Research on Control System of an Exoskeleton Upper-limb Rehabilitation Robot_Journal of Biomedical Engineering

Authors：

WANGLulu ^1,2 , HUXin ^2,3 , HUJie ^1,2 , FANGYoufang ^1,2 , HERongrong ^1,2 ,  YUHongliu ^1,2

1. Institute of Biomechanics and Rehabilitation Engineering, School of Medical Instrument and Food Eingineering, University of Shanghai for Science and Technology, Shanghai 200093, China;
2. Shanghai Engineering Research Center of Assistive Devices, Shanghai 200093, China;
3. Central Academy, Shanghai Electric Group Co., Ltd., Shanghai 200073, China;

Corresponding author：

YUHongliu, Email: yhl98@hotmail.com

Keywords：

upper-limb exoskeleton rehabilitation robot; voice control; electromyography control

DOI：

10.7507/1001-5515.20160185

Video：

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In order to help the patients with upper-limb disfunction go on rehabilitation training, this paper proposed an upper-limb exoskeleton rehabilitation robot with four degrees of freedom (DOF), and realized two control schemes, i.e., voice control and electromyography control. The hardware and software design of the voice control system was completed based on RSC-4128 chips, which realized the speech recognition technology of a specific person. Besides, this study adapted self-made surface eletromyogram (sEMG) signal extraction electrodes to collect sEMG signals and realized pattern recognition by conducting sEMG signals processing, extracting time domain features and fixed threshold algorithm. In addition, the pulse-width modulation(PWM)algorithm was used to realize the speed adjustment of the system. Voice control and electromyography control experiments were then carried out, and the results showed that the mean recognition rate of the voice control and electromyography control reached 93.1% and 90.9%, respectively. The results proved the feasibility of the control system. This study is expected to lay a theoretical foundation for the further improvement of the control system of the upper-limb rehabilitation robot.

Citation： WANGLulu, HUXin, HUJie, FANGYoufang, HERongrong, YUHongliu. Research on Control System of an Exoskeleton Upper-limb Rehabilitation Robot. Journal of Biomedical Engineering, 2016, 33(6): 1168-1175. doi: 10.7507/1001-5515.20160185 Copy

1.	李秀玲, 杜磊,李藏芬,等.卒中后偏瘫上肢功能康复研究进展[J].中国康复,2010,25(1):61-63.
2.	梁天佳. 脑卒中偏瘫上肢功能康复的技术与方法[J].中国康复理论与实践,2012,18(6):518-520.
3.	LU E C, WANG R H, HEBERT D, et al. The development of an upper limb stroke rehabilitation robot: identification of clinical practices and design requirements through a survey of therapists[J]. Disabil Rehabil Assist Technol, 2011, 6(5): 420-431.
4.	BAI Yulong, HU Yongshan, WU Yi, et al. A prospective, randomized, single-blinded trial on the effect of early rehabilitation on daily activities and motor function of patients with hemorrhagic stroke[J]. J Clin Neurosci, 2012, 19(10): 1376-1379.
5.	MEHRHOLZ J, PLATZ T, KUGLER J, et al. Electromechanical and robot-assisted arm training for improving arm function and activities of daily living after stroke[J]. Stroke, 2008, 40(4): CD006876.
6.	CALABRÒ R S, RUSSO M, NARO A, et al. Who May benefit from armeo power treatment? a neurophysiological approach to predict neurorehabilitation outcomes[J]. PM R, 2016: 1-8.
7.	PERRY J C, ROSEN J, BUMS S. Upper-limb powered exoskeleton design[J]. IEEE-ASME Transactions on Mechatronics, 2007, 12(4): 408-417.
8.	NEF T, GUIDALI M, RIENER R. ARMin III-arm therapy exoskeleton with an ergonomic shoulder actuation[J]. Applied Bionics & Biomechanics, 2009, 6(6): 127-142.
9.	李庆玲. 基于sEMG信号的外骨骼式机器人上肢康复系统研究[D].哈尔滨:哈尔滨工业大学,2009.
10.	XIONG Caihua, JIANG Xianzhi, SUN Ronglei, et al. Control methods for exoskeleton rehabilitation robot driven with pneumatic muscles[J]. Industrial Robot, 2009, 36(3): 210-220.
11.	王珏. 数字化上肢运动功能康复与治疗设备研发[J].中国科技成果,2015(8):22-24.
12.	赵春梅, 王玉惠.RS-485通讯协议在工业控制工程中的应用[J].油气田地面工程,2005,24(3):38-39.
13.	毛依雯. Modbus协议通讯介绍以及如何在mscomm和winsock上实现[J].信息通信,2013(8):31-32.
14.	王海鹏, 阙大顺,祁宠杰,等.基于RSC-4128的聋哑人语音交互系统设计[J].电气自动化,2013,35(4):12-14.
15.	HALIM S, BUDIHARTO W. The framework of navigation and voice recognition system of robot guidance for supermarket[J]. Intern J Software Eng Its Appl, 2014, 8(10): 143-152.
16.	朱鹏, 路灿,张艳.Super Flash型存储器SST39SF020的特性及应用[J].现代电子技术,2003(9):88-91.
17.	朱昊, 辛长宇,吉小军,等.表面肌电信号前端处理电路与采集系统设计[J].测控技术,2008,27(3):37-39.
18.	SURESH M, KRISHNAMOHAN P G, HOLI M S. GMM modeling of person information from EMG signals[C]//Recent Advances in Intelligent Computational Systems (RAICS), 2011 IEEE. IEEE, 2011: 712-717.
19.	HOLI M S. Electromyography analysis for person identification[J]. Int.J.Biometrics Bioinformatics, 2011, 5(3): 172-179.
20.	SURESH M, KRISHNAMOHAN P G, HOLI M S. Processing of natural signals like EMG for person identification using NUFB-GMM[J]. Int j adv comput res, 2014, 4(3): 819-827.
21.	PHINYOMARK A, PHUKPATTARANONT P, LIMSAKUL C. Feature reduction and selection for EMG signal classification[J]. Expert Syst Appl, 2012, 39(8): 7420-7431.
22.	BIN AHMAD NADZRI A A, AHMAD S A, MARHABAN M H, et al. Characterization of surface electromyography using time domain features for determining hand motion and stages of contraction[J]. Australas Phys Eng Sci Med, 2014, 37(1): 133-137.
23.	AL OMARI F, HUI Jiang, MEI Cong-li, et al. Pattern recognition of eight hand motions using feature extraction of forearm EMG signal[J]. Proc Natl Acad Sci U S A, 2014, 84(3): 473-480.

