The realization of robotic neurorehabilitation in clinical: use of computational intelligence and future prospects analysis

References

• Mozaffarian D, Benjamin EJ, Go AS, et al. Heart disease and stroke statistics-2015 update a report from the american heart association. Circulation. 2015 Jan;131(4):E29–E322. [Article]
   PubMed Web of Science ®Google Scholar
• Nordin NAM, Abd Aziz NA, Aziz AFA, et al. Exploring views on long term rehabilitation for people with stroke in a developing country: findings from focus group discussions. BMC Health Serv Res. 2014 Mar 10;14:118.
   PubMed Web of Science ®Google Scholar
• He Q, Wu C, Luo H, et al. Trends in in-hospital mortality among patients with stroke in China. PLoS One. 2014;9(3):e92763.
   PubMed Web of Science ®Google Scholar
• Meng G, Huang Y, Yu Q, et al. Adopting text mining on rehabilitation therapy repositioning for stroke. Front Neuroinform. 2019;13:17.
   PubMed Web of Science ®Google Scholar
• Pothiban L, Srirat C. Association between stroke knowledge, stroke awareness, and preventive behaviors among older people: a cross-sectional study. Nurs Health Sci. 2019 Sep;21(3):399–405.
   PubMed Web of Science ®Google Scholar
• Smithard DG. Stroke in frail older people. Geriatrics (Basel). 2017 Jul 15;2:3.
   Google Scholar
• Markus TM, Tsai SY, Bollnow MR, et al. Recovery and brain reorganization after stroke in adult and aged rats. Ann Neurol. 2005 Dec;58(6):950–953.
   PubMed Web of Science ®Google Scholar
• Carmichael ST, Kathirvelu B, Schweppe CA, et al. Molecular, cellular and functional events in axonal sprouting after stroke. Exp Neurol. 2017 Jan;287(Pt 3):384–394.
   PubMedGoogle Scholar
• Leifer D. Neuronal plasticity and recovery from stroke [Article]. Neuroscientist. 1998 Mar;4(2):68–70.
   Web of Science ®Google Scholar
• Carmichael ST. Plasticity of cortical projections after stroke. Neuroscientist. 2003 Feb;9(1):64–75.
   PubMed Web of Science ®Google Scholar
• Kwakkel G, Kollen BJ, Krebs HI. Effects of robot-assisted therapy on upper limb recovery after stroke: a systematic review. Neurorehabil Neural Repair. 2008 Mar-Apr;22(2):111–121.
   PubMed Web of Science ®Google Scholar
• Prange GB, Jannink MJA, Groothuis-Oudshoorn CGM, et al. Systematic review of the effect of robot-aided therapy on recovery of the hemiparetic arm after stroke. J Rehabil Res Dev. 2006 Mar-Apr;43(2):171–183. [Review]
   PubMed Web of Science ®Google Scholar
• Houtrow AJ, Pruitt DW. Meeting the growing need for pediatric rehabilitation medicine physicians. Arch Phys Med Rehabil. 2016 Apr;97(4):501–506.
   PubMed Web of Science ®Google Scholar
• Zeng X, Zhu G, Yue L, et al. A feasibility study of SSVEP-based passive training on an ankle rehabilitation robot. J Healthc Eng. 2017;2017:6819056.
   PubMed Web of Science ®Google Scholar
• Rosati G, Gallina P, Masiero S. Design, implementation and clinical tests of a wire-based robot for neurorehabilitation. IEEE Trans Neural Syst Rehabil Eng. 2007 Dec;15(4):560–569.
   PubMed Web of Science ®Google Scholar
• Rosati G. The place of robotics in post-stroke rehabilitation. Expert Rev Med Devices. 2010 Nov;7(6):753–758.
   PubMed Web of Science ®Google Scholar
• Volpe BT, Krebs HI, Hogan N. Is robot-aided sensorimotor training in stroke rehabilitation a realistic option? Curr Opin Neurol. 2001 Dec;14(6):745–752. [Article].
