## 4 Conclusions  
In this study, we proposed a weighted fuzzy logic algorithm using hip joint angles and plantar pressure sensors to control the knee joint angle of the EHA-driven exoskeleton robot. We conducted two experiments using an exoskeleton robot--namely, with and without an actuator in the exoskeleton robot. The first experiment was conducted without an actuator for muscle strength, where only the exoskeleton robot was worn, and walking was performed. The predefined rule bases of the proposed system were fixed, but the knee joint angle was adjusted using the weight adjustment criteria according to the hip joint angle. Through this experiment, we verified that the motion profiles estimated the robot's joint angle well in real time using the hip joint angles and the pressure sensors. Although the proposed algorithm was designed with 68 predefined rule bases in the fuzzy inference, which did not exactly estimate the knee angle in real time, the motion profiles created from the fuzzy inference estimated the robot's knee joint angle close to the desired joint angle. In the second experiment, we evaluated whether the proposed algorithm estimated the wearer's intention well when combined with the EHA system. In the gait experiment, an error temporarily occurred in the swing section,  
Figure 12: The sit-to-stand movement experiment: (**a**) the change in the hip joint angles, (**b**) the motor speed, (**c**) tracking error, and (**d**) a comparison of the actual and the estimated knee joint angles of the exoskeleton robot.  
but it was confirmed that the error was reduced. In the sit-to-stance movement experiment, it was confirmed that the knee angle was more stable than in the gait experiment. Due to the misalignment between the exoskeleton and the wearer, the complexity, and the uncertainties, it is difficult to identity a wearer's intention. Despite these difficulties, through these experiments, the novel method estimated the wearer's intention in real time and successfully produced a motion profile for the exoskeleton robot knee control. As a result, when workers need to repeatedly transport heavy objects on industrial and construction sites, the exoskeleton robot applying the proposed algorithm can help to increase the worker productivity and to reduce fatigue and the risk of injury.  
The proposed method can be programmed for various situations using the fuzzy inference. Although the weights of the proposed system are fixed, the system can be further improved by using criteria that adjust the weights according to the system's performance. If an improved system is to be used, it can be applied not only to industrial robot requiring power and mobility but, also, to rehabilitation robots requiring precision and stability.  
Conceptualization, formal analysis, methodology, software, validation, and writing--original draft preparation, B.S.; methodology, validation, and writing--review and editing, D.L.; methodology, formal analysis, and writing--review and editing, S.Y.P.; and supervision, project administration, and funding acquisition, Y.S.B. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program grant funded by the National Research Foundation of Korea (NRF) (No. 2017R1A2B4009265).  
The authors declare no conflict of interest.