Please wait a minute...
工程设计学报
Chinese Journal of Engineering Design  2019, Vol. 26 Issue (3): 252-259    DOI: 10.3785/j.issn.1006-754X.2019.03.002
Innovative Design     
Human-robot interactive information sensing system for gait rehabilitation training robot
GUO Bing-jing1,2, MAO Yong-fei1, HAN Jian-hai1,2,3, LI Xiang-pan1,2, MA Jin-qi1
1.School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, China
2.Henan Provincial Key Laboratory of Robotics and Intelligent Systems, Luoyang 471003, China
3.Collaborative Innovation Center of Machinery Equipment Advanced Manufacturing of Henan Province, Luoyang 471003, China
Download: HTML     PDF(1694KB)
Export: BibTeX | EndNote (RIS)      

Abstract  

Aiming at the requirements of real-time interaction between the gait rehabilitation training robot and the patient，a sensing system for the contact force between them was developed. The lower limb exoskeleton robot dynamics model including the active force of human was established by Lagrange method，and the patient's lower limb movement intentions were analyzed to provide judgment criterions for the interactive control in gait rehabilitation training. The static and dynamic measurement experiments and the passive/active rehabilitation training measurement experiments were carried out on the gait rehabilitation training robot prototype system. The results showed that the sensing system could satisfy the detecting accuracy requirements for human-robot contact force, and acquire human movement intention in the rehabilitation training. The design of the human-robot interactive information sensing system lays a foundation for the research on the intelligent interactive control strategy of gait rehabilitation training robot.

Key words： gait rehabilitation training      exoskeleton robot      human-robot interaction      human-robot contact force      sensing system     
Received: 24 August 2018      Published: 28 June 2019
CLC:  TP 242.6  
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
GUO Bing-jing
MAO Yong-fei
HAN Jian-hai
LI Xiang-pan
MA Jin-qi
Cite this article:

GUO Bing-jing, MAO Yong-fei, HAN Jian-hai, LI Xiang-pan, MA Jin-qi. Human-robot interactive information sensing system for gait rehabilitation training robot. Chinese Journal of Engineering Design, 2019, 26(3): 252-259.

URL:

https://www.zjujournals.com/gcsjxb/10.3785/j.issn.1006-754X.2019.03.002     OR     https://www.zjujournals.com/gcsjxb/Y2019/V26/I3/252


步态康复训练机器人人机交互信息感知系统

针对步态康复训练机器人与患者实时交互的需求，研发了获取人机接触力信息的感知系统。利用拉格朗日法建立包含人体主动力的下肢外骨骼机器人动力学模型，并分析患者下肢运动动作意图，为步态康复训练交互控制提供判断依据。在步态康复训练机器人样机系统上进行了静态、动态测量实验及被动/主动康复训练测量实验，结果表明该感知系统能够满足人机接触力的检测精度要求，能在康复训练中获取人体运动意图，这为步态康复训练机器人的智能交互控制策略研究奠定了基础。


