# Review of sEMG for Robot Control: Techniques and Applications
## 4 Application of sEMG Interface in Robot Control
### Classification of Robot Control Strategies
#### 4.1.3 Discrete Control Strategy (S3)  
Unlike the previous two control strategies, in this strategy, the human and robot movements are often dissimilar. The most convenient and commonly used human movement involves various gestures, and there are studies using other limb movements of amputees. On the other hand, robot movements are expressed as movement or joint rotation, and there are more types of controllable devices. However, this type of control only uses sEMG signals for simple threshold control switch-label settings for specific movements. The control logic is simple but limited to simple scenarios involving few movements. For scenarios with many movements, a certain amount of learning is required.  
[PERSON] defined five gestures as motion commands for a multi-axis mechanical arm, recorded sEMG signals using the Myo armband, identified them, and then controlled the mechanical arm to move in a predetermined manner [33]. [PERSON] collected EMG signals from the forearm using the Myo armband and compared the recognition accuracies of the SVM, LDA, and KNN algorithms [54]. The gesture recognition results were applied to control a mechanical arm with 5-DOF. In [31], the Myo armband was applied to recognize motions using sEMG and IMU signals, and the recognition results were used to control a PeopleBot home robot.  
[PERSON] mapped the sEMG signals of seven gestures to the movement and grasping of a mechanical arm, which matched seven predetermined movements [52]. The corresponding mechanical arm control commands were obtained from the results of SVM classification. [PERSON] employed sEMG and IMU to control a 5-DOF mechanical arm, where the IMU signals of the left and right arms controlled the movement of four rotating DOF, and the sEMG signal of the palm opening and closing controlled the movement of the end gripper [129]. [PERSON] controlled the movement of electric wheelchairs and gripped mechanical arms using sEMG signals from both upper limbs [85]. [PERSON] developed eight wrist movements and mapped them using fuzzy logic for wheelchair motion commands [78].  
[PERSON] investigated the utilization of residual limb gestures of amputees for prosthesis control via EMG signals [65]. [PERSON] et al. applied SVM classification to classify sEMG signals and mapped the results to four predetermined gestures, allowing a prosthetic robot to perform grabbing, left-right, up-and-down, and rotating movements [60]. [PERSON] et al. applied the results to the control of dexterous hand prostheses using sEMG signals from the forearms of amputees for motion classification recognition [6].  
[PERSON] et al. utilized four-channel sEMG signals from his forearm and hand to define six distinct actions and accomplished real-time control of a wearable five-finger robot forhand mobility rehabilitation training [20]. [PERSON] et al. defined ten gestures and utilized the KNN algorithm for sEMG classification recognition, translating these into mobile robot control commands [44]. [PERSON] et al. designed a simple mechanical arm for amputees that could be controlled by a sEMG interface for rudimentary movements [26]. [PERSON] et al. proposed a human-machine interface based on the fusion of three sensors: glosso-kinetic potential, electroocologram, and EMG signals from facial muscles, providing robot assistance for people with limb movement disorders or complete paralysis [17].