康复机器人与智能辅助系统的研究进展

侯增广; 赵新刚; 程龙; 王启宁; 王卫群

doi:10.16383/j.aas.2016.y000006

康复机器人与智能辅助系统的研究进展

doi: 10.16383/j.aas.2016.y000006

侯增广^1,2,3, ,,
赵新刚^4,,
程龙^1,3,,
王启宁^5,,
王卫群^1,

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
2.
中国科学院脑科学与智能技术卓越创新中心北京 100190
3.
中国科学院大学北京 100049
4.
中国科学院沈阳自动化研究所机器人学国家重点实验室沈阳 110016
5.
北京大学工学院北京大学工程科学与新兴技术高精尖创新中心北京 100871

详细信息

作者简介:
赵新刚中国科学院沈阳自动化研究所机器人学国家重点实验室研究员.主要研究方向为机器人控制, 智能系统与康复机器人.E-mail:zhaoxingang@sia.cn

程龙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研究员.主要研究方向为机器人系统的智能控制.E-mail:long.cheng@ia.ac.cn

王启宁北京大学工学院研究员.2009年获得北京大学力学系博士学位.主要研究方向为智能机器人, 康复工程.E-mail:qiningwang@pku.edu.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, 生理电信号处理.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复杂系统管理与控制国家重点实验室研究员.主要研究方向为机器人与智能系统, 康复机器人与微创介入手术机器人. E-mail:zengguang.hou@ia.ac.cn

计量
- 文章访问数: 3399
- HTML全文浏览量: 1488
- PDF下载量: 3010
- 被引次数: 0
出版历程
- 收稿日期: 2016-11-17
- 刊出日期: 2016-12-01

Recent Advances in Rehabilitation Robots and Intelligent Assistance Systems

HOU Zeng-Guang^{1,2,3
, ,},
ZHAO Xin-Gang^4
,,
CHENG Long^{1,3
,},
WANG Qi-Ning^5
,,
WANG Wei-Qun^1
,

1.
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190
2.
CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing 100190
3.
University of Chinese Academy of Sciences, Beijing 100049
4.
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016
5.
College of Engineering, Beijing Innovation Center for Engineering Science and Advanced Technology (BIC-ESAT), Peking University, Beijing 100871

More Information

Author Bio:
Professor at State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences. His research interest covers robot control, intelligent systems and rehabilitation robots

Professor at the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences. His main research interest is intelligent robotic control

Professor at the College of Engineering, Peking University. He received his Ph. D. degree from Peking University in 2009. His research interest covers robotics and rehabilitation engineering

Associate professor at the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences. His research interest covers rehabilitation robot, dynamics of human-robot system, human-robot interaction control, and biomedical signal processing

Corresponding author: HOU Zeng-Guang Professor at the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences. His research interest covers intelligent robotic systems, rehabilitation and surgery robots

摘要

摘要: 我国正面临日益严重的老龄化问题和数量庞大的残疾人群，康复机器人与智能辅助系统的研究开发和应用有望为解决养老、失能辅助和康复问题提供部分技术手段.康复机器人与智能辅助系统涉及医学、信息、机械、电子、材料、力学等多个学科领域，其研究与开发也面临诸多挑战和困难，本文从“康复机器人及多种康复训练模式”、“智能辅助系统与生机电技术”、“康复与辅助相关的多模态传感与控制方法”、“外骨骼和可穿戴系统、智能假肢与人机安全性”等方面介绍和讨论康复机器人和智能辅助系统的问题和研究进展，以期为未来康复机器人和智能辅助系统的研究与开发提供些许借鉴.
- 康复机器人 /
- 智能辅助系统 /
- 外骨骼 /
- 可穿戴系统 /
- 智能假肢 /
- 康复训练 /
- 多模态传感 /
- 人机交互 /
- 生机电技术 /
- 人机安全性
Abstract: We are facing serious issues of an increasingly aging population and a huge number of disabled people. The research and applications of rehabilitation robots and intelligent assistance systems would provide potential solutions to the elderly and disabled people with efficient care, assistance and rehabilitation methods and tools. The design and implementation of rehabilitation robots and intelligent assistance systems require the multidisciplinary knowledge, such as medical science, information technology, mechanical engineering, material science and mechatronics, and face many challenges and difficulties. To help the interested researchers have an overview of this promising area, this paper reviews the recent advances in rehabilitation robots and intelligent assistance systems in the following aspects:rehabilitation robots and multi-mode rehabilitation training methods; intelligent assistance systems and bio-electro-mechanical systems; multi-mode sensing and control methods in the rehabilitation robots and assistance systems, and exoskeleton and wearable systems, intelligent prosthesis and human-machine safety.
- Rehabilitation robot /
- intelligent assistance system /
- exoskeleton /
- wearable system /
- intelligent prosthesis /
- rehabilitation training /
- multi-mode sensing /
- human-machine interaction /
- bio-electro-mechanical system /
- human-machine safety

HTML全文

参考文献(127)