   PubMed Web of Science ®Google Scholar
• Krebs HI, Hogan N, Aisen ML, et al. Robot-aided neurorehabilitation [clinical trial; controlled clinical trial;; research support, Non-U.S. Gov’t; Research Support, U.S. Gov’t, Non-P.H.S.]. IEEE Trans Rehabil Eng. 1998;6(1):75–87.
   PubMedGoogle Scholar
• Yoo DH, Kim SY. Effects of upper limb robot-assisted therapy in the rehabilitation of stroke patients. J Phys Ther Sci. 2015 Mar;27(3):677–679.
   PubMedGoogle Scholar
• Abdullah HA, Tarry C, Datta R, et al. Dynamic biomechanical model for assessing and monitoring robot-assisted upper-limb therapy. J Rehabil Res Dev. 2007;44(1):43–61. [Article; Proceedings Paper]..
   PubMedGoogle Scholar
• Chen B, Ma H, Qin LY, et al. Recent developments and challenges of lower extremity exoskeletons. J Orthop Translat. 2016 Apr;5:26–37.
   PubMed Web of Science ®Google Scholar
• Merrick MA. Secondary injury after musculoskeletal trauma: a review and update [Review]. J Athl Train. 2002 Apr-Jun;37(2):209–217.
   PubMed Web of Science ®Google Scholar
• Yang GZ, Riener R, Dario P. To integrate and to empower: robots for rehabilitation and assistance. Sci Rob. 2017 May;2(6):2. [Editorial Material].
   Web of Science ®Google Scholar
• Blum C, Roli A. Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput Surv. 2003 Sep;35(3):268–308.
   Web of Science ®Google Scholar
• Holland JH. GENETIC ALGORITHMS. SciAm. 1992 Jul;267(1):66–72. [Article].
   PubMed Web of Science ®Google Scholar
• Yang XS, Chien SF, Ting TO. Computational intelligence and metaheuristic algorithms with applications. ScientificWorldJournal. 2014;2014:425853.
   PubMedGoogle Scholar
• Lopez-Rubio E, Elizondo DA, Grootveld M, et al. Computational intelligence techniques in medicine. Comput Math Methods Med. 2015;2015:196976.
   PubMed Web of Science ®Google Scholar
• Poli P, Morone G, Rosati G, et al. Robotic technologies and rehabilitation: new tools for stroke patients’ therapy. Biomed Res Int. 2013;2013:153872.
   PubMed Web of Science ®Google Scholar
• Bamforth SE, Brookes NJ Customer-focused design methodologies and their suitability for the rehabilitation industry. In: Pham DT, Dimov SS, Ohagan V, editors. Advances In Manufacturing Technology - Xv. Westminister: Professional Engineering Publishing Ltd; 2001. English.
   Google Scholar
• Ferreira F, Chaves MEA, Oliveira VC, et al. Effectiveness of robot therapy on body function and structure in people with limited upper limb function: a systematic review and meta-analysis. PLoS One. 2018;13(7): e0200330.
   PubMed Web of Science ®Google Scholar
• Shi D, Zhang WX, Zhang W, et al. A review on lower limb rehabilitation exoskeleton robots. Chin J Mech Eng. 2019 Aug;32(1):11. [Review]
   Web of Science ®Google Scholar
• Weber LM, Stein J, Harvey RL. The use of robots in stroke rehabilitation: a narrative review. NeuroRehabilitation. 2018;43(1):99–110.
   PubMed Web of Science ®Google Scholar
• Jamwal PK, Hussain S, Xie SQ. Review on design and control aspects of ankle rehabilitation robots. Disabil Rehabil Assist Technol. 2015 Mar;10(2):93–101.
   PubMed Web of Science ®Google Scholar
• Hussain S. State-of-the-art robotic gait rehabilitation orthoses: design and control aspects. Neurorehabilitation. 2014;35(4):701–709.
