Review of sEMG for Robot Control: Techniques and Applications

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.

Automated summary

Surface electromyography (sEMG) is a promising technology for controlling robots through human-machine interfaces by capturing muscle activation signals. This article provides an overview of sEMG-based robot control, including signal processing and classification methods, as well as robot control strategies and methods based on sEMG. The article discusses the steps in sEMG signal processing, commonly used data acquisition and feature extraction methods, and introduces machine-learning-based pattern recognition methods for sEMG signal classification. It also classifies user intent-based robot control strategies into three categories and compares their control methods and applicable scenarios.