康复机器人的同步主动交互控制与实现

彭亮; 侯增广; 王卫群

doi:10.16383/j.aas.2015.c150270

康复机器人的同步主动交互控制与实现

doi: 10.16383/j.aas.2015.c150270

1.
中国科学院自动化研究所复杂系统管理与控制国家重点实验室北京 100190

基金项目:

国家自然科学基金(61175076,61225017,61421004)资助

详细信息

作者简介:
彭亮中国科学院自动化研究所复杂系统管理与控制国家重点实验室控制科学与工程专业博士研究生.主要研究方向为机器人控制,生物信号处理.E-mail:liang.peng@ia.ac.cn

王卫群中国科学院自动化研究所复杂系统管理与控制国家重点实验室副研究员.主要研究方向为康复机器人,人机动力学,人-机交互控制,生物电信号处理.E-mail:weiqun.wang@ia.ac.cn

通讯作者:
侯增广中国科学院自动化研究所研究员,复杂系统管理与控制国家重点实验室副主任.主要研究方向为机器人控制,智能控制理论与方法,嵌入式系统软硬件开发、医学和健康自动化领域的康复与手术机器人.本文通信作者.E-mail:zengguang.hou@ia.ac.cn

计量
- 文章访问数: 2373
- HTML全文浏览量: 127
- PDF下载量: 1934
- 被引次数: 0
出版历程
- 收稿日期: 2015-05-04
- 修回日期: 2015-08-07
- 刊出日期: 2015-11-20

Synchronous Active Interaction Control and Its Implementation for a Rehabilitation Robot

1.
The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190

Funds:

Supported by National Natural Science Foundation of China (61175076, 61225017, 61421004)

摘要

摘要: 提出了一种适用于康复机器人的人机交互控制方法. 结合一款具有平面并联结构的上肢康复机器人, 实现了与用户(患者)运动意图同步的、柔顺的主动康复训练. 在训练中, 利用自适应频率振荡器, 从表面肌电信号(Surface electromyography, sEMG)中获取运动模式信息, 然后结合运动模式和期望的正常运动轨迹, 生成与主动运动意图同步的参考训练轨迹. 本文通过仿真和实际实验对所提出的方法进行了验证, 振荡器可以在2~5s内快速实现与用户主动运动意图的同步, 然后利用阻抗控制器给予柔顺的辅助. 通过调节阻抗参数, 可以为患者的运动训练提供不同程度的辅助.
- 康复机器人 /
- 表面肌电信号 /
- 自适应频率振荡器 /
- 阻抗控制
Abstract: This paper proposes a novel human-robot interaction control method for rehabilitation robots. Based on an upper-limb rehabilitation robot, active training is realized, which is compliant, and can synchronize with the human motion intention. During the training, the user's motion pattern information is detected by the adaptive frequency oscillator, then a synchronous reference training trajectory is generated by combining the pattern information with normal trajectory features. The implementation of this method is described at the end of this paper, where the adaptive frequency oscillator can synchronize with surface electromyography (sEMG) within 2~5s, and an impedance controller provides compliant assistance. By simply adjusting the impedance parameters, different assistance levels can be achieved.
- Rehabilitation robot /
- surface electromyography (sEMG) /
- adaptive frequency oscillator /
- impedance control

HTML全文

参考文献(29)