   PubMed Web of Science ®Google Scholar
• Cabrera JA, Nadal F, Muñoz JP, et al. Multiobjective constrained optimal synthesis of planar mechanisms using a new evolutionary algorithm. Mech Mach Theory. 2007;42(7):791–806.
   Web of Science ®Google Scholar
• Ali M, Siarry P, Pant M. An efficient differential evolution based algorithm for solving multi-objective optimization problems. Eur J Oper Res. 2011. DOI:https://doi.org/10.1016/j.ejor.2011.09.025
   Google Scholar
• Wang C, Fang Y, Guo S. Multi-objective optimization of a parallel ankle rehabilitation robot using modified differential evolution algorithm. Chin J Mech Eng. 2015;28(4):702–715.
   Web of Science ®Google Scholar
• Yong Kwun L, Shimoyama I. A skeletal framework artificial hand actuated by pneumatic artificial muscles. Adv Rob. 2012;13(3):349–350.
   Google Scholar
• Zhao X, Zi B. Design and analysis of a pneumatic muscle driven parallel mechanism for imitating human pelvis. Proc Inst Mech Eng Part C. 2013;228(4):723–741.
   Web of Science ®Google Scholar
• Zi B, Yin G, Zhang D. Design and optimization of a hybrid-driven waist rehabilitation robot. Sensors (Basel). 2016 14;16(12):Dec.
   Web of Science ®Google Scholar
• Jamwal PK, Hussain S, Xie SQ. Three-stage design analysis and multicriteria optimization of a parallel ankle rehabilitation robot using genetic algorithm. IEEE Trans Autom Sci Eng. 2015;12(4):1433–1446.
   Web of Science ®Google Scholar
• Jamwal PK, Hussain S. Design optimization of a cable actuated parallel ankle rehabilitation robot: a fuzzy based multi-objective evolutionary approach. J Intell Fuzzy Syst. 2016;31(3):1897–1908.
   Web of Science ®Google Scholar
• Jamwal PK, Hussain S. Multicriteria design optimization of a parallel ankle rehabilitation robot: fuzzy dominated sorting evolutionary algorithm approach. IEEE Trans Syst Man Cybern Syst. 2016;46(5):589–597.
   Web of Science ®Google Scholar
• Lamine H, Laribi MA, Bennour S, et al. Design study of a cable-based gait training machine. J Bionic Eng. 2017;14(2):232–244.
   Web of Science ®Google Scholar
• Gabardi M, Solazzi M, Leonardis D, et al. Design and evaluation of a novel 5 DoF underactuated thumb-exoskeleton. IEEE Rob Autom Lett. 2018;3(3):2322–2329.
   Web of Science ®Google Scholar
• Enferadi J, Shahi A. On the position analysis of a new spherical parallel robot with orientation applications. Rob Comput Integr Manuf. 2016;37:151–161.
   Web of Science ®Google Scholar
• Laribi MA, Carbone G, Zeghloul S. On the optimal design of cable driven parallel robot with a prescribed workspace for upper limb rehabilitation tasks. J Bionic Eng. 2019;16(3):503–513.
   Web of Science ®Google Scholar
• Zhong CH, Yang XJ. Driving torque reduction in linkage mechanisms using joint compliance for robot head. Chin J Mech Eng. 2015 Sep;28(5):888–895. [Article]
   Web of Science ®Google Scholar
• Lu Y, Leinonen T Unified type synthesis of planar and spatial mechanisms by systematic linkage technique. Huang T, editor. Eleventh World Congress In Mechanism And Machine Science, Vols 1-5, Proceedings. Beijing: China Machine Press; 2004. English.
   Google Scholar
• Liu XJ, Wang QM, Wang JS. Kinematics, dynamics and dimensional synthesis of a novel 2-DoF translational manipulator. J Intell Rob Syst. 2005 Jan;41(4):205–224.
