## 1 Introduction
The hip and ankle angle joints could move at any angle of freedom of the wearer, but the knee angle of the wearer was estimated by the proposed algorithm in the active joint. The proposed algorithm was used as a position input to control the exoskeleton robot's knee. In other words, the knee angle of the wearer was estimated using the hip joint and the foot pressure sensors. Since the sagittal plane is mainly used for walking, it is possible to sufficiently grasp the intention of the wearer by using the hip joint angle. For this, two types of physical human-robot interaction (pHRI) sensors were used: (1) absolute rotary encoders on the hip and knee joint axis of the exoskeleton robot and (2) the plantar pressure sensors on the foot for the gait phase classification. When the pressure sensor measurements were higher than the set threshold, this was defined as the stance phase, and fuzzy inference for the stance phase was applied. In contrast, when these measurements were lower than the set threshold, fuzzy inference for the swing phase was applied. The encoder sensors measured the hip joint angle, and they were used as crisp inputs to yield four fuzzy input conditions through the linear transfer functions in the fuzzy set. Four fuzzy inputs were selected though the minimum in AND operation from the hip encoder sensors. After we determined the minimum in AND operation, four fuzzy inputs were used as the input for the fuzzy inference. Four fuzzy output values were implemented using the four inputs and 68 predefined rule bases in the fuzzy inference. The rule bases were divided into swing-phase rule bases and stance-phase rule bases, and they were designed based on the gait analysis of the wearer in locomotion. A crisp output was calculated using the selected four fuzzy output values and weighted values based on the center of area (CoA) method found in the Mamdani method. The criterion of the weighted values was assigned using the angle section to estimate the knee joint angle. The crisp output created an estimated motion profile, which was used as a position input to control the exoskeleton robot's knee. The proposed algorithm is called a high-level system. A high-level system transmits the motion profiles of a robot's knee to the low-level system of the developed electro-hydraulic actuator (EHA) system [9; 10], such that the two systems become combined. The proposed algorithm was verified through experiments, showing that it alleviates the difficulty of mathematically expressingcomplex structures. The algorithm was used to verify the motion profiles in real time to estimate the knee's joint angle in two experiments. The first experiment was performed without the EHA for the exoskeleton knee angle estimation, and in the second experiment, the motion profiles of the knee were estimated in real time, and the knee was controlled using the EHA. Through the experiments, it was shown that the proposed method accurately estimated the knee joint angle of the exoskeleton robot. The remainder of this paper is structured as follows. In Section 2.1, each component of the designed exoskeleton robot is briefly described, and the gait analysis of the exoskeleton robot is explained. Section 2.2 describes the proposed estimation design process for the motion profiles. Section 2.3 explains the method of applying the proposed algorithm to the exoskeleton robot. In Section 3, the experimental configuration and two experimental results are described. The conclusions of the results are presented in the last section.