[1] LUAN C, LIU M. Comparison of prevalence of physical disabilities in year 2006 and 1987, in China [J]. Chinese Journal of Epidemiology, 2008, 29(7): 639-642(in Chinese).
[2] HE B R, ZHENG B L. General research situation of standardized treatment and repair mechanism of spinal cord injury in China [J]. Chinese Journal of Trauma, 2020(4): 289-292 (in Chinese).
[3] China Stroke Data Center. Report on stroke prevention and treatment in China in 2016. [EB/OL]. [2021-01-27]. https://service.chinasdc.cn/ healthcare/ topicanalysis report.
[4] Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat. 2019 revision of world population prospects [EB/OL]. [2021-01-27]. https://population.un.org/wpp/.
[5] The State Council of the People’s Republic of China. Opinions of the state council on the establishment of [EB/OL]. (2018-07-10). http:// www.gov.cn/ zhengce/ content/ 2018-07/ 10/ content 5305296.htm (in Chinese).
[6] ZHAO M, HALEY D R, NOLIN J M, et al. Utilization, cost, payment, and patient satisfaction of rehabilitative services in Shandong, China [J]. Health Policy, 2009, 93(1): 21-26.
[7] XIAO Y, ZHAO K, MA Z X, et al. Integrated medical rehabilitation delivery in China [J]. Chronic Diseases and Translational Medicine, 2017, 3(2): 75-81.
[8] NOROUZI-GHEIDARI N, ARCHAMBAULT P S, FUNG J. Effects of robot-assisted therapy on stroke
rehabilitation in upper limbs: Systematic review and meta-analysis of the literature [J]. Journal of Rehabilitation Research and Development, 2012, 49(4): 479-496.
[9]DUNDAR U, TOKTAS H, SOLAK O, et al. A comparative study of conventional physiotherapy versus robotic training combined with physiotherapy in patients with stroke [J]. Topics in Stroke Rehabilitation, 2014, 21(6): 453-461.
[10]LERNER Z F, DAMIANO D L, BULEA T C. A lowerextremity exoskeleton improves knee extension in children with crouch gait from cerebral palsy [J]. Science Translational Medicine, 2017, 9(404): eaam9145.
[11]WHITE H, HAYES S, WHITE M. The effect of using a powered exoskeleton training programme on joint range of motion on spinal injured individuals: A pilot study [J]. International Journal of Physical Therapy & Rehabilitation, 2015, 1(1): 102.
[12]WAN D Q, XU Y M, BAI Y H, et al. Application of lower limb exoskeletons rehabilitation robots in rehabilitation treatment of activity limited knee joint [J]. Chinese Journal of Tissue Engineering Research, 2012, 16(4): 597-600 (in Chinese).
[13]The State Council of the People’s Republic of China. Opinions of the state council on accelerating the development of rehabilitation assistive devices industry [EB/OL]. (2016-10-27). http://www.gov.cn/ zhengce/content/2016-10/27/content 5125001.htm (in Chinese).
[14]ESQUENAZI A, TALATY M, JAYARAMAN A. Powered exoskeletons for walking assistance in persons with central nervous system injuries: A narrative review [J]. PM &R, 2017, 9(1): 46-62.
[15]EKSOBIONICS. Ekso GT Robotic exoskeleton cleared by FDA for use with stroke and spinal cord injury patients [EB/OL]. [2021-01-27]. http://ir.eksobionics.com/ press-releases/ detail/570/ ekso-gt-roboticexoskeletoncleared-by-fda-for-use-with.
[16]INDEGO. Introduction to indego personal [EB/OL]. [2021-01-27]. http://www.indego.com/ Indego/ ImageDownload?staticfile=/ Indego/ en/ Indego% 20Personal/ Indego% 20Personal% 20Data% 20Sheet.pdf.
[17]CYBERDYNE. HAL for medical use (lower limb type) [EB/OL]. [2021-01-27]. http:// www.cyberdyne.jp/ english/ products/ LowerLimb medical.html.