   Web of Science ®Google Scholar
• Dai JS, Delun W, Cui L. Orientation and workspace analysis of the multifingered metamorphic hand—metahand. IEEE Trans Rob. 2009;25(4):942–947.
   Web of Science ®Google Scholar
• Gao J, Li M, Allison G, et al. Optimal dimensional synthesis of a symmetrical five-bar planar upper-extremity neuromotor device. Chin J Mech Eng. 2015;28(4):684–690.
   Web of Science ®Google Scholar
• Shao Y, Xiang Z, Liu H, et al. Conceptual design and dimensional synthesis of cam-linkage mechanisms for gait rehabilitation. Mech Mach Theory. 2016;104:31–42.
   Web of Science ®Google Scholar
• McDaid AJ. Design, analysis, and multicriteria optimization of an overground pediatric robotic gait trainer. IEEE ASME Trans Mechatron. 2017;22(4):1674–1684.
   Web of Science ®Google Scholar
• Singh R, Chaudhary H, Singh AK. A novel gait-based synthesis procedure for the design of 4-bar exoskeleton with natural trajectories. J Orthop Translat. 2018 Jan;12:6–15.
   PubMed Web of Science ®Google Scholar
• Singh R, Chaudhary H, Singh AK. A novel gait-inspired four-bar lower limb exoskeleton to guide the walking movement. J Mech Med Biol. 2019;19(4):1950020.
   Web of Science ®Google Scholar
• Singh R, Chaudhary H, Singh AK. A new hybrid teaching-learning particle swarm optimization algorithm for synthesis of linkages to generate path. Sadhana-Acad Proc Eng Sci. 2017 Nov;4211:1851–1870. [Article].
   Web of Science ®Google Scholar
• Su HJ, McCarthy JM. A polynomial homotopy formulation of the inverse static analysis of planar compliant mechanisms. J Mech Des. 2006 Jul;128(4):776–786. [Article]
   Web of Science ®Google Scholar
• Jin M, Zhang X, Zhu B, et al. Spring-joint method for topology optimization of planar passive compliant mechanisms. Chin J Mech Eng. 2013;26(6):1063–1072.
   Web of Science ®Google Scholar
• Chau NL, Le HG, Dao T-P, et al. Design and optimization for a new compliant planar spring of upper limb assistive device using hybrid approach of RSM–FEM and MOGA. Arab J Sci Eng. 2019;44(9):7441–7456.
   Web of Science ®Google Scholar
• Cristian A, Green J. Patient safety and quality improvement in rehabilitation medicine. Phys Med Rehabil Clin N Am. 2012 May;23(2):221-+. [Article]
   Web of Science ®Google Scholar
• Miao Q, Zhang M, Cao J, et al. Reviewing high-level control techniques on robot-assisted upper-limb rehabilitation. Adv Rob. 2018;32(24):1253–1268.
   Web of Science ®Google Scholar
• Saglia JA, Tsagarakis NG, Dai JS, et al. Control strategies for patient-assisted training using the ankle rehabilitation robot (ARBOT). IEEE ASME Trans Mechatron. 2013;18(6):1799–1808.
   Web of Science ®Google Scholar
• Langhorne P, Bernhardt J, Kwakkel G. Stroke care 2 stroke rehabilitation. Lancet. 2011 May;377(9778):1693–1702. [Article]
   PubMed Web of Science ®Google Scholar
• Krebs HI, Palazzolo JJ, Dipietro L, et al. Rehabilitation robotics: performance-based progressive robot-assisted therapy. Auton Robot. 2003 Jul;15(1):7–20. [Article].
   Web of Science ®Google Scholar
• Gaino R, Covacic MR, Teixeira MCM, et al. Electrical stimulation tracking control for paraplegic patients using T–S fuzzy models. Fuzzy Sets Syst. 2017;314:1–23.
   Web of Science ®Google Scholar
• Koceska N, Koceski S, Zobel PB, et al. Control architecture for a lower limbs rehabilitation robot system. 2008 Ieee International Conference on Robotics And Biomimetics, Vols 1-4. New York: Ieee; 2009. English.
