Exoskeleton Recognition of Human Movement Intent Based on Surface Electromyographic Signals: Review

被引:8
作者
Lu, Changjia [1 ,2 ]
Qi, Qingjie [2 ]
Liu, Yingjie [2 ]
Li, Dan [2 ]
Xian, Wenhao [2 ]
Wang, Yue [2 ]
Chen, Changbing [2 ]
Xu, Xin [2 ]
机构
[1] China Coal Res Inst, Beijing 100013, Peoples R China
[2] Emergency Sci Res Acad, China Coal Res Inst, Beijing 100013, Peoples R China
关键词
Exoskeletons; Electromyography; Human computer interaction; Trajectory; Social factors; Technological innovation; Sensors; Human-machine systems; Classification algorithms; Feature detection; Lower limb exoskeleton; human-machine interaction; movement intent recognition; electromyographic signals; feature classification; FEATURE-EXTRACTION; EMG SIGNALS; CLASSIFICATION; PERFORMANCE; REDUCTION; INTERFACE; MOTION; ROBUST;
D O I
10.1109/ACCESS.2024.3388044
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The lower limb exoskeleton technology is designed to facilitate the movement of human lower limbs. Significant progress has been made in this technology, which has important implications for rehabilitation patients and individuals who are eager to enhance their mobility. Electromyogram (EMG) signals, which encompass the complexity of human physiology, are integrated into lower limb exoskeletons due to their deep connection to movement and predictability before movement begins, and this integration is expected to enable intelligent control and improved human-computer interaction. This review explores a pattern based on EMG signals for identifying human motor intent in lower limb exoskeletons. Firstly, the development of lower limb exoskeleton and the existing lower limb exoskeleton products are systematically described. Combined with the intelligent control system of wearable device, the main methods and research progress of recognizing the motion intention of lower limb exoskeleton by surface EMG are discussed. It shows that the use of surface EMG can effectively improve the human-machine interaction of lower limb exoskeleton. Together, the study provides insight into the challenges that are hindering the commercialization of the market and provides a perspective on the future development of EMG signals.
引用
收藏
页码:53986 / 54004
页数:19
相关论文
共 131 条
[81]  
Otbioelettronica, Quattrocento
[82]   Transfer learning-based convolutional neural networks with heuristic optimization for hand gesture recognition [J].
Ozcan, Tayyip ;
Basturk, Alper .
NEURAL COMPUTING & APPLICATIONS, 2019, 31 (12) :8955-8970
[83]   FATIGUE COMPENSATION OF THE ELECTROMYOGRAPHIC SIGNAL FOR PROSTHETIC CONTROL AND FORCE ESTIMATION [J].
PARK, EJ ;
MEEK, SG .
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 1993, 40 (10) :1019-1023
[84]   A review of wearable sensors and systems with application in rehabilitation [J].
Patel, Shyamal ;
Park, Hyung ;
Bonato, Paolo ;
Chan, Leighton ;
Rodgers, Mary .
JOURNAL OF NEUROENGINEERING AND REHABILITATION, 2012, 9
[85]   QUANTITATIVE-ANALYSIS OF 4 EMG AMPLIFIERS [J].
PERREAULT, EJ ;
HUNTER, IW ;
KEARNEY, RE .
JOURNAL OF BIOMEDICAL ENGINEERING, 1993, 15 (05) :413-419
[86]   Feature reduction and selection for EMG signal classification [J].
Phinyomark, Angkoon ;
Phukpattaranont, Pornchai ;
Limsakul, Chusak .
EXPERT SYSTEMS WITH APPLICATIONS, 2012, 39 (08) :7420-7431
[87]  
Pingao H, 2020, Tech. Rep.
[88]   Performance Evaluation of Lower Limb Exoskeletons: A Systematic Review [J].
Pinto-Fernandez, David ;
Torricelli, Diego ;
Sanchez-Villamanan, Maria del Carmen ;
Aller, Felix ;
Mombaur, Katja ;
Conti, Roberto ;
Vitiello, Nicola ;
Moreno, Juan C. ;
Pons, Jose Luis .
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2020, 28 (07) :1573-1583
[89]  
Pravin A. J., 2023, INTCONF BIOSIGNALS I, P16
[90]   Identification of a feature selection based pattern recognition scheme for finger movement recognition from multichannel EMG signals [J].
Purushothaman, Geethanjali ;
Vikas, Raunak .
AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE, 2018, 41 (02) :549-559