关键词： 步态康复训练,  外骨骼机器人,  人机交互,  人机接触力,  感知系统 

1 National Bureau of Statistics of the People's Republic of China. Main data bulletin of the 6th National Population Census in 2010 (No.1) [J]. Chinese Journal of Family Planning, 2011, 19(8): 511-512.
2 ZHANGTong. Chinese stroke rehabilitation treatment guide (2011 full version) [J]. Chinese Journal of Rehabilitation Theory and Practice, 2012, 18(4): 301-318.
3 KAZEROONIH. The Berkeley lower extremity exoskeleton[J]. Journal of Dynamic Systems Measurement & Control, 2006, 128(1): 9-15. doi:10.1007/978-3-540-33453-8_2
4 ZOSSA B, KAZEROONIH, CHUA. Biomechanical design of the Berkeley lower extremity exoskeleton (BLEEX)[J]. IEEE/ASME Transactions on Mechatronics, 2006, 11(2): 128-138.doi:10.1109/tmech.2006.871087
5 MEULEMANJ, ASSELDONK EVAN , OORT GVAN , et al. LOPES II—Design and evaluation of an admittance controlled gait training robot with shadow-leg approach[J]. IEEE Transactions on Neural Systems & Rehabilitation Engineering A Publication of the IEEE Engineering in Medicine & Biology Society, 2015, 24(3): 352-363. doi:10.1109/TNSRE.2015.2511448
6 ROSSI S MDE , VELLOREN, STUARTS J, et al. Soft artificial tactile sensors for the measurement of human-robot interaction in the rehabilitation of the lower limb[C]//Annual International Conference of the IEEE Engineering in Medicine & Biology, Buenos Aires, Aug. 31-Sep. 4, 2010. doi:10.1109/IEMBS.2010.5626409
7 MIRBAGHERIM M, TSAOC, PELOSINE, et al. Therapeutic effects of robotic-assisted locomotor training on neuromuscular properties[C]//International Conference on Rehabilitation Robotics, Chicago, Jun. 28-Jul.1, 2005. doi:10.1109/icorr.2005.1502025
8 JEZERNIKSAŠO , COLOMBOG, KELLERT, et al. Robotic orthosis lokomat: a rehabilitation and research tool[J]. Neuromodulation Technology at the Neural Interface, 2010, 6(2): 108-115.doi:10.1046/j.1525-1403.2003.03017.x
9 YANGJin-jiang. Research on multi-signal fusion sensing system for assisted lower extremity exoskeleton robot[D]. Hangzhou: Zhejiang University, School of Mechanical Engineering, 2016: 28-31.
10 HUANGZi-liang, FANGChen-hao, OUYANGXiao-ping, et al. Research on the sensing system of lower limb exoskeleton robot based on multi-information fusion[J]. Chinese Journal of Engineering Design, 2018, 25(2): 159-166.
11 FANYuan-jie. Study on lower limb exoskeleton for rehabilitation based on multi-source information fusion including sEMG & interactive force and its clinical trial [D]. Shanghai: Shanghai Jiaotong University, Institute of Robotic， 2014: 61-62.
12 SUNJian, YUYong, GEYun-jian, et al. Research on sensing system of wearable power assist leg[J]. Micronanoelectronic Technology, 2007, 44(7): 353-357.
13 SHENLin-yong, ZHANGYa-nan, QIANJin-wu, et al. Measuring on coupling force between human-robot of lower limb walking rehabilitation training robot[J]. Electronic Measurement Technology, 2012, 35(12): 14-18.
14 SUNJian, YUYong, GEYun-jian, et al. Research on multi-sensors perceptual system of wearable power assist leg based on interaction force signal and joint angle signal [J]. Journal of University of Science and Technology of China, 2008, 38(12): 1432-1438.
15 NOVAKD, RIENERR. A survey of sensor fusion methods in wearable robotics[J]. Robotics & Autonomous Systems, 2015, 73(C): 155-170.doi:10.1016/j.robot.2014.08.012
16 KONGK, JEOND. Design and control of an exoskeleton for the elderly and patients[J]. IEEE/ASME Transactions on Mechatronics, 2006, 11(4): 428-432. doi:10.1109/tmech.2006.878550
17 BAIDEND, IVLEVO. Human-robot-interaction control for orthoses with pneumatic soft-actuators-concept and initial trails[C]//IEEE International Conference on Rehabilitation Robotics, Washington, Jun. 24-26, 2013.doi:10.1109/ICORR.2013.6650353
18 HAYASHIT, KAWAMOTOH, SANKAIY. Control method of robot suit HAL working as operator's muscle using biological and dynamical information[C]//Intelligent Robots and Systems(IROS2005), Edmonton, Alta, Aug. 2-6, 2005. doi:10.1109/iros.2005.1545505
19 SHIXiao-hua, WANGHong-bo, SUNLi, et al. Structural design and dynamics analysis of exoskeleton lower limb rehabilitation robot[J]. Journal of Mechanical Engineering, 2014, 50(3): 41-48.
[1] HUANG Zi-liang, Fang Chen-hao, OUYANG Xiao-ping, YANG Jin-jiang, YANG Hua-yong. Research on the sensing system of lower limb exoskeleton robot based on multi-information fusion[J]. Chinese Journal of Engineering Design, 2018, 25(2): 159-166.