   Google Scholar
• Taha Z, Abdul Majeed APP, Zainal Abidin AF, et al. A hybrid active force control of a lower limb exoskeleton for gait rehabilitation. Biomed Tech (Berl). 2018 Jul 26;63(4):491–500.
   PubMed Web of Science ®Google Scholar
• Joyo MK, Raza Y, Ahmed SF, et al. Optimized proportional-integral-derivative controller for upper limb rehabilitation robot. Electronics. 2019;8:8.
   Web of Science ®Google Scholar
• Belkadi A, Oulhadj H, Touati Y, et al. On the robust PID adaptive controller for exoskeletons: A particle swarm optimization based approach. Appl Soft Comput. 2017;60:87–100.
   Web of Science ®Google Scholar
• Li W, Huang D, Zhang M, et al. Bifurcation control of a generalized VDP system driven by color-noise excitation via FOPID controller. Chaos Solitons Fractals. 2019;121:30–38.
   Web of Science ®Google Scholar
• Lakshmanaprabu SK, Najumnissa Jamal D, Sabura Banu U. Multiloop FOPID Controller Design for TITO Process Using Evolutionary Algorithm. Int J Energy Optim Eng. 2019;8(3):117–130.
   Web of Science ®Google Scholar
• Yang XS, Deb S Cuckoo Search via Levey Flights. In: Abraham A, Herrera F, Carvalho A, et al., editors. 2009 World Congress on Nature & Biologically Inspired Computing. World Congress on Nature and Biologically Inspired Computing. New York: Ieee; 2009. p. 210-+.
   Google Scholar
• Sharma R, Rana KPS, Kumar V. Performance analysis of fractional order fuzzy PID controllers applied to a robotic manipulator. Expert Syst Appl. 2014;41(9):4274–4289.
   Web of Science ®Google Scholar
• Calderón AJ, Vinagre BM, Feliu V. Fractional order control strategies for power electronic buck converters. Signal Process. 2006;86(10):2803–2819.
   Web of Science ®Google Scholar
• Ayas MS, Altas IH, Sahin E. Fractional order based trajectory tracking control of an ankle rehabilitation robot. Trans Inst Meas Control. 2016;40(2):550–564.
   Web of Science ®Google Scholar
• Li M, Meng W, Hu J, et al. Adaptive sliding mode control of functional electrical stimulation (FES) for tracking knee joint movement. 2017 10th International Symposium on Computational Intelligence and Design (ISCID), Wuhan, Hubei, China. 2017. p. 346–349.
   Google Scholar
• Azar WA, Akbarimajd A, Parvari E. Intelligent control method of a 6-DOF parallel robot used for rehabilitation treatment in lower limbs. Automatika. 2017;57(2):466–476.
   Web of Science ®Google Scholar
• Ayas MS, Altas IH. Fuzzy logic based adaptive admittance control of a redundantly actuated ankle rehabilitation robot. Control Eng Pract. 2017;59:44–54.
   Web of Science ®Google Scholar
• Silawatchananai C, Parnichkun M. Haptics control of an arm exoskeleton for virtual reality using PSO-based fixed structure H∞ control. Int J Adv Rob Syst. 2019;16(3):172988141984919.
   Web of Science ®Google Scholar
• Ott C, Lee D, Nakamura Y, et al. Motion capture based human motion recognition and imitation by direct marker control. 2008 8th Ieee-Ras International Conference on Humanoid Robots. IEEE-RAS International Conference on Humanoid Robots. New York: Ieee; 2008. p. 406–412.
   Google Scholar
• Fukuda O, Tsuji T, Kaneko M, et al. A human-assisting manipulator teleoperated by EMG signals and arm motions. IEEE Trans Rob Autom. 2003;19(2):210–222.
