电子测量与仪器学报

徐瑞,李志才,王雯婕,王紫尧,明东.基于肌电的人机交互控制策略及其应用与挑战[J].电子测量与仪器学报,2020,34(2):1-11

基于肌电的人机交互控制策略及其应用与挑战

Human computer interaction control strategies based onelectromyography and their applications and challenges

DOI：

英文关键词:surface electromyography human computer interaction myoelectric control strategy pattern recognition motor rehabilitation

基金项目:国家重点研发计划（2017YFB1002504）、国家自然科学基金（81901860，51905375，61877042）、中国博士后科学基金（2019M651043，2019M651033）资助项目

作者	单位
徐瑞	1.天津大学医学工程与转化医学研究院，2.天津大学精密仪器与光电子工程学院
李志才	1.天津大学医学工程与转化医学研究院
王雯婕	1.天津大学医学工程与转化医学研究院
王紫尧	2.天津大学精密仪器与光电子工程学院
明东	1.天津大学医学工程与转化医学研究院，2.天津大学精密仪器与光电子工程学院

Author	Institution
Xu Rui	1.Academy of Medical Engineering and Translational Medicine, Tianjin University，2.College of Precision Instruments & Optoelectronics Engineering, Tianjin University
Li Zhicai	1.Academy of Medical Engineering and Translational Medicine, Tianjin University
Wang Wenjie	1.Academy of Medical Engineering and Translational Medicine, Tianjin University
Wang Ziyao	2.College of Precision Instruments & Optoelectronics Engineering, Tianjin University
Ming Dong	1.Academy of Medical Engineering and Translational Medicine, Tianjin University，2.College of Precision Instruments & Optoelectronics Engineering, Tianjin University

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中文摘要:

基于表面肌电信号的控制策略是人机交互技术的一种重要基础，在人类对外围设备的控制方面具有重要的意义。肌电控制策略通过人体的表面肌电信号识别出人体的运动意图，进而转化为控制指令实现对外围设备的精确、稳定控制，改变了使用传统输入设备在便携性、操作空间以及特殊人群等方面的局限性。论述了基于肌电控制策略的发展历程，并比较了表面肌电信号相较于脑电信号作为控制系统输入信号的差异;阐述了基于表面肌电信号的控制策略在康复及辅助领域的应用情况，并指出了相关应用的未来改进和突破方向;分析和探讨了制约基于模式识别技术的肌电控制策略发展中涉及的表面肌电信号采集与提高肌电控制系统的控制效果和鲁棒性的相关技术问题，并给肌电控制系统的后续研究提出了一些可行的切入点。

英文摘要:

The control strategy based on surface electromyography (sEMG) is an important basis for human computer interaction and has important significance in human control of peripheral devices. The myoelectric control strategy recognizes the body′s motion intention through sEMG, and then converts it into control commands to achieve a precise and stable control of peripheral devices. The use of sEMG has overcome the limitations of traditional input devices in terms of portability, operating space and special groups. This paper discussed the development of the myoelectric control strategy, and compared the differences between sEMG and the Electroencephalogram (EEG) as the input of the control system. The application of the sEMG based control strategy in rehabilitation and orthopedics was expounded, and the future improvement of these applications was pointed out. This paper analyzed the technical problems related to the acquisition of sEMG and the improvement of the control effect and robustness, which hindered the development of myoelectric control strategy based on pattern recognition. Finally, the paper listed some feasible improvement directions for sEMG control systems.

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