation, X.J. and X.W.; formal Analysis, F.L.; Investigation, J.G. and Y.C.; Resources, X.J.; Data Curation, J.G.; Writing--Original Draft Preparation, J.G. and Y.C.; Writing--Review and Editing, J.G., X.W., and F.L.; Visualization, J.G., and X.J.; Supervision, [PERSON] and X.J.; Funding Acquisition, [PERSON] and J.G.  
This research was funded by the National Natural Science Foundation of China Grant No.51875454 and the Education Foundation of Xi'an University of Technology Grant No. 106-256091705.  
The authors are very grateful to other members of staff for their suggestions which have improved the paper.  
The authors declare no conflict of interest associated with this study.