[1]	潘畅, 徐麟.中风偏瘫实用康复术图解.北京:中国中医药出版社, 1999. Pan Chang, Xu Lin. Diagram of Practical Rehabilitation for Stroke Patients with Hemiplegia. Beijing:China Press of Traditional Chinese Medicine, 1999.
[2]	Krebs H I, Volpe B T, Williams D, Celestino J, Charles S K, Lynch D, Hogan N. Robot-aided neurorehabilitation:a robot for wrist rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2007, 15(3):327-335 doi: 10.1109/TNSRE.2007.903899
[3]	Krebs H I, Hogan N, Volpe B T, Aisen M L, Edelstein L, Diels C. Overview of clinical trials with MIT-MANUS:a robot-aided neuro-rehabilitation facility. Technology and Health Care, 1999, 7(6):419-423 https://www.researchgate.net/publication/12649007_Overview_of_clinical_trials_with_MIT-MANUS_A_robot-aided_neuro-_rehabilitation_facility
[4]	Loureiro R, Amirabdollahian F, Topping M, Driessen B, Harwin W. Upper limb robot mediated stroke therapy-GENTLE/s approach. Autonomous Robots, 2003, 15(1):35-51 doi: 10.1023/A:1024436732030
[5]	Burgar C G, Lum P S, Shor P C, Van der Loos H F M. Development of robots for rehabilitation therapy:the Palo Alto VA/Stanford experience. Journal of Rehabilitation Research and Development, 2000, 37(6):663-673 https://www.researchgate.net/publication/12015148_Development_of_robots_for_rehabilitation_therapy_The_Palo_Alto_VAStanford_experience
[6]	Nef T, Guidalic M, Riener R. ARMin III-arm therapy exoskeleton with an ergonomic shoulder actuation. Applied Bionics and Biomechanics, 2009, 6(2):127-142 doi: 10.1155/2009/962956
[7]	Colombo G, Joerg M, Schreier R, Dietz V. Treadmill training of paraplegic patients using a robotic orthosis. Journal of Rehabilitation Research and Development, 2000, 37(6):693-700 http://www.academia.edu/15095877/Treadmill_training_of_paraplegic_patients_using_a_robotic_orthosis
[8]	Zanotto D, Stegall P, Agrawal S K. ALEX Ⅲ:a novel robotic platform with 12 DOFs for human gait training. In:Proceedings of the 2013 IEEE International Conference on Robotics and Automation (ICRA). Karlsruhe, Germany:IEEE, 2013. 3914-3919
[9]	Freivogel S, Schmalohr D, Mehrholz J. Improved walking ability and reduced therapeutic stress with an electromechanical gait device. Journal of Rehabilitation Medicine, 2009, 41(9):734-739 doi: 10.2340/16501977-0422
[10]	Schmidt H, Krüger J, Hesse S. HapticWalker-haptic foot device for gait rehabilitation. Human Haptic Perception:Basics and Applications. Basel:Springer, 2008. 501-511
[11]	Susko T G. MIT Skywalker:A Novel Robot for Gait Rehabilitation of Stroke and Cerebral Palsy Patients[Ph.D. dissertation], Massachusetts Institute of Technology, USA, 2015.
[12]	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):Article No.3
[13]	Krebs H I. Rehabilitation robotics:an academic engineer perspective. In:Proceedings of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS). Boston, Massachusetts, USA:IEEE, 2011. 6709-6712
[14]	Lotze M, Braun C, Birbaumer N, Anders S, Cohen L G. Motor learning elicited by voluntary drive. Brain, 2003, 126(4):866-872 doi: 10.1093/brain/awg079
[15]	丁其川, 熊安斌, 赵新刚, 韩建达.基于表面肌电的运动意图识别方法研究及应用综述.自动化学报, 2016, 42(1):13-25) http://www.aas.net.cn/CN/abstract/abstract18792.shtml Ding Qi-Chuan, Xiong An-Bin, Zhao Xin-Gang, Han Jian-Da. A review on researches and applications of sEMG-based motion intent recognition methods. Acta Automatica Sinica, 2016, 42(1):13-25( http://www.aas.net.cn/CN/abstract/abstract18792.shtml
[16]	Van Dijk W, Van der Kooij H, Koopman B, and Van Asseldonk E H. Improving the transparency of a rehabilitation robot by exploiting the cyclic behaviour of walking. In:Proceedings of the 2013 IEEE International Conference on Rehabilitation Robotics. Seattle, Washington, USA:IEEE, 2013. 1-6
[17]	Shirzad N and Van der Loos H. Error amplification to promote motor learning and motivation in therapy robotics. In:Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2012. 3907-3910
[18]	Duschau-Wicke A, Von Zitzewitz J, CaprezA, Luenenburger L, Riener R. Path control:A method for patient-cooperative robot-aided gait rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineerin, 2010, 18(1):38-48 doi: 10.1109/TNSRE.2009.2033061
[19]	del-Ama A J, Gil-Agudo Á, Pons J L, Moreno J C. Hybrid FES-robot cooperative control of ambulatory gait rehabilitation exoskeleton. Journal of NeuroEngineering and Rehabilitation, 2014, 11(1):Article No.27
[20]	Hillier S, Immink M, Thewlis D. Assessing proprioception:a systematic review of possibilities. Neurorehabilitation and Neural Repair, 2015, 29(10):933-949 doi: 10.1177/1545968315573055
[21]	Neckel N D, Blonien N, Nichols D, Hidler J. Abnormal joint torque patterns exhibited by chronic stroke subjects while walking with a prescribed physiological gait pattern. Journal of NeuroEngineering and Rehabilitation, 2008, 5(3):Article No.19
[22]	Latash M, Zatsiorsky V M. Biomechanics and Motor Control:Defining Central Concepts. Cambridge:Academic Press, 2015.