1. 李秀玲, 杜磊,李藏芬,等.卒中后偏瘫上肢功能康复研究进展[J].中国康复,2010,25(1):61-63.
2. 梁天佳. 脑卒中偏瘫上肢功能康复的技术与方法[J].中国康复理论与实践,2012,18(6):518-520.
3. LU E C, WANG R H, HEBERT D, et al. The development of an upper limb stroke rehabilitation robot: identification of clinical practices and design requirements through a survey of therapists[J]. Disabil Rehabil Assist Technol, 2011, 6(5): 420-431.
4. BAI Yulong, HU Yongshan, WU Yi, et al. A prospective, randomized, single-blinded trial on the effect of early rehabilitation on daily activities and motor function of patients with hemorrhagic stroke[J]. J Clin Neurosci, 2012, 19(10): 1376-1379.
5. MEHRHOLZ J, PLATZ T, KUGLER J, et al. Electromechanical and robot-assisted arm training for improving arm function and activities of daily living after stroke[J]. Stroke, 2008, 40(4): CD006876.
6. CALABRÒ R S, RUSSO M, NARO A, et al. Who May benefit from armeo power treatment? a neurophysiological approach to predict neurorehabilitation outcomes[J]. PM R, 2016: 1-8.
7. PERRY J C, ROSEN J, BUMS S. Upper-limb powered exoskeleton design[J]. IEEE-ASME Transactions on Mechatronics, 2007, 12(4): 408-417.
8. NEF T, GUIDALI M, RIENER R. ARMin III-arm therapy exoskeleton with an ergonomic shoulder actuation[J]. Applied Bionics & Biomechanics, 2009, 6(6): 127-142.
9. 李庆玲. 基于sEMG信号的外骨骼式机器人上肢康复系统研究[D].哈尔滨:哈尔滨工业大学,2009.
10. XIONG Caihua, JIANG Xianzhi, SUN Ronglei, et al. Control methods for exoskeleton rehabilitation robot driven with pneumatic muscles[J]. Industrial Robot, 2009, 36(3): 210-220.
11. 王珏. 数字化上肢运动功能康复与治疗设备研发[J].中国科技成果,2015(8):22-24.
12. 赵春梅, 王玉惠.RS-485通讯协议在工业控制工程中的应用[J].油气田地面工程,2005,24(3):38-39.
13. 毛依雯. Modbus协议通讯介绍以及如何在mscomm和winsock上实现[J].信息通信,2013(8):31-32.
14. 王海鹏, 阙大顺,祁宠杰,等.基于RSC-4128的聋哑人语音交互系统设计[J].电气自动化,2013,35(4):12-14.
15. HALIM S, BUDIHARTO W. The framework of navigation and voice recognition system of robot guidance for supermarket[J]. Intern J Software Eng Its Appl, 2014, 8(10): 143-152.
16. 朱鹏, 路灿,张艳.Super Flash型存储器SST39SF020的特性及应用[J].现代电子技术,2003(9):88-91.
17. 朱昊, 辛长宇,吉小军,等.表面肌电信号前端处理电路与采集系统设计[J].测控技术,2008,27(3):37-39.
18. SURESH M, KRISHNAMOHAN P G, HOLI M S. GMM modeling of person information from EMG signals[C]//Recent Advances in Intelligent Computational Systems (RAICS), 2011 IEEE. IEEE, 2011: 712-717.
19. HOLI M S. Electromyography analysis for person identification[J]. Int.J.Biometrics Bioinformatics, 2011, 5(3): 172-179.
20. SURESH M, KRISHNAMOHAN P G, HOLI M S. Processing of natural signals like EMG for person identification using NUFB-GMM[J]. Int j adv comput res, 2014, 4(3): 819-827.
21. PHINYOMARK A, PHUKPATTARANONT P, LIMSAKUL C. Feature reduction and selection for EMG signal classification[J]. Expert Syst Appl, 2012, 39(8): 7420-7431.
22. BIN AHMAD NADZRI A A, AHMAD S A, MARHABAN M H, et al. Characterization of surface electromyography using time domain features for determining hand motion and stages of contraction[J]. Australas Phys Eng Sci Med, 2014, 37(1): 133-137.
23. AL OMARI F, HUI Jiang, MEI Cong-li, et al. Pattern recognition of eight hand motions using feature extraction of forearm EMG signal[J]. Proc Natl Acad Sci U S A, 2014, 84(3): 473-480.

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