# Review of sEMG for Robot Control: Techniques and Applications
## 1 Introduction  
Surface electromyography (sEMG) is the superposition of numerous motor unit action potentials (MUAPs) in time and space, which can be recorded by sEMG sensors. sEMG-based technologies have been widely used in rehabilitation [1; 2; 3; 4; 5; 6; 7], exercise physiology [8; 9; 10], and other fields [11; 12; 13; 14; 15]. Research by Das Deutsche Zentrum fur Luft- und Raumfahrt (DLR) has shown that, among various biological signal interfaces in the design of HMIs, non-invasive methods in BCI (brain-computer interface technology, including electroencephalograms, magnetoencephalography, and functional magnetic resonance imaging) have poor time or spatial resolution, whereas invasive methods (electrocorticography and microelectrodes) perform well but have harsh usage conditions, making them unfeasible for robot control [16]. There have also been studies that explore the use of head, tongue, and eye movements as control signals [17]; however, these methods are difficult to apply to the continuous control of robotic arms. With the gradual maturity of sEMG sensortechnology, the convenience and practicality of using sEMG in device control has become increasingly prominent. The sEMG signal, which appears 50-100 ms before movement [18; 19], can be used to predict human movement and then control robots based on such predictions. There are numerous application scenarios for sEMG-based robot control. In the rehabilitation field, [PERSON] created an EMG-driven exoskeleton hand robot designed to assist in hand rehabilitation training of neurologically injured patients [20]. [PERSON] demonstrated the effectiveness of sEMG-driven robots in stroke rehabilitation training [7]. [PERSON] presented a fully wearable Hand Exoskeleton System (HES) controlled by a novel sEMG-based classification strategy [21]. It can also be used as an auxiliary device to assist individuals with limb movement impairment in their daily activities. [PERSON] introduced two novel strategies for power-assist exoskeleton control using sEMG [22]. [PERSON] has been working on sEMG-based neuro-fuzzy control in a power-assisted upper-limb exoskeleton [23]. [PERSON] and [PERSON] used sEMG as an interface to control a manipulator that can assist people with severe muscular atrophy (SMA) in some daily activities [24]. An integrated system with 27-DOF named EDAN consists of a wheelchair, a manipulator, and a robot hand and can help people with movement disorders in daily activities [25]. For amputees, some sEMG-driven prostheses are available [26; 27]. The mode of robot control varies depending on the type of robot and application scenario. A method combining a low-pass filter with spherical linear interpolation was used for lightweight robot trajectory generation in [PERSON]'s study. Guided by an sEMG signal, the robot can mimic human motion with a small delay [28]. Despite extensive research on sEMG in the academic community, its development in practical applications, particularly in robot control, is not yet mature. Factors such as instability and interindividual variability of sEMG signals pose significant challenges for control. A comprehensive review of the relevant literature will be constructive in synthesizing key research results and revealing major research trends in this field. Therefore, this study aimed to summarize and answer the following questions:  
* What methods are used in each step of sEMG signal processing? * What are the strategies and methods for robot control driven by sEMG? * What are the advantages and disadvantages of using sEMG for robot control, the main challenges encountered, and the future development trends? To address these questions, this paper searches for research articles, conference papers, and books related to the use of surface electromyography for robot control in the IEEE Xplore Digital Library, Web of Science, and PubMed databases using keywords such as sEMG, robot, exoskeleton, etc.