[23]	Awai L, Curt A. Intralimb coordination as a sensitive indicator of motor-control impairment after spinal cord injury. Frontiers in Human Neuroscience, 2014, 8(6):Article No.148
[24]	Clarkson H M. Joint Motion and Function Assessment:A Research-Based Practical Guide. Philadelphia:Lippincott Williams and Wilkins, 2005.
[25]	Riener R, Lünenburger L, Maier I C, Colombo G, Dietz V. Locomotor training in subjects with sensori-motor deficits:an overview of the robotic gait orthosis lokomat. Journal of Healthcare Engineering, 2010, 1(2):197-216 doi: 10.1260/2040-2295.1.2.197
[26]	Banala S K, Kim S H, Agrawal S K, Scholz J P. Robot assisted gait training with active leg exoskeleton (ALEX). IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2009, 17(1):2-8 doi: 10.1109/TNSRE.2008.2008280
[27]	Veneman J F, Kruidhof R, Hekman E E G, Ekkelenkamp R, Van Asseldonk E H F, Van Der Kooij H. Design and evaluation of the LOPES exoskeleton robot for interactive gait rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2007, 15(3):379-386 doi: 10.1109/TNSRE.2007.903919
[28]	何清华, 黄素平, 黄志雄.智能轮椅的研究现状和发展趋势.机器人技术与应用, 2003, (2):12-16) http://www.cnki.com.cn/Article/CJFDTOTAL-JIQI200302004.htm He Qing-Hua, Huang Su-Ping, Huang Zhi-Xiong. The research status and development trend of intelligent wheelchair. Robot Technology and Application, 2003, (2):12-16( http://www.cnki.com.cn/Article/CJFDTOTAL-JIQI200302004.htm
[29]	Yanco H A. Wheelesley:a robotic wheelchair system:indoor navigation and user interface. Assistive technology and artificial intelligence. Berlin Heidelberg:Springer, 1998. 256-268
[30]	Christensen H V, Garcia J C. Infrared non-contact head sensor for control of wheelchair movements. In:Proceedings of the 8th European Conference for the Advancement of Assistive Technology in Europe. Lille, France, 2005. 336-340
[31]	Matsumoto O, Komoriya K, Hatase T, Nishimura H. Autonomous traveling control of the "TAO Aicle" intelligent wheelchair. In:Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. Beijing, China:IEEE, 2006. 4322-4327
[32]	Lu T, Yuan K, Zou W, Hu H S. Study on navigation strategy of intelligent wheelchair in narrow spaces. In:Proceedings of the 6th World Congress on Intelligent Control and Automation. Dalian, China:IEEE, 2006. 9252-9256
[33]	Jia P, Hu H H, Lu T, Yuan K. Head gesture recognition for hands-free control of an intelligent wheelchair. Industrial Robot:An International Journal, 2007, 34(1):60-68 http://www.citeulike.org/article/1047677
[34]	Zou W, Ye A X, Lu T, Ren Y N, Xu Z D, Yuan K. Contour detection and localization of intelligent wheelchair for parking into and docking with U-shape bed. In:Proceedings of the 2011 IEEE International Conference on Robotics and Biomimetics. Karon Beach, Phuket:IEEE, 2011. 378-383
[35]	曾翔.面向助老助残的智能轮椅开发[硕士学位论文], 上海交通大学, 中国, 2007) http://cdmd.cnki.com.cn/Article/CDMD-10248-2007052603.htm Zeng Xiang. Developing Smart Wheelchair for the Handicapped and the Elderly[Master dissertation], Shanghai Jiao Tong University, China, 2007 http://cdmd.cnki.com.cn/Article/CDMD-10248-2007052603.htm
[36]	王丽军, 王景川, 陈卫东.动态环境下智能轮椅的路径规划与导航.上海交通大学学报, 2010, 44(11):1524-1528) http://www.cnki.com.cn/Article/CJFDTOTAL-SHJT201011010.htm Wang Li-Jun, Wang Jing-Chun, Chen Wei-Dong. Path planning and navigation for intelligent wheelchair in dynamic environments. Journal of Shanghai Jiaotong University, 2010, 44(11):1524-1528( http://www.cnki.com.cn/Article/CJFDTOTAL-SHJT201011010.htm
[37]	张毅, 张辉, 罗元, 胡豁生.采用Emotiv感知的智能轮椅运动控制的研究.重庆邮电大学学报(自然科学版), 2012, 24(3):358-362) http://www.cnki.com.cn/Article/CJFDTOTAL-CASH201203019.htm Zhang Yi, Zhang Hui, Luo Yuan, Hu Huo-Sheng. Motion control for intelligent wheelchair using Emotiv perception. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 2012, 24(3):358-362 http://www.cnki.com.cn/Article/CJFDTOTAL-CASH201203019.htm
[38]	张毅, 张姣, 罗元.基于手势跟踪的智能轮椅控制系统.重庆邮电大学学报(自然科学版), 2011, 23(6):741-745) http://www.cnki.com.cn/Article/CJFDTOTAL-CASH201106019.htm Zhang Yi, Zhang Jiao, Luo Yuan. Intelligent wheelchair control system based on hand tracking. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 2011, 23(6):741-745( http://www.cnki.com.cn/Article/CJFDTOTAL-CASH201106019.htm
[39]	罗元, 谢彧, 张毅.基于Kinect传感器的智能轮椅手势控制系统的设计与实现.机器人, 2012, 34(1):110-113, 119) doi: 10.3724/SPJ.1218.2012.00110 Luo Yuan, Xie Yu, Zhang Yi. Design and implementation of a gesture-driven system for intelligent wheelchairs based on the Kinect sensor. Robot, 2012, 34(1):110-113, 119 doi: 10.3724/SPJ.1218.2012.00110
[40]	Dubowsky S, Genot F, Godding S, Kozono H, Skwersky A, Yu H Y, Yu L S. PAMM-a robotic aid to the elderly for mobility assistance and monitoring:a "helping-hand" for the elderly. In:Proceedings of the 2000 IEEE International Conference on Robotics and Automation. San Francisco, CA:IEEE, 2000, 1:570-576