   Google Scholar
• Cedras C, Shah M. Motion-based recognition - a survey. Image Vis Comput. 1995 Mar;13(2):129–155. [Article].
   Web of Science ®Google Scholar
• Zhu B, Wan Z, Xu Y. The recognition of motion intention of knee joint based on piezoelectric signals. J Phys. 2019;1176(5):052024.
   Google Scholar
• Zhang L, Liu G, Han B, et al. sEMG Based Human Motion Intention Recognition. J Rob. 2019;2019:1–12.
   Web of Science ®Google Scholar
• Nas K, Gur A, Cevik R, et al. The relationship between physical impairment and disability during stroke rehabilitation: effect of cognitive status. Int J Rehabil Res. 2004 Sep;27(3):181–184. [Article]
   Web of Science ®Google Scholar
• Pasha Zanoosi AA, Naderi D, Sadeghi-Mehr M, et al. Investigation of the effects of human body stability on joint angles’ prediction. Multibody Sys Dyn. 2015;35(2):111–129.
   Web of Science ®Google Scholar
• Feng X, Ai Q, Meng W KUKA real-time control through angle estimation of wrist from sEMG with support vector regression. Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence - CSAI, Shenzhen, China ‘182018. p. 389–393.
   Google Scholar
• Buongiorno D, Barsotti M, Barone F, et al. A linear approach to optimize an EMG-driven neuromusculoskeletal model for movement intention detection in myo-control: a case study on shoulder and elbow joints. Front Neurorobot. 2018;12:74.
   PubMed Web of Science ®Google Scholar
• Peng L, Hou Z-G, Luo L, et al. An sEMG-driven neuromusculoskeletal model of upper limb for rehabilitation robot control. 2016. (2016 Ieee International Conference on Robotics And Biomimetics), Qingdao, China.
   Google Scholar
• Noughaby AG, Vossoughi GR The control of an exoskeleton and the reduction of interaction force using human intent detection by EMG signals and torque estimation. 6th Rsi International Conference on Robotics And Mechatronics. RSI International Conference on Robotics and Mechatronics ICRoM2018, Tehran, Iran. 2018 . p. 536–541.
   Google Scholar
• Zhang X, Dai J, Li X, et al. sEMG-based shoulder-elbow composite motion pattern recognition and control methods for upper limb rehabilitation robot. Assembly Autom. 2019;39(3):394–400.
   Web of Science ®Google Scholar
• Cai S, Chen Y, Huang S, et al. SVM-based classification of sEMG signals for upper-limb self-rehabilitation training. Front Neurorobot. 2019;13:31.
   PubMed Web of Science ®Google Scholar
• Oskoei MA, Hu HS. Myoelectric control systems-A survey [Review]. Biomed Signal Process Control. 2007 Oct;2(4):275–294.
   Web of Science ®Google Scholar
• Millan JD, Rupp R, Muller-Putz GR, et al. Combining Brain-computer interfaces and assistive technologies: state-of-the-art and challenges. Front Neurosci. 2010;4:161.
   Web of Science ®Google Scholar
• Ma Y, Wang Y, Yang J, et al. Big health application system based on health internet of things and big data. IEEE Access. 2017;5:7885–7897.
   Web of Science ®Google Scholar
• Alhussain T. Medical big data analysis using big data tools and methods. J Med Imaging Health Inform. 2018 May;8(4):793–795. [Article]
   Web of Science ®Google Scholar
• Stroppa F, Loconsole C, Frisoli A. Convex polygon fitting in robot-based neurorehabilitation. Appl Soft Comput. 2018;68:609–625.
   Web of Science ®Google Scholar
• Brutsch K, Koenig A, Zimmerli L, et al. Virtual reality for enhancement of robot-assisted gait training in children with central gait disorders. J Rehabil Med. 2011 May;43(6):493–499.
