# Review of sEMG for Robot Control: Techniques and Applications
## 5 Discussion and Future Perspectives  
sEMG signals contain rich information, which can not only reflect the level of muscle activation and the strength of contraction, but also extract information on limb movement control, limb impedance, and muscle fatigue. The use of sEMG sensors does not require complex operations, and signals can be obtained by attaching electrodes to the skin, which is simple, convenient, and efficient. sEMG precedes movement, which makes it advantageous as a control interface for devices, and it can be used to predict human motion in real time. The myoelectric signals of the residual muscle can still provide movement information for people with movement disorders or amputations.  
However, there are still some unresolved challenges in using sEMG as a control signal for robotic arms. The information obtained from sEMG is ambiguous. The biological signals of adjacent muscles overlap and form cross-talk, making it difficult to extract  
Figure 3: sEMG-driven robot control method and control strategy.  
specific muscle information. Factors such as the electrode position and muscle fatigue during movement also affect the collected data. Differences between individuals, such as muscle development level and health status, mean the same control algorithm needs adjustment to its parameters to be applied to different people. There are few studies on individual differences, and the use of machine-learning methods to train with large sample sizes can reduce the negative effects of such differences to some extent.  
After more than 20 years of research, pre-processing methods and the selection of signal feature sets for sEMG signals have matured. Currently, the research focuses mainly on the following two areas: one is the improvement and innovation of pattern recognition algorithms based on machine learning to achieve higher classification performance, mainly in terms of accuracy; the other is the design of new HMIs based on sEMG.  
Many studies have been conducted on the use of sEMG for discrete robot control. These studies used supervised learning methods to map predefined actions onto the actions of the robot. Although this method is simple, its disadvantage is that the robot's movement capability is limited, and it can only perform several predetermined actions. Conversely, using sEMG to recognize continuous movement intentions and control the robot can greatly improve the flexibility of the controlled device; however, it is difficult to conduct theoretical research on the complex human muscle-bone model, which increases the difficulty of research.  
In clinical medicine, sEMG can be used as biofeedback for the treatment of diseases such as Parkinson's disease and stroke. [PERSON]'s research is dedicated to decomposing the obtained sEMG signal into motor unit action potentials (MUAP).  
As an emerging technology, sEMG has been widely researched and applied in fields such as rehabilitation therapy, sports, and the design of new human-robot interactions. With the continuous progress of technology and the decrease in cost, the practicality, convenience, and uniqueness of sEMG sensors will mean they have good development prospects.