[41]	Bogue R. Exoskeletons and robotic prosthetics:a review of recent developments. Industrial Robot:An International Journal, 2009, 36(5):421-427 doi: 10.1108/01439910910980141
[42]	Nam Y, Koo B, Cichocki A, Choi S. GOM-face:GKP, EOG, and EMG-based multimodal interface with application to humanoid robot control. IEEE Transactions on Biomedical Engineering, 2014, 61(2):453-462 doi: 10.1109/TBME.2013.2280900
[43]	Phinyomark A, Phukpattaranont P, Limsakul C. Feature reduction and selection for EMG signal classification. Expert Systems with Applications, 2012, 39(8):7420-7431 doi: 10.1016/j.eswa.2012.01.102
[44]	Chan A D C, Englehart K B. Continuous myoelectric control for powered prostheses using hidden Markov models. IEEE Transactions on Biomedical Engineering, 2005, 52(1):121-124 doi: 10.1109/TBME.2004.836492
[45]	Chu J U, Moon I, Lee Y J, Kim S K, Mun M S. A supervised feature-projection-based real-time EMG pattern recognition for multifunction myoelectric hand control. IEEE/ASME Transactions on Mechatronics, 2007, 12(3):282-290 doi: 10.1109/TMECH.2007.897262
[46]	Cavallaro E E, Rosen J, Perry J C, Burns S. Real-time myoprocessors for a neural controlled powered exoskeleton arm. IEEE Transactions on Biomedical Engineering, 2006, 53(11):2387-2396 doi: 10.1109/TBME.2006.880883
[47]	Artemiadis P K, Kyriakopoulos K J. EMG-based control of a robot arm using low-dimensional embeddings. IEEE Transactions on Robotics, 2010, 26(2):393-398 doi: 10.1109/TRO.2009.2039378
[48]	Artemiadis P K, Kyriakopoulos K J. An EMG-based robot control scheme robust to time-varying EMG signal features. IEEE Transactions on Information Technology in Biomedicine, 2010, 14(3):582-588 doi: 10.1109/TITB.2010.2040832
[49]	Ajoudani A, Tsagarakis N, Bicchi A. Tele-impedance:teleoperation with impedance regulation using a body-machine interface. The International Journal of Robotics Research, 2012, 31(13):1642-1656 doi: 10.1177/0278364912464668
[50]	Karavas N, Ajoudani A, Tsagarakis N, Saglia J, Bicchi A, Caldwell D. Tele-impedance based assistive control for a compliant knee exoskeleton. Robotics and Autonomous Systems, 2015, 73:78-90 doi: 10.1016/j.robot.2014.09.027
[51]	Hochberg L R, Bacher D, Jarosiewicz B, Masse N Y, Simeral J D, Vogel J, Haddadin S, Liu J, Cash S S, Van Der Smagt P, Donoghue J P. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature, 2012, 485(7398):372-375 doi: 10.1038/nature11076
[52]	Sadeghian E B, Moradi M H. Continuous detection of motor imagery in a four-class asynchronous BCI. In:Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS). Lyon, France:IEEE, 2007. 3241-3244
[53]	Iturrate I, Antelis J M, Kubler A, Minguez J. A noninvasive brain-actuated wheelchair based on a P300 neurophysiological protocol and automated navigation. IEEE Transactions on Robotics, 2009, 25(3):614-627 doi: 10.1109/TRO.2009.2020347
[54]	Witkowski M, Cortese M, Cempini M, Mellinger J, Vitiello N, Soekadar S R. Enhancing brain-machine interface (BMI) control of a hand exoskeleton using electrooculography (EOG). Journal of NeuroEngineering and Rehabilitation, 2014, 11:165 doi: 10.1186/1743-0003-11-165
[55]	Wang H T, Li Y Q, Long J Y, Yu T Y, Gu Z H. An asynchronous wheelchair control by hybrid EEG-EOG brain-computer interface. Cognitive Neurodynamics, 2014, 8(5):399-409 doi: 10.1007/s11571-014-9296-y
[56]	Tello R J, Bissoli A L C, Ferrara F, Müller S, Ferreira A, Bastos-Filho T F. Development of a human machine interface for control of robotic wheelchair and smart environment. In:Preprints of the 11th IFAC Symposium on Robot Control (SYROCO). Salvador, BA, Brazil:IFAC, 2015.
[57]	Ma J X, Zhang Y, Cichocki A, Matsuno F. A novel EOG/EEG hybrid human-machine interface adopting eye movements and ERPs:application to robot control. IEEE Transactions on Biomedical Engineering, 2015, 62(3):876-889 doi: 10.1109/TBME.2014.2369483
[58]	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) http://www.aas.net.cn/CN/abstract/abstract18514.shtml 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( http://www.aas.net.cn/CN/abstract/abstract18514.shtml
[59]	Jones C L, Wang F R, Morrison R, Sarkar N, Kamper D G. Design and development of the cable actuated finger exoskeleton for hand rehabilitation following stroke. IEEE/ASME Transactions on Mechatronics, 2014, 19(1):131-140 doi: 10.1109/TMECH.2012.2224359
[60]	Chiri A, Vitiello N, Giovacchini F, Roccella S, Vecchi F, Carrozza M C. Mechatronic design and characterization of the index finger module of a hand exoskeleton for post-stroke rehabilitation. IEEE/ASME Transactions on Mechatronics, 2012, 17(5):884-893 doi: 10.1109/TMECH.2011.2144614
[61]	Ali A M M, Yusof Z M, Kushairy A K, Zaharah F, Ismail A. Development of smart glove system for therapy treatment. In:Proceedings of the 2015 International Conference on BioSignal Analysis, Processing and Systems. Kuala Lumpur, Malaysia:IEEE, 2015. 67-71
