全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...
-  2016 

一种上肢外骨骼康复机器人的控制系统研究

DOI: doi:10.7507/1001-5515.20160185

Keywords: 上肢外骨骼康复机器人, 语音控制, 肌电控制

Full-Text   Cite this paper   Add to My Lib

Abstract:

为帮助上肢功能障碍患者进行康复训练,本文设计了一种4自由度上肢外骨骼康复机器人,并实现了语音和肌电两种控制方案。本文基于RSC-4128语音芯片,实现了对特定人的识别。另外自制表面肌电(sEMG)信号采集电极,采集sEMG信号,并通过信号处理、时域特征提取、固定阈值算法等实现模式识别,还利用脉冲宽度调制(PWM)算法实现了对系统运动速度的调节。最后,对系统进行了语音和肌电控制的测试。结果表明语音控制平均识别率达93.1%,肌电控制平均识别率达90.9%,验证了系统控制方案的可行性,为上肢康复机器人控制系统的进一步完善奠定了理论基础

References

[1]  7. PERRY J C, ROSEN J, BUMS S. Upper-limb powered exoskeleton design[J]. IEEE-ASME Transactions on Mechatronics, 2007, 12(4): 408-417.
[2]  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.
[3]  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.
[4]  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.
[5]  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.
[6]  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.
[7]  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.
[8]  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.
[9]  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.
[10]  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.
[11]  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.
[12]  21. PHINYOMARK A, PHUKPATTARANONT P, LIMSAKUL C. Feature reduction and selection for EMG signal classification[J]. Expert Syst Appl, 2012, 39(8): 7420-7431.
[13]  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.
[14]  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.
[15]  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.
[16]  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.
[17]  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.
[18]  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.
[19]  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.
[20]  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.
[21]  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.
[22]  19. HOLI M S. Electromyography analysis for person identification[J]. Int.J.Biometrics Bioinformatics, 2011, 5(3): 172-179.
[23]  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.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133