Review of sEMG for Robot Control: Techniques and Applications
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Muscle activation and electromyography studies · EEG and Brain-Computer Interfaces · Neuroscience and Neural Engineering
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
Shanghai Golden Arrow Robot Technology Co., Ltd., 701, Building 3, No. 377 Shanlian Road, Baoshan District, Shanghai 200444, China
National Demonstration Center for Experimental Engineering Training Education, Shanghai University, Shanghai 200444, China
School of Artificial Intelligence, Anhui University of Science and Technology, Huainan 232001, China
Department of Aerospace Engineering, Toronto Metropolitan University, 350 Victoria Street, Toronto, ON M5B 2K3, Canada
Funding
National Natural Science Foundation of China · Shanghai Municipal of Science and Technology Commission · Special fund for Digital Transformation of Shanghai · Key Research and Development Program of Anhui Province · Open Project of the Medical and Industrial Integration Laboratory of Jiangning Hospital Affiliated to Nanjing Medical University
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