# Review of sEMG for Robot Control: Techniques and Applications
## 4 Application of sEMG Interface in Robot Control
### Classification of Robot Control Strategies
#### 4.1.1 Full-Human Continuous Control Strategy (S1)  
Under this control strategy, the movement of the robot is entirely dependent on muscle activation information. The robot begins to move synchronously when the signal collected by the sEMG interface exceeds a given threshold. Typically, a robot's movement mimics human limb movement or collaborates with the subject to accomplish tasks when using this control strategy. A Gaussian regression process-based method was used to control the manipulator in [PERSON]'s study. They decoded biological signals using a Gaussian regression process and mapped an eight-channel sEMG to directed velocity-based 2D or 3D control commands for manipulator control in a microgravity (space) environment [11]. In [24,37], a machine-learning method based on the Gaussian regression process was used to transform the EMG signals of SMA patients into force and velocity control commands for a manipulator. [PERSON] proposed a spherical linear interpolation motion-planning method that allows an sEMG-controlled manipulator to imitate human motion with a short delay [28]. [PERSON] and [PERSON] dimensionally reduced the acquired sEMG signals to two dimensions and mapped them to the four joint angles of the shoulder and elbow joints [86; 88; 82]. By interacting with a 4-DOF manipulator, the hand position and interaction force, which can be used as exoskeleton control, are obtained through the forward kinematics and dynamics of the human body. [PERSON] implemented variable admittance control for a 7-DOF manipulator based on sEMG and compared it with constant admittance control in terms of motion accuracy, execution time, and energy consumption [35]. [PERSON] implemented adaptive impedance control for an upper-limb exoskeleton using sEMG [126]. Fuzzy neural networks were used to model the various factors of the dominant muscles when the upper arm shoulder-elbow joint was in different positions in [PERSON]'s work. The model takes multi-channel sEMG signals as input and maps them to the joint torques on the human upper limb. The resulting model is applied to control exoskeletons [23; 76; 85; 87; 91; 93; 117]. [PERSON] et al. collected sEMG information from agonist and antagonist muscles simultaneously to calculate the exoskeleton's joint torque control and applied LDA classifiers to improve the classification efficiency at each joint [22]. [PERSON] et al. proposed a method to adapt a household rehabilitation bilateral exoskeleton's stiffness based on the user's dynamic motion using sEMG and upper-limb musculoskeletal models [3]. [PERSON] proposed a model that combines [PERSON]'s muscle model and human dynamics to estimate the joint angular velocity and joint angle, which can be applied to drive a manipulator [81]. [PERSON] et al. controlled a simple single-axis robot for upper-limb rehabilitation training for stroke patients using myoelectric signals from the triceps brachii [7]. [PERSON] proposed a control method for a 6-DOF robot that combines multi-sensor fusion perception and control, including eye tracking, EEG, EMG, and head motion sensors. This control method can be applied to control wheelchairs and daily assistive robots [127]. [PERSON] proposed an HRI method that combines both active and antagonist myoelectric signals to control a robotic gripper, allowing users to change grip stiffness by altering the degree of muscle contraction [49]. HRI can be applied to the remote operation of robots and the design of prosthetics. In [71], a hand synergistic control method combining a multifactor model and sEMG signals was proposed. A five-finger robotic hand with two degrees of freedom for each finger is illustrated in [69]. This robotic hand was driven by sEMG signals from the forearm, and control commands were generated by the classification results of EMG using the Random Forest (RF) method. [PERSON] introduced a method that applies an ANN to estimate lower-limb strength from sEMG signals and apply it to a knee rehabilitation robot.