[1]	Go A S, Mozaffarian D, Roger V L, Benjamin E J, Berry J D, Blaha M J, Dai S, Ford E S, Fox C S, Franco S, Fullerton H J, Gillespie C, Hailpern S M, Heit J A, Howard V J, Huffman M D, Judd S E, Kissela B M, Kittner S J, Lackland D T, Lichtman J H, Lisabeth L D, Mackey R H, Magid D J, Marcus G M, Marelli A, Matchar D B, McGuire D K, Mohler E R 3rd, Moy C S, Mussolino M E, Neumar R W, Nichol G, Pandey D K, Paynter N P, Reeves M J, Sorlie P D, Stein J, Towfighi A, Turan T N, Virani S S, Wong N D, Woo D, Turner M B; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics-2014 update:a report from the American heart association. Circulation, 2014, 129(3):e28-e292
[2]	[2] Maciejasz P, Eschweiler J, Gerlach-Hahn K, Jansen-Troy A, Leonhardt S. A survey on robotic devices for upper limb rehabilitation. Journal of NeuroEngineering and Rehabilitation, 2014, 11(1):3-32
[3]	Hu Jin, Hou Zeng-Guang, Chen Yi-Xiong, Zhang Feng, Wang Wei-Qun. Lower limb rehabilitation robots and interactive control methods. Acta Automatica Sinica, 2014, 40(11):2377-2390(胡进, 侯增广, 陈翼雄, 张峰, 王卫群. 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)
[4]	[4] Buerger S P, Palazzolo J J, Krebs H I, Hogan N. Rehabilitation robotics:adapting robot behavior to suit patient needs and abilities. In:Proceedings of the 2004 American Control Conference. Boston, USA:IEEE, 2004. 3239-3244
[5]	[5] Lnenburger L, Colombo G, Riener R. Biofeedback for robotic gait rehabilitation. Journal of NeuroEngineering and Rehabilitation, 2007, 4(1):1
[6]	[6] Lo A C, Guarino P D, Richards L G, Haselkorn J K, Wittenberg G F, Federman D G, Ringer R J, Wagner T H, Krebs H I, Volpe B T, Bever C T Jr, Bravata D M, Duncan P W, Corn B H, Maffucci A D, Nadeau S E, Conroy S S, Powell J M, Huang G D, Peduzzi P. Robot-assisted therapy for long-term upper-limb impairment after stroke. New England Journal of Medicine, 2010, 362(19):1772-1783
[7]	[7] Klamroth-Marganska V, Blanco J, Campen K, Curt A, Dietz V, Ettlin T, Felder M, Fellinghauer B, Guidali M, Kollmar A, Luft A, Nef T, Schuster-Amft C, Stahel W, Riener R. Three-dimensional, task-specific robot therapy of the arm after stroke:a multicentre, parallel-group randomised trial. The Lancet Neurology, 2014, 13(2):159-166
[8]	[8] Marchal-Crespo L, Reinkensmeyer D J. Review of control strategies for robotic movement training after neurologic injury. Journal of NeuroEngineering and Rehabilitation, 2009, 6(1):20
[9]	[9] Hu J, Hou Z G, Zhang F, Chen Y X, Li P F. Training strategies for a lower limb rehabilitation robot based on impedance control. In:Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. San Diego, USA:IEEE, 2012. 6032-6035
[10]	Wang W Q, Hou Z G, Tong L, Chen Y X, Peng L, Tan M. Dynamics modeling and identification of the human-robot interface based on a lower limb rehabilitation robot. In:Proceedings of the 2014 IEEE International Conference on Robotics and Automation. Hong Kong, China:IEEE, 2014. 6012-6017
[11]	Tong Li-Na, Hou Zeng-Guang, Peng Liang, Wang Wei-Qun, Chen Yi-Xiong, Tan Min. Multi-channel sEMG time series analysis based human motion recognition method. Acta Automatica Sinica, 2014, 40(5):810-821(佟丽娜, 侯增广, 彭亮, 王卫群, 陈翼雄, 谭民. 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)
[12]	Zhang F, Li P F, Hou Z G, Lu Z, Chen Y X, Li Q L, Tan M. sEMG-based continuous estimation of joint angles of human legs by using BP neural network. Neurocomputing, 2012, 78(1):139-148
[13]	Artoni F, Chisari C, Menicucci D, Fanciullacci C, Micera S. REMOV:EEG artifacts removal methods during Lokomat lower-limb rehabilitation. In:Proceedings of the 4th IEEE RAS EMBS International Conference on Biomedical Robotics and Biomechatronics. Roma, Italy:IEEE, 2012. 992-997
[14]	Cai L L, Fong A J, Liang Y, Burdick J, Edgerton V R. Assist-as-needed training paradigms for robotic rehabilitation of spinal cord injuries. In:Proceedings of the 2006 IEEE International Conference on Robotics and Automation. Orlando, USA:IEEE, 2006. 3504-3511
[15]	Krebs H I, Palazzolo J J, Dipietro L, Ferraro M, Krol J, Rannekleiv K, Volpe B T, Hogan N. Rehabilitation robotics:Performance-based progressive robot-assisted therapy. Autonomous Robots, 2003, 15(1):7-20
[16]	Riener R, Nef T, Colombo G. Robot-aided neurorehabilitation of the upper extremities. Medical and Biological Engineering and Computing, 2005, 43(1):2-10
[17]	Peng L, Hou Z G, Peng L, Wang W Q. Design of CASIAARM:a novel rehabilitation robot for upper limbs. In:Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems. Hamburg, Germany:IEEE, 2015. 5611-5616
[18]	Li L, Baum B S. Electromechanical delay estimated by using electromyography during cycling at different pedaling frequencies. Journal of Electromyography and Kinesiology, 2004, 14(6):647-652
[19]	Cifrek M, Medved V, Tonković S, Ostojić S. Surface EMG based muscle fatigue evaluation in biomechanics. Clinical Biomechanics, 2009, 24(4):327-340
[20]	Winslow J, Martinez A, Thomas C K. Automatic identification and classification of muscle spasms in long-term EMG recordings. IEEE Journal of Biomedical and Health Informatics, 2015, 19(2):464-470
[21]	Ajallooeian M, van den Kieboom J, Mukovskiy A, Giese M A, Ijspeert A J. A general family of morphed nonlinear phase oscillators with arbitrary limit cycle shape. Physica D:Nonlinear Phenomena, 2013, 263:41-56
[22]	Yu J Z, Tan M, Chen J, Zhang J W. A survey on CPG-inspired control models and system implementation. IEEE Transactions on Neural Networks and Learning Systems, 2014, 25(3):441-456
[23]	Righetti L, Buchli J, Ijspeert A J. Dynamic Hebbian learning in adaptive frequency oscillators. Physica D:Nonlinear Phenomena, 2006, 216(2):269-281
[24]	Righetti L, Ijspeert A J. Programmable central pattern generators:an application to biped locomotion control. In:Proceedings of the 2006 IEEE International Conference on Robotics and Automation. Orlando, USA:IEEE, 2006. 1585-1590
[25]	Hogan N. Impedance control:an approach to manipulation. In:Proceedings of the 1984 American Control Conference. San Diego, USA:IEEE, 1984. 304-313
[26]	Yu H N. Modeling and control of hybrid machine systems:a five-bar mechanism case. International Journal of Automation and Computing, 2006, 3(3):235-243
[27]	Peng L, Hou Z G, Wang W Q. Dynamic modeling and control of a parallel upper-limb rehabilitation robot. In:Proceedings of the 2015 IEEE/RAS-EMBS International Conference on Rehabilitation Robotics. Singapore, 2015. 532-537
[28]	Winter D A. Biomechanics and Motor Control of Human Movement (4th Edition). United Kingdom:John Wiley Sons Ltd, 2009.
[29]	Hogan N. An organizing principle for a class of voluntary movements. The Journal of Neuroscience, 1984, 4(11):2745-2754