## 5 Conclusions  
This study mainly analyzed the problems of insufficient human-computer interaction in upper limb exoskeleton rehabilitation training and proposed a rehabilitation training control method based on sEMG active motion intention recognition. A joint angle prediction model based on LSSVM was established to obtain the patient's motion intention. In addition, in view of the complex calculations of the nonlinear system dynamics model of the strongly coupled upper limb exoskeleton and the disturbance of environmental uncertainty, an adaptive sliding mode passive control and sliding mode variable impedance active control algorithm based on RBF network approximation were proposed. The experimental test results showed that the passive control method reduced the torque chattering problem in the process of trajectory tracking and reduced the trajectory error. This method could effectively approximate the nonlinear system dynamics model of the exoskeleton manipulator and reduce the calculations in the system. The complexity improved the computing efficiency and the robustness of the control system, sped up the dynamic response time of the system, and improved the strong coupling of the system. The active control method integrated the patient's movement intention and reduced the human-computer interaction force in the training process, which realized the flexible movement of the upper limb rehabilitation exoskeleton. Aiming at the perception decision making and control of the upper limb exoskeleton manipulator under rehabilitation training, this study completed the overall design of the upper limb exoskeleton rehabilitation training system and the research on the control method of active and passive compliance rehabilitation training, which provides a new method for the control research of rehabilitation robots.  
Although the feasibility and stability of the proposed method were clarified, there were still some limitations to our work that we need to state. First, this research aimed to assist hemiplegia patients to realize rehabilitation training with upper limb exoskeleton. However, due to the COVID-19 and research conditions, we could not conduct experiments with the participation of real patients to verify the angle prediction accuracy of a diseased upper limb. Second, the trajectory tracking based on an sEMG reconstructed angle scheme is vulnerable to system uncertainties and disturbances, which often leads to performance degradation. In future work, event-triggered sliding mode control (ETSMC) will be applied to our work since ETSMC can reduce the high-frequency chattering using the symbolic function. In this way, resources can be used reasonably and effectively, and the working time of the controller can be extended.  
Figure 11: Human–machine interaction torque.  
**Author Contributions:** The contributions of all authors are listed as follows: conceptualization, D.K., W.W. and D.G.; methodology, D.K. and D.G.; software, D.K.; validation, D.K. and D.G.; formal analysis, D.K.; investigation, D.G.; resources, W.W. and Y.S.; data curation, D.K.; writing--original draft preparation, D.K.; writing--review and editing, D.K., W.W., and Y.S.; supervision, Y.S.; project administration, W.W.; funding acquisition, W.W. and Y.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Natural Science Foundation of Shaanxi Province under grant 2020 JM-131 and grant 2020 KW-058, in part by the Guangdong Basic and Applied Basic Research Foundation under grant 2019A1515111176, and in part by the Guangdong Science and Technology Innovation Strategy Special Foundation under grant 2019B009004007.  
Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.