   PubMed Web of Science ®Google Scholar
• Holden MK. Virtual environments for motor rehabilitation: review. CyberPsychol Behav. 2005 Jun;8(3):187–211. [Review]
   PubMedGoogle Scholar
• Tutak JS. Virtual reality and exercises for paretic upper limb of stroke survivors. Teh Vjesn. 2017 Sep;24:451–458. [Article]
   Web of Science ®Google Scholar
• Pennycott A, Wyss D, Vallery H, et al. Towards more effective robotic gait training for stroke rehabilitation: a review. J NeuroEng Rehabil. 2012;Sep(9):13. [Review].
   Google Scholar
• Koenig A, Omlin X, Bergmann J, et al. Controlling patient participation during robot-assisted gait training. J NeuroEng Rehabil. 2011 Mar;8:12. [Article]
   PubMed Web of Science ®Google Scholar
• Nielsen JB, Willerslev-Olsen M, Christiansen L, et al. Science-based neurorehabilitation: recommendations for neurorehabilitation from basic science. J Mot Behav. 2015;47(1):7–17. [Review].
   PubMed Web of Science ®Google Scholar
• Brunette ES, Flemmer RC, Flemmer CL A review of artificial intelligence. In Gupta GS, Mukhopadhyay SC, editors. Proceedings Of the Fourth International Conference on Autonomous Robots And Agents. New York: Ieee; 2009. English.
   Google Scholar
• Novak D, Riener R. Control strategies and artificial intelligence in rehabilitation robotics. AI Mag. 2015;36(4):23–33. [Article]. Win.
   Web of Science ®Google Scholar
• Parker G. Are robots smart? Ind Rob. 2001;28(4):279–280. [Editorial Material].
   Google Scholar
• Lisboa PJG. A review of evidence of health benefit from artificial neural networks in medical intervention. Neural Netw. 2002 Jan;15(1):11–39. [Review]
   PubMed Web of Science ®Google Scholar
• Su Y, Su L. Intelligent medical rehabilitation training instrument based on movement coordination. IEEE Access. 2020;8:73373–73387.
   Web of Science ®Google Scholar
• Ling W, Yu G, Li Z. Lower limb exercise rehabilitation assessment based on artificial intelligence and medical big data. IEEE Access. 2019;7:126787–126798.
   Web of Science ®Google Scholar
• Oña ED, Garcia-Haro JM, Jardón A, et al. Robotics in health care: perspectives of robot-aided interventions in clinical practice for rehabilitation of upper limbs. Appl Sci. 2019;9(13):2586.
   Google Scholar
• Rehmat N, Zuo J, Meng W, et al. Upper limb rehabilitation using robotic exoskeleton systems: a systematic review. Int J Intell Robot Appl. 2018;2(3):283–295.
   Web of Science ®Google Scholar
• Yue Z, Zhang X, Wang J. Hand rehabilitation robotics on poststroke motor recovery. Behav Neurol. 2017;2017:3908135.
   PubMed Web of Science ®Google Scholar
• Lorish TR, Sandin KJ, Roth EJ, et al. Stroke rehabilitation .3. rehabilitation evaluation and management. Arch Phys Med Rehabil. 1994 May;75(5):S47–S51. [Article]
   PubMed Web of Science ®Google Scholar
• Lynch EA, Luker JA, Cadilhac DA, et al. Rehabilitation assessments for patients with stroke in australian hospitals do not always reflect the patients’ rehabilitation requirements. Arch Phys Med Rehabil. 2015 May;96(5):782–789. [Article]
   PubMed Web of Science ®Google Scholar
• Zeng X, Zhu G, Zhang M, et al. Reviewing clinical effectiveness of active training strategies of platform-based ankle rehabilitation robots. J Healthc Eng. 2018;2018:2858294.
   PubMed Web of Science ®Google Scholar
• Proietti T, Crocher V, Roby-Brami A, et al. Upper-limb robotic exoskeletons for neurorehabilitation: a review on control strategies. IEEE Rev Biomed Eng. 2016;9:4–14.
   PubMedGoogle Scholar