[62]	Jeong S K, Kim K S, Kim S. Development of a robotic finger with an active dual-mode twisting actuation and a miniature tendon tension sensor. In:Proceedings of the 2016 IEEE International Conference on Advanced Intelligent Mechatronics. Banff, AB, Canada:IEEE, 2016. 1-6
[63]	Cempini M, Cortese M, Vitiello N. A powered finger-thumb wearable hand exoskeleton with self-aligning joint axes. IEEE/ASME Transactions on Mechatronics, 2015, 20(2):705-716 doi: 10.1109/TMECH.2014.2315528
[64]	Iqbal J, Tsagarakis N G, Caldwell D G. Human hand compatible underactuated exoskeleton robotic system. Electronics Letters, 2014, 50(7):494-496 doi: 10.1049/el.2014.0508
[65]	Iqbal J, Tsagarakis NG, Caldwell D G. Four-fingered lightweight exoskeleton robotic device accommodating different hand sizes. Electronics Letters, 2015, 51(12):888-890 doi: 10.1049/el.2015.0850
[66]	Zanotto D, Stegall P, Agrawal S K. Adaptive assist-as-needed controller to improve gait symmetry in robot-assisted gait training. In:Proceedings of the 2014 IEEE International Conference on Robotics and Automation. Hong Kong, China:IEEE, 2014. 724-729
[67]	Agarwal P, Deshpande A D. Impedance and force-field control of the index finger module of a hand exoskeleton for rehabilitation. In:Proceedings of the 2015 IEEE International Conference on Rehabilitation Robotics. Singapore:IEEE, 2015. 85-90
[68]	Tang Z J, Sugano S, Iwata H. A finger exoskeleton for rehabilitation and brain image study. In:Proceedings of the 2013 IEEE International Conference on Rehabilitation Robotics. Seattle, USA:IEEE, 2013. 1-6
[69]	Dalli D, Saliba M A. The university of malta minimal anthropomorphic robot (UM-MAR) hand II. In:Proceedings of the 2016 IEEE International Conference on Advanced Intelligent Mechatronics. Banff, Canada:IEEE, 2016. 371-276
[70]	Li Q L, Song Y, Hou Z G. Estimation of lower limb periodic motions from sEMG using least squares support vector regression. Neural Processing Letters, 2015, 41(3):371-388 doi: 10.1007/s11063-014-9391-4
[71]	Bao G J, Li K, Xu S, Huang P X, Wu L, Yang Q H. Motion identification based on sEMG for flexible pneumatic hand rehabilitator. Industrial Robot:An international Journal, 2015, 42(1):25-35 doi: 10.1108/IR-08-2014-0376
[72]	Ding Q C, Han J D, Zhao X G, Chen Y. Missing-data classification with the extended full-dimensional Gaussian mixture model:applications to EMG-based motion recognition. IEEE Transactions on Industrial Electronics, 2015, 62(8):4994-5005 doi: 10.1109/TIE.2015.2403797
[73]	Adewuyi A A, Hargrove L J, Kuiken T A. An analysis of intrinsic and extrinsic hand muscle EMG for improved pattern recognition control. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2016, 24(4):485-494 doi: 10.1109/TNSRE.2015.2424371
[74]	Lee J, Kim M, Kim K. A robust control method of multi-DOF power-assistant robots for unknown external perturbation using sEMG signals. In:Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems. Hamburg, Germany:IEEE, 2015. 1045-1051
[75]	Guo S X, Zhang F, Wei W, Zhao F, Wang Y L. Kinematic analysis of a novel exoskeleton finger rehabilitation robot for stroke patients. In:Proceedings of the 2014 IEEE International Conference on Mechatronics and Automation. Tianjin, China:IEEE, 2014. 924-929
[76]	Goldfarb M, Lawson B E, Shultz A H. Realizing the promise of robotic leg prostheses. Science Translational Medicine, 2013, 5(210):5302-5314 https://www.researchgate.net/publication/258336585_Realizing_the_Promise_of_Robotic_Leg_Prostheses
[77]	Kazerooni H, Racine J L, Huang L H, Steger R. On the control of the berkeley lower extremity exoskeleton (BLEEX). In:Proceedings of the 2005 IEEE International Conference on Robotics and Automation (ICRA). Seattle, WA, USA:IEEE, 2005. 4353-4360
[78]	Zoss A B, Kazerooni H, Chu A. Biomechanical design of the Berkeley lower extremity exoskeleton (BLEEX). IEEE/ASME Transactions on Mechatronics, 2006, 11(2):128-138 doi: 10.1109/TMECH.2006.871087
[79]	Kazerooni H, Steger R. The Berkeley lower extremity exoskeleton. Journal of Dynamic Systems, Measurement, and Control, 2006, 128(1):14-25 doi: 10.1115/1.2168164
[80]	Sankai Y. HAL:hybrid assistive limb based on cybernics. Robotics Research. Berlin Heidelberg:Springer, 2011. 25-34
[81]	Suzuki K, Mito G, Kawamoto H, Hasegawa Y, Sankai Y. Intention-based walking support for paraplegia patients with robot suit HAL. Advanced Robotics, 2007, 21(12):1441-1469
[82]	Tsukahara A, Kawanishi R, Hasegawa Y, Sankai Y. Sit-to-stand and stand-to-sit transfer support for complete paraplegic patients with robot suit HAL. Advanced Robotics, 2010, 24(11):1615-1638 doi: 10.1163/016918610X512622
[83]	http://www.cyberdyne.jp/english/products/HAL/index.html