[18]WANG T, LEI J, WEI H, et al. Introduction of robot series standards – research progress of modular design of service robots and international standards [J]. Robot Technique and Application, 2014(4): 10-14 (in Chinese).
[19]MEIJNEKE C, WANG S Q, SLUITER V, et al. Introducing a modular, personalized exoskeleton for ankle and knee support of individuals with a spinal cord injury [M]//Wearable robotics: Challenges and trends. Cham: Springer, 2016: 169-173.
[20]CHEN H, YIN Y H. Modular design method for exoskeleton robot [J]. Machinery & Electronics, 2013, 31(6): 62-65 (in Chinese).
[21]DOS SANTOS W M, NOGUEIRA S L, DE OLIVEIRA G C, et al. Design and evaluation of a modular lower limb exoskeleton for rehabilitation [C]//2017 International Conference on Rehabilitation Robotics. London, UK: IEEE, 2017: 447-451.
[22]BARTENBACH V, GORT M, RIENER R. Concept and design of a modular lower limb exoskeleton [C]//2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics. Singapore: IEEE, 2016: 649-654.
[23]SOUZA R S, SANFILIPPO F, SILVA J R, et al. Modular exoskeleton design: Requirement engineering with KAOS [C]//2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics. Singapore: IEEE, 2016: 978-983.
[24]MIGLIORE S A, BROWN E A, DEWEERTH S P. Biologically inspired joint stiffness control [C]//2005 IEEE International Conference on Robotics and Automation. Barcelona, Spain: IEEE, 2005: 4508-4513.
[25]VANDERBORGHT B, VERRELST B, HAM R, et al. Development of a compliance controller to reduce energy consumption for bipedal robots [J]. Autonomous Robots, 2008, 24(4): 419-434.
[26]JAFARI A, TSAGARAKIS N G, CALDWELL D G. A novel intrinsically energy efficient actuator with adjustable stiffness (AwAS) [J]. IEEE/ASME Transactions on Mechatronics, 2013, 18(1): 355-365.
[27]VENEMAN J F, EKKELENKAMP R, KRUIDHOF R, et al. A series elastic- and bowden-cable-based actuation system for use as torque actuator in exoskeletontype robots [J]. The International Journal of Robotics Research, 2006, 25(3): 261-281.
[28]ZHANG C, LIU G F, LI C L, et al. Development of a lower limb rehabilitation exoskeleton based on real-time gait detection and gait tracking [J]. Advances in Mechanical Engineering, 2016, 8(1): 168781401562798.
[29]RAHMAN S M M, IKEURA R. A novel variable impedance compact compliant ankle robot for overground gait rehabilitation and assistance [J]. Procedia Engineering, 2012, 41: 522-531.
[30]HAN Y L, WU Z Y, XU Y X, et al. The knee exoskeleton mechanical leg based on multi-modal elastic actuator [J]. Robot, 2017, 39(4): 498-504 (in Chinese).
[31]STEGER R, KIM S H, KAZEROONI H. Control scheme and networked control architecture for the Berkeley lower extremity exoskeleton (BLEEX) [C]//2006 IEEE International Conference on Robotics and Automation. Orlando, FL, USA: IEEE, 2006: 3469-3476.
[32]LEE S, SANKAI Y. Power assist control for walking aid with HAL-3 based on EMG and impedance adjustment around knee joint [C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Lausanne, Switzerland: IEEE, 2002: 1499-1504.
[33]HAYASHI T, KAWAMOTO H, SANKAI Y. Control method of robot suit HAL working as operator’s muscle using biological and dynamical information [C]//2005 IEEE/RSJ International Conference on Intelligent Robots and Systems. Edmonton, Canada: IEEE, 2005: 3063-3068.
[34]KARAVAS N, AJOUDANI A, TSAGARAKIS N, et al. Tele-impedance based assistive control for a compliant knee exoskeleton [J]. Robotics and Autonomous Systems, 2015, 73: 78-90.
