## 1 Introduction  
With the expansion of the application range of robots, it is increasingly common for humans and robots to collaborate closely in the same space, especially in the fields of industrial assembly and medical robots [1]. In order to improve the working efficiency and safety of the human-robot coupling system, the study of physical human-robot interaction (pHRI) has been widely studied by scholars [2]. The improvement of pHRI performance can be achieved from two aspects: structure design and controller design methods of structure design, such as artificial muscles and soft robots, have been studied extensively. For example, [PERSON] et al. [3] proposed a handshaking robotic arm based on a variable visco-elastic joint consisting of artificial muscles and magnetorheological brakes, enabling it to acquire properties similar to human arm joints. [PERSON] et al. [4] developed a seven-DOF bio-inspired manipulator that combines the elements of rigid and soft robots, thus showing excellent characteristics in compliance and accuracy while maintaining a low weight. This paper focuses on improving the pHRI performance from the perspective of controller design, especially the integration of human arm impedance into variable admittance control. The impedance characteristics of the arm are generally incorporated into the controller design of the pHRI by means of parameter estimation, indirect measurements and artificial intelligence and adaptive control. Scholars have done a lot of work in modeling and indirect measurement of arm impedance characteristics. As early as 2002, [PERSON] et al. [5] modeled the human arm as a spring-damper system, after collecting the interactive force and position data through human-robot cooperation experiments, he obtained the impedance characteristics of the human arm by means of a parametric model, which was used to control a robot to simulate human motion and thus achieve human-robot cooperation. [PERSON] et al. [6] estimated the stiffness of the human arm based on experiment data including the interaction force and position of the robot end effector in low-speed range, and adjusted the viscosity coefficient of the impedance controller in real time to be proportional to the estimated stiffness of the operator's arm. The drawback of this method is that the proportion of the environment force relative to the operating force must not be too large, otherwise it affects the accuracy of estimated human stiffness. [PERSON] et al. [7; 8; 9] carried out a series of works on human-robot interaction in robot-assisted welding. In [7], he distinguished skilled workers and unskilled workers by analyzing the pose information of welding gun. In [8], he investigated the optimal impedance parameters of robot to reduce the human effort while suppressing the vibration of welding torch. In [9], he applied the measurement of human arm impedance to human-robot interaction control. In order to measure the impedance of the human arm, the robotic arm is set to the admittance control mode, and multiple subjects are used to operate the torch to draw straight lines repeatedly, and the force-position information is collected to measure the impedance of human arm. The results showed that in the direction perpendicular to the drawing line, the experienced operator showed greater impedance. On this basis, a directional damping scheme for robot-assisted welding was developed, which significantly improved the accuracy of the operators. [PERSON] et al. [10] proposed a method for variable admittance control by evaluating human arm stiffness through EMG signal strength, in which the virtual damping is adjusted in real time by measuring the operator's muscle activation state to switch between high and low damping, and the algorithm significantly reduced the operator's operation force and improved the accuracy of the co-movement compared to constant admittance. [PERSON] et al.