[84]	Yamamoto K, Hyodo K, Ishii M, Matsuo T. Development of power assisting suit for assisting nurse labor. JSME International Journal Series C Mechanical Systems, Machine Elements and Manufacturing, 2002, 45(3):703-711 doi: 10.1299/jsmec.45.703
[85]	Yamamoto K, Ishii M, Hyodo K, Yoshimitsu T, Matsuo T. Development of power assisting suit (miniaturization of supply system to realize wearable suit). JSME International Journal Series C Mechanical Systems, Machine Elements and Manufacturing, 2003, 46(3):923-930 doi: 10.1299/jsmec.46.923
[86]	Wehner M, Quinlivan B, Aubin P M, Martinez-Villalpando E, Baumann M, Stirling L, Holt K, Wood R, Walsh C. A lightweight soft exosuit for gait assistance. In:Proceedings of the 2013 IEEE International Conference on Robotics and Automation (ICRA). Karlsruhe, Germany:IEEE, 2013. 3362-3369
[87]	Asbeck A T, Dyer R J, Larusson A F, Walsh C J. Biologically-inspired soft exosuit. In:Proceedings of the 2013 IEEE international conference on Rehabilitation robotics (ICORR). Seattle, WA:IEEE, 2013. 1-8
[88]	Asbeck A T, De Rossi S M M, Galiana I, Ding Y, Walsh C J. Stronger, smarter, softer:next-generation wearable robots. IEEE Robotics & Automation Magazine, 2014, 21(4):22-33 https://www.researchgate.net/publication/273396915_Stronger_Smarter_Softer_Next-Generation_Wearable_Robots
[89]	Asbeck A T, De Rossi S M M, Holt K G, Walsh C J. A biologically inspired soft exosuit for walking assistance. The International Journal of Robotics Research, 2015, 34(6):744-762 doi: 10.1177/0278364914562476
[90]	http://rewalk.com/about-products-2/
[91]	http://bleex.me.berkeley.edu/research/exoskeleton/bleex/
[92]	https://www.hocoma.com/world/en/products/lokomat/
[93]	Jezernik S, Colombo G, Keller T, Frueh H, Morari M. Robotic orthosis lokomat:a rehabilitation and research tool. Neuromodulation:Technology at the Neural Interface, 2003, 6(2):108-115 doi: 10.1046/j.1525-1403.2003.03017.x
[94]	Banala S K, Agrawal S K, Kim S H, Scholz J P. Novel gait adaptation and neuromotor training results using an active leg exoskeleton. IEEE/ASME Transactions on Mechatronics, 2010, 15(2):216-225 doi: 10.1109/TMECH.2010.2041245
[95]	Giovacchini F, Vannetti F, Fantozzi M, Cempini M, Cortese M, Parri A, Yan T F, Lefeber D, Vitiello N. A light-weight active orthosis for hip movement assistance. Robotics and Autonomous Systems, 2014, 73:123-134 https://www.researchgate.net/publication/265689247_A_light-weight_active_orthosis_for_hip_movement_assistance
[96]	Zajac F E, Neptune R R, Kautz S A. Biomechanics and muscle coordination of human walking:part II:lessons from dynamical simulations and clinical implications. Gait & Posture, 2003, 17(1):1-17 http://www.citeulike.org/user/ALHALL20/article/3008921
[97]	Blaya J A, Herr H. Adaptive control of a variable-impedance ankle-foot orthosis to assist drop-foot gait. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2004, 12(1):24-31 doi: 10.1109/TNSRE.2003.823266
[98]	Park Y L, Chen B R, Young D, Stirling L, Wood R J, Goldeld E, Nagpal R. Bio-inspired active soft orthotic device for ankle foot pathologies. In:Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). San Francisco, CA:IEEE, 2011. 4488-4495
[99]	Collins S H, Wiggin M B, Sawicki G S. Reducing the energy cost of human walking using an unpowered exoskeleton. Nature, 2015, 522(7555):212-215 doi: 10.1038/nature14288
[100]	Au S K, Weber J, Herr H. Powered ankle-foot prosthesis improves walking metabolic economy. IEEE Transactions on Robotics, 2009, 25(1):51-66 doi: 10.1109/TRO.2008.2008747
[101]	Sup F, Varol H, Mitchell J, Withrow T J, Goldfarb M. Preliminary evaluations of a self-contained anthropomorphic transfemoral prosthesis. IEEE/ASME Transactions on Mechatronics, 2009, 14(6):667-676 doi: 10.1109/TMECH.2009.2032688
[102]	Hitt J, Sugar T, Holgate M, Bellmann R, Hollander K. Robotic transtibial prosthesis with biomechanical energy regeneration. Industrial Robot:An International Journal, 2009, 36(5):441-447 doi: 10.1108/01439910910980169
[103]	Cherelle P, Grosu V, Matthys A, Vanderborght B, Lefeber D. Design and validation of the ankle mimicking prosthetic (AMP-) foot 2.0. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2014, 22(1):138-148
[104]	Wang Q N, Yuan K B, Zhu J Y, Wang L. Walk the walk:a lightweight active transtibial prosthesis. IEEE Robotics & Automation Magazine, 2015, 22(4):80-89 https://www.researchgate.net/publication/281618143_Walk_the_Walk_A_Lightweight_Active_Transtibial_Prosthesis
[105]	Varol H A, Sup F, Goldfarb M. Multiclass real-time intent recognition of a powered lower limb prosthesis. IEEE Transactions on Biomedical Engineering, 2010, 57(3):542-551 doi: 10.1109/TBME.2009.2034734
[106]	Huang H, Zhang F, Hargrove L J, Dou Z, Rogers D R, Englehart K B. Continuous locomotion-mode identification for prosthetic legs based on neuromuscular-mechanical fusion. IEEE Transactions on Biomedical Engineering, 2011, 58(10):2867-2875 doi: 10.1109/TBME.2011.2161671