[35]HUO W G, MOHAMMED S, AMIRAT Y, et al. Active Impedance Control of a lower limb exoskeleton to assist sit-to-stand movement [C]//2016 IEEE International Conference on Robotics and Automation. Stockholm: IEEE, 2016: 3530-3536.
[36]XU G Z, SONG A G, LI H J. Fuzzy-based adaptive impedance control for upper-limb rehabilitation robot [J]. Journal of Southeast University (Natural Science Edition), 2009, 39(1): 156-160 (in Chinese).
[37]BEYL P, VAN DAMME M, CHERELLE P, et al. Safe and compliant guidance in robot-assisted gait rehabilitation using proxy-based sliding mode control [C]//2009 IEEE International Conference on Rehabilitation Robotics. Kyoto, Japan: IEEE, 2009: 277-282.
[38]TALATY M, ESQUENAZI A, BRICENO J E. Differentiating ability in users of the ReWalkTM powered exoskeleton: An analysis of walking kinematics [C]//2013 IEEE 13th International Conference on Rehabilitation Robotics. Seattle, WA, USA: IEEE, 2013: 1-5.
[39]WANG P, LOW K H, MCGREGOR A H. A subjectbased motion generation model with adjustable walking pattern for a gait robotic trainer: NaTUre-gaits [C]//2011 IEEE/RSJ International Conference on Intelligent Robots and Systems. San Francisco, CA, USA: IEEE, 2011: 1743-1748.
[40]DEHGHANI R, FATTAH A, ABEDI E. Cyclic gait planning and control of a five-link biped robot with four actuators during single support and double support phases [J]. Multibody System Dynamics, 2015, 33(4): 389-411.
[41]UGURLU B, OSHIMA H, NARIKIYO T. Lower body exoskeleton-supported compliant bipedal walking for paraplegics: How to reduce upper body effort? [C]//2014 IEEE International Conference on Robotics and Automation. Hong Kong, China: IEEE, 2014: 1354-1360.
[42]KAGAWA T, ISHIKAWA H, KATO T, et al. Optimization-based motion planning in joint space for walking assistance with wearable robot [J]. IEEE Transactions on Robotics, 2015, 31(2): 415-424.
[43]LIU G L, HABIB M K, WATANABE K, et al. Central pattern generators based on Matsuoka oscillators for the locomotion of biped robots [J]. Artificial Life and Robotics, 2008, 12(1/2): 264-269.
[44]LIM H B, LUU T P, HOON K H, et al. Natural gait parameters prediction for gait rehabilitation via artificial neural network [C]//2010 IEEE/RSJ International Conference on Intelligent Robots and Systems. Taipei, China: IEEE, 2010: 5398-5403.
[45]LI J. Research on gait strategies and gait planning of exoskeleton for hemiplegia patients [D]. Shanghai, China: Shanghai University, 2017 (in Chinese).
[46]DUSCHAU-WICKE A, VON ZITZEWITZ J, CAPREZ A, et al. Path control: A method for patient-cooperative robot-aided gait rehabilitation [J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2010, 18(1): 38-48.
[47]DUSCHAU-WICKE A, CAPREZ A, RIENER R. Patient-cooperative control increases active participation of individuals with SCI during robot-aided gait training [J]. Journal of Neuroengineering and Rehabilitation, 2010, 7: 43.
[48]LUU T P, LOW K H, QU X D, et al. An individualspecific gait pattern prediction model based on generalized regression neural networks [J]. Gait & Posture, 2014, 39(1): 443-448.
[49]TUCKER M, NOVOSELLER E, KANN C, et al. Preference-based learning for exoskeleton gait optimization [C]//2020 IEEE International Conference on Robotics and Automation. Paris, France: IEEE, 2020: 2351-2357.
[50]LIU D X. Research on multimodal fusion-based control strategy for lower-limb exoskeleton robot [D]. Shenzhen, China: University of Chinese Academy of Science (Shenzhen Institutes of Advanced Technology), 2018 (in Chinese).
