Review of sEMG for Robot Control: Techniques and Applications

Abstract

Surface electromyography (sEMG) is a promising technology that can capture muscle activation signals to control robots through novel human–machine interfaces (HMIs). This technology has already been applied in scenarios such as prosthetic design, assisted robot control, and rehabilitation training. This article provides an overview of sEMG-based robot control, covering two important aspects: (1) sEMG signal processing and classification methods and (2) robot control strategies and methods based on sEMG. First, the article outlines the general steps in sEMG signal processing and summarizes the commonly used methods for data acquisition, pre-processing, and feature extraction. In addition, machine-learning-based pattern recognition methods have been introduced for sEMG signal classification. Subsequently, user intent-based robot control strategies are classified into three categories: full-human continuous control, semi-autonomous continuous control, and discrete control, and their control methods and applicable scenarios are compared. Finally, this article discusses the advantages, disadvantages, and future development prospects of sEMG-based robot control. This review provides a comprehensive overview of sEMG-based robot control, from signal processing and classification methods to robot control strategies and methods, aiming to guide future research on selecting filters, feature sets, and pattern recognition methods and to assist in establishing sEMG-driven robot control frameworks.

Novel Design of a Robotic Arm Prototype with Complex Movements Based on Surface EMG Signals to Assist Disabilities in Vietnam

“…This will increase the flexibility and applicability of the robot arm in real-life tasks. In addition, the research can also be expanded to apply machine learning and artificial intelligence methods to improve the precise recognition and control of robot arms based on sEMG signals [14][15][16][17][18][19][20]. Developments in this area will bring significant advances in supporting and enhancing the quality of life of individuals with disabilities.…”

“…In the past decades, ANN tools have garnered significant attention from researchers in the field of EMG signal classification. ANN has several advantages in EMG signal classification, such as the ability to learn from examples, high noise tolerance, and generalization capabilities in highdimensional input spaces [14][15][16][17][18][19][20]. In one experiment [14], researchers meticulously examined a surface electromyography (sEMG) signal classification system based on Deep Neural Networks (DNN).…”

“…Achieving a categorization accuracy of 95.04% for 52 gestures and demonstrating high performance in real scenarios, the proposed method holds promise for applications such as human intent detection and manipulator control. In [20], fuzzy neural networks were employed to represent the different elements affecting the primary muscles when the shoulder-elbow joint of the upper arm was positioned differently. This model utilizes multiple-channel sEMG signals as its input and translates them into the torque exerted on the human upper limb joints.…”

Research on the method of identifying upper and lower limb coordinated movement intentions based on surface EMG signals

“…Clinical rehabilitation research suggests that tailoring rehabilitation training to the patient’s limb movement patterns enhances rehabilitation efficiency ( Pichiorri et al, 2015 ; Song et al, 2023 ). sEMG signals, known for their non-invasiveness and operational simplicity, serve as a common tool to reflect human muscle activity, facilitating research in human motion classification ( Wu et al, 2016 ).…”

Does Exerting Grasps Involve a Finite Set of Muscle Patterns? A Study of Intra- and Intersubject Variability of Forearm sEMG Signals in Seven Grasp Types

“…A concerning issue is the intuitiveness of the control of such devices [20], which can be achieved if users do not have to change the way they do the tasks under normal conditions. However, there are several handicaps that hinder this intuitive control [21]: complexity of executing the hand grasp; redundancy of the neuromotor system; the existence of intra-and intersubject variability when doing the same task goal; the many sEMG characteristics that can be used to control different devices [22].…”