[107]	Hargrove L J, Simon A M, Young A J, Lipschutz R D, Finucane S B, Smith D G, Kuiken T A. Robotic leg control with EMG decoding in an amputee with nerve transfers. New England Journal of Medicine, 2013, 369(13):1237-1242 doi: 10.1056/NEJMoa1300126
[108]	Zheng E H, Wang L, Wei K L, Wang Q N. A noncontact capacitive sensing system for recognizing locomotion modes of transtibial amputees. IEEE Transactions on Biomedical Engineering, 2014, 61(12):2911-2920 doi: 10.1109/TBME.2014.2334316
[109]	Chapin J K, Moxon K A, Markowitz R S, Nicolelis M A. Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex. Nature Neuroscience, 1999, 2(7):664-670 doi: 10.1038/10223
[110]	Cavanagh P R, Komi P V. Electromechanical delay in human skeletal muscle under concentric and eccentric contractions. European Journal of Applied Physiology and Occupational Physiology, 1979, 42(3):159-163 doi: 10.1007/BF00431022
[111]	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 doi: 10.1016/j.jelekin.2004.04.004
[112]	Norman R W, Komi P V. Electromechanical delay in skeletal muscle under normal movement conditions. Acta Physiologica Scandinavica, 1979, 106(3):241-248 doi: 10.1111/apha.1979.106.issue-3
[113]	Englehart K, Hudgin B, Parker P. A wavelet-based continuous classification scheme for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering, 2001, 48(3):302-311 doi: 10.1109/10.914793
[114]	Englehart K, Hudgins B. A robust, real-time control scheme for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering, 2003, 50(7):848-854 doi: 10.1109/TBME.2003.813539
[115]	Kuiken T A, Dumanian G A, Lipschutz R D, Miller L A, Stubblefield K A. The use of targeted muscle reinnervation for improved myoelectric prosthesis control in a bilateral shoulder disarticulation amputee. Prosthetics and Orthotics International, 2004, 28(3):245-253 doi: 10.3109/03093640409167756?tab=permissions&scroll=top
[116]	Kuiken T A, Miller L A, Lipschutz R D, Lock B A, Stubblefield K, Marasco P D, Zhou P, Dumanian G A. Targeted reinnervation for enhanced prosthetic arm function in a woman with a proximal amputation:a case study. The Lancet, 2007, 369(9559):371-380 doi: 10.1016/S0140-6736(07)60193-7
[117]	Huang H, Kuiken T A, Lipschutz R D. A strategy for identifying locomotion modes using surface electromyography. IEEE Transactions on Biomedical Engineering, 2009, 56(1):65-72 doi: 10.1109/TBME.2008.2003293
[118]	Sensinger J W, Lock B A, Kuiken T A. Adaptive pattern recognition of myoelectric signals:Exploration of conceptual framework and practical algorithms. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2009, 17(3):270-278 doi: 10.1109/TNSRE.2009.2023282
[119]	Young A J, Simon A M, Hargrove L J. A training method for locomotion mode prediction using powered lower limb prostheses. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2014, 22(3):671-677 doi: 10.1109/TNSRE.2013.2285101
[120]	Chen B J, Zheng E H, Fan X D, Liang T, Wang Q N, Wei K L, Wang L. Locomotion mode classification using a wearable capacitive sensing system. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2013, 21(5):744-755 doi: 10.1109/TNSRE.2013.2262952
[121]	Yan T F, Cempini M, Oddo C M, Vitiello N. Review of assistive strategies in powered lower-limb orthoses and exoskeletons. Robotics and Autonomous Systems, 2015, 64:120-136 doi: 10.1016/j.robot.2014.09.032
[122]	Van Damme M, Beyl P, Vanderborght B, Versluys R, Van Ham R, Vanderniepen I, Daerden F, Lefeber D. The safety of a robot actuated by pneumatic muscles-a case study. International Journal of Social Robotics, 2010, 2(3):289-303 doi: 10.1007/s12369-009-0042-2
[123]	De Santis A, Siciliano B, De Luca A, Bicchi A. An atlas of physical human-robot interaction. Mechanism and Machine Theory, 2008, 43(3):253-270 doi: 10.1016/j.mechmachtheory.2007.03.003
[124]	Kikuuwe R, Yasukouchi S, Fujimoto H, Yamamoto M. Proxy-based sliding mode control:a safer extension of PID position control. IEEE Transactions on Robotics, 2010, 26(4):670-683 doi: 10.1109/TRO.2010.2051188
[125]	Van Damme M, Vanderborght B, Verrelst B, Van Ham R, Daerden F, Lefeber D. Proxy-based sliding mode control of a planar pneumatic manipulator. The International Journal of Robotics Research, 2009, 28(2):266-284 doi: 10.1177/0278364908095842
[126]	Beyl P, Van Damme M, Van Ham R, Vanderborght B, Lefeber D. Pleated pneumatic artificial muscle-based actuator system as a torque source for compliant lower limb exoskeletons. IEEE/ASME Transactions on Mechatronics, 2014, 19(3):1046-1056 doi: 10.1109/TMECH.2013.2268942
[127]	Chen G, Zhou Z H, Vanderborght B, Wang N H, Wang Q N. Proxy-based sliding mode control of a robotic ankle-foot system for post-stroke rehabilitation. Advanced Robotics, 2016, 30(15):992-1003 doi: 10.1080/01691864.2016.1176601