[51]CHEN J T. Research of body size adaptive gait for lower limb exoskeleton for hemiplegic rehabilitation [D]. Chengdu, China: University of Electronic Science and Technology of China, 2020 (in Chinese).
[52]ZHAO X G, TAN X W, ZHANG B. Development of soft lower extremity exoskeleton and its key technologies: A survey [J]. Robot, 2020, 42(3): 365-384 (in Chinese).
[53]DJURIC M. Automatic recognition of gait phases from accelerations of leg segments [C]//2008 9th Symposium on Neural Network Applications in Electrical Engineering. Belgrade, Serbia: IEEE, 2008: 121-124.
[54]LI Y D, HSIAO-WECKSLER E T. Gait mode recognition and control for a portable-powered ankle-foot orthosis [C]//2013 13th International Conference on Rehabilitation Robotics. Seattle, WA, USA: IEEE, 2013: 1-8.
[55]SONG P, MO X M, DENG Y P, et al. Exoskeleton gait recognition method based on the proportional normalized threshold [J]. Journal of Gun Launch & Control, 2018, 39(2): 81-85 (in Chinese).
[56]ZHAO Z Y, MA L, SUN Y K. Recognition of gait phase using plantar pressure sensors [J]. Electronic Measurement Technology, 2019, 42(13): 26-31 (in Chinese).
[57]QUINTERO H A, FARRIS R J, GOLDFARB M. A method for the autonomous control of lower limb exoskeletons for persons with paraplegia [J]. Journal of Medical Devices, 2012, 6(4): 041003.
[58]KASAOKA K, SANKAI Y. Predictive control estimating operator’s intention for stepping-up motion by exo-skeleton type power assist system HAL [C]//2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Maui, HI, USA: IEEE, 2001: 1578-1583.
[59]KAWABATA T, SATOH H, SANKAI Y. Working posture control of Robot Suit HAL for reducing structural stress [C]//2009 IEEE International Conference on Robotics and Biomimetics. Guilin, China: IEEE, 2009: 2013-2018.
[60]WANG S Q, WANG L T, MEIJNEKE C, et al. Design and control of the MINDWALKER exoskeleton [J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2015, 23(2): 277-286.
[61]DING Y, PANIZZOLO F A, SIVIY C, et al. Effect of timing of hip extension assistance during loaded walking with a soft exosuit [J]. Journal of Neuroengineering and Rehabilitation, 2016, 13(1): 87.
[62]DING Y, GALIANA I, SIVIY C, et al. IMU-based iterative control for hip extension assistance with a soft exosuit [C]//2016 IEEE International Conference on Robotics and Automation. Stockholm, Sweden: IEEE, 2016: 3501-3508.
[63]AGUIRRE-OLLINGER G. Learning muscle activation patterns via nonlinear oscillators: Application to lower-limb assistance [C]//2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. Tokyo, Japan: IEEE, 2013: 1182-1189.
[64]XI X G, WU H, ZUO J, et al. EMG fall recognition based on permutation entropy and WKFDA [J]. Journal of Shanghai Jiao Tong University, 2015, 49(11): 1685-1689 (in Chinese).
[65]LIU J, ZHOU P. A novel myoelectric pattern recognition strategy for hand function restoration after incomplete cervical spinal cord injury [J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2013, 21(1): 96-103.
[66]YANG D P, ZHAO J D, JIANG L, et al. Study on recognition of multi-mode hand gestures based on myoelectric signal [J]. Journal of Shanghai Jiao Tong University, 2009, 43(7): 1071-1075 (in Chinese).
[67]JIANG M, WANG Z L, LIU X B, et al. Research on human daily activity recognition method based on BSN and CHMMs [J]. Journal of Dalian University of Technology, 2013, 53(1): 121-126 (in Chinese).
[68]KILICARSLAN A, GROSSMAN R G, CONTRERASVIDAL J L. A robust adaptive denoising framework for real-time artifact removal in scalp EEG measurements [J]. Journal of Neural Engineering, 2016, 13(2): 026013.
