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基于表面肌电的运动意图识别方法研究及应用综述
A Review on Researches and Applications of sEMG-based Motion Intent Recognition Methods

查看参考文献83篇

文摘 表面肌电信号(Surface electromyography, sEMG)是人体自身的资源,蕴含着关联人体运动的丰富信息,用它作为交互媒介以构建人机交互(Human-robot interaction, HRI)系统有天然的优势.通过肌电信号实现人机自然交互的关键是由肌电信号识别出人体运动意图,通常包括离散动作模态分类、关节连续运动量估计及关节刚度/阻抗估计等三方面内容.本文详细归纳基于表面肌电的运动识别方法研究成果,总结当前研究的特点;随后,介绍基于表面肌电的运动识别技术的应用现状,并探讨制约其推广的主要问题;最后,展望该技术的未来发展.
其他语种文摘 Surface electromyography (sEMG) signals are human's own resources, which contain a wealth of information associated with one's movement. Thus, there is a natural advantage in utilizing sEMG signals as interface media to construct human-robot interaction (HRI) systems. The key to realize a natural HRI with sEMG is recognizing human's motion intention from sEMG signals, which usually involves three aspects, i.e., classifying discrete motion modes, estimating continuous movements of joints, and estimating stiffness or impedance of joints. This paper fully collects the researches on methods of sEMG-based motion recognition, and summarizes the features of current studies. Afterwards, this paper introduces the application status of sEMG-based motion recognition technology, and discusses the key issues constraining its marketing applications. Finally, future development of the technology is presented.
来源 自动化学报 ,2016,42(1):13-25 【核心库】
DOI 10.16383/j.aas.2016.c140563
关键词 表面肌电信号 ; 人机交互 ; 运动识别 ; 刚度估计 ; 自然控制
地址

中国科学院沈阳自动化研究所, 机器人学国家重点实验室, 沈阳, 110016

语种 中文
文献类型 综述型
ISSN 0254-4156
学科 自动化技术、计算机技术
基金 中国科学院沈阳自动化研究所机器人学重点实验室基金 ;  国家高技术研究发展计划(863计划) ;  国家自然科学基金
文献收藏号 CSCD:5614907

参考文献 共 83 共5页

1.  Goodrich M A. Human-robot interaction: a survey. Foundations and Trends in Human-Computer Interaction,2007,1(3):203-275 被引 8    
2.  胡进. 下肢康复机器人及其交互控制方法. 自动化学报,2014,40(11):2377-2390 被引 49    
3.  Nam Y. GOM-face: GKP, EOG, and EMG-based multimodal interface with application to humanoid robot control. IEEE Transactions on Biomedical Engineering,2014,61(2):453-462 被引 8    
4.  Artemiadis P. EMG-based robot control interfaces: past, present and future. Advances in Robotics & Automation,2012,1(2):1-3 被引 4    
5.  Ngeo J G. Continuous and simultaneous estimation of finger kinematics using inputs from an EMG-to-muscle activation model. Journal of NeuroEngineering and Rehabilitation,2014,11:122 被引 8    
6.  佟丽娜. 基于多路sEMG 时序分析的人体运动模式识别方法. 自动化学报,2014,40(5):810-821 被引 25    
7.  Chowdhury R H. Surface electromyography signal processing and classification techniques. Sensors,2013,13(9):12431-12466 被引 14    
8.  Ahsan M R. EMG signal classification for human computer interaction: a review. European Journal of Scientific Research,2009,33(3):480-501 被引 8    
9.  丁其川. 基于肌电信号的上肢多关节连续运动估计. 机器人,2014,36(4):469-476 被引 10    
10.  Ison M. Multi-directional impedance control with electromyography for compliant human-robot interaction. Proceedings of the 2015 International Conference on Rehabilitation Robotics (ICORR),2015:416-421 被引 1    
11.  Farina D. The extraction of neural strategies from the surface EMG. Journal of Applied Physiology,2004,96(4):1486-1495 被引 17    
12.  De Luca C J. Imaging the Behavior of Motor Units by Decomposition of the EMG Signal,2008 被引 1    
13.  Chu J U. A supervised feature-projection-based real-time EMG pattern recognition for multifunction myoelectric hand control. IEEE/ASME Transactions on Mechatronics,2007,12(3):282-290 被引 21    
14.  Scheme E J. Selective classification for improved robustness of myoelectric control under nonideal conditions. IEEE Transactions on Biomedical Engineering,2011,58(6):1698-1705 被引 6    
15.  Jamal M Z. Signal acquisition using surface EMG and circuit design considerations for robotic prosthesis. Computational Intelligence in Electromyography Analysis-A Perspective on Current Applications and Future Challenges,2012 被引 1    
16.  Li Z J. sEMG-based joint force control for an upper-limb powerassist exoskeleton robot. IEEE Journal of Biomedical and Health Informatics,2014,18(3):1043-1050 被引 4    
17.  Pons J L. Wearable Robots: Biomechatronic Exoskeletons,2008:87-122 被引 3    
18.  Gopura R A R C. Recent trends in EMG-based control methods for assistive robots. Electrodiagnosis in New Frontiers of Clinical Research,2013:237-268 被引 2    
19.  Gijsberts A. Movement error rate for evaluation of machine learning methods for sEMG-based hand movement classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering,2014,22(4):735-744 被引 9    
20.  Castellini C. sEMG-based estimation of human stiffness: towards impedance-controlled rehabilitation. Proceedings of the 5th International Conference on Biomedical Robotics and Biomechatronics,2014:604-609 被引 1    
引证文献 69

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2 侯增广 康复机器人与智能辅助系统的研究进展 自动化学报,2016,42(12):1765-1779
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