施引文献

资源附件(0)

访问统计

计量

文章访问数: 3399
HTML全文浏览量: 1488
PDF下载量: 3010
被引次数: 0

姓名
邮箱
手机号码
标题
留言内容
验证码

留言板

康复机器人与智能辅助系统的研究进展

doi: 10.16383/j.aas.2016.y000006

通讯作者:
侯增广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研究员.主要研究方向为机器人与智能系统, 康复机器人与微创介入手术机器人. E-mail:zengguang.hou@ia.ac.cn

计量

Recent Advances in Rehabilitation Robots and Intelligent Assistance Systems

Corresponding author: HOU Zeng-Guang Professor at the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences. His research interest covers intelligent robotic systems, rehabilitation and surgery robots

计量

目录

留言板

康复机器人与智能辅助系统的研究进展

doi: 10.16383/j.aas.2016.y000006

通讯作者: 侯增广 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研究员.主要研究方向为机器人与智能系统, 康复机器人与微创介入手术机器人. E-mail:zengguang.hou@ia.ac.cn

计量

出版历程

Recent Advances in Rehabilitation Robots and Intelligent Assistance Systems

Corresponding author: HOU Zeng-Guang Professor at the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences. His research interest covers intelligent robotic systems, rehabilitation and surgery robots

计量

出版历程

目录

通讯作者:
侯增广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研究员.主要研究方向为机器人与智能系统, 康复机器人与微创介入手术机器人. E-mail:zengguang.hou@ia.ac.cn