[69]KILICARSLAN A, PRASAD S, GROSSMAN R G, et al. High accuracy decoding of user intentions using EEG to control a lower-body exoskeleton [C]//2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Osaka, Japan: IEEE, 2013: 5606-5609.
[70] YOUNG A J, KUIKEN T A, HARGROVE L J. Analysis of using EMG and mechanical sensors to enhance intent recognition in powered lower limb prostheses [J]. Journal of Neural Engineering, 2014, 11(5): 056021.
[71] ZHANG Y H, PRASAD S, KILICARSLAN A, et al. Multiple kernel based region importance learning for neural classification of gait states from EEG signals [J]. Frontiers in Neuroscience, 2017, 11: 170.
[72] FLEISCHER C, HOMMEL G. A human-exoskeleton interface utilizing electromyography [J]. IEEE Transactions on Robotics, 2008, 24(4): 872-882.
[73] ZHANG F, LI P F, HOU Z G, et al. sEMG-based continuous estimation of joint angles of human legs by using BP neural network [J]. Neurocomputing, 2012, 78(1): 139-148.
[74] HUANG Y C, HE Z X, LIU Y X, et al. Real-time intended knee joint motion prediction by deep-recurrent neural networks [J]. IEEE Sensors Journal, 2019, 19(23): 11503-11509.
[75] WANG F, WEI X T, QIN H. Estimation of lower limb continuous movements based on sEMG and LSTM [J]. Journal of Northeastern University (Natural Science), 2020, 41(3): 305-310 (in Chinese).
[76] DING Q C, XIONG A B, ZHAO X G, et al. A review on researches and applications of sEMG-based motion intent recognition methods [J]. Acta Automatica Sinica, 2016, 42(1): 13-25 (in Chinese).
[77] DUAN Y K,CHEN X G, GUI J, et al. Continuous kinematics prediction of lower limbs based on phase division [J]. Journal of Zhejiang University (Engineering Science), 2021, 55(1): 89-95 (in Chinese).
[78] TUKKER A, TISCHNER U. Product-services as a research field: Past, present and future. Reflections from a decade of research [J]. Journal of Cleaner Production, 2006, 14(17): 1552-1556.
[79] [80] HAASE R P, PIGOSSO D C A, MCALOONE T C. Product/service-system origins and trajectories: A systematic literature review of PSS definitions and their characteristics [J]. Procedia CIRP, 2017, 64: 157- 162.
[80] TAO J, YU S R. Developing conceptual PSS models of upper limb exoskeleton based post-stroke rehabilitation in China [J]. Procedia CIRP, 2019, 80: 750-755.
[81] TAO J, YU S R. Service design of rehabilitative exoskeleton for sustainable value creation: A case study of stroke rehabilitation in China [M]//Ecodesign and sustainability I. Singapore: Springer Singapore, 2020: 59-70.
[82] JIANG G Q. Application design and development prospect of home medical equipment [J]. China Medical Device Information, 2018, 24(3): 43-44 (in Chinese).
[83] ZHENG H R, DAVIES R, ZHOU H Y, et al. SMART project: Application of emerging information and communication technology to home-based rehabilitation for stroke patients [J]. International Journal on Disability and Human Development, 2006, 5(3): 271-276.
[84] WU F, YIN Y H. Design of remote information system for lower limb rehabilitation robots based on GSM [J]. Machinery & Electronics, 2014, 32(7): 78-80 (in Chinese).
[85] CHEN D. Research on mobile terminal module of exoskeleton condition monitoring system [D]. Shanghai, China: East China University of science and technology, 2019 (in Chinese).
[86] LI J Q. Research on exoskeleton remote rehabilitation system based on virtual reality technology [J]. Machine Design and Research, 2011, 27(4): 35-38 (in Chinese).
[87] LI Z. Design of upper limb rehabilitation exoskeleton and research on inter-cloud system [D]. Zibo, China: Shandong University of Technology, 2019 (in Chinese).