Robotics in Lower-Limb Rehabilitation after Stroke

Xue Zhang, Zan Yue, Jing Wang, Xue Zhang, Zan Yue, Jing Wang

Abstract

With the increase in the elderly, stroke has become a common disease, often leading to motor dysfunction and even permanent disability. Lower-limb rehabilitation robots can help patients to carry out reasonable and effective training to improve the motor function of paralyzed extremity. In this paper, the developments of lower-limb rehabilitation robots in the past decades are reviewed. Specifically, we provide a classification, a comparison, and a design overview of the driving modes, training paradigm, and control strategy of the lower-limb rehabilitation robots in the reviewed literature. A brief review on the gait detection technology of lower-limb rehabilitation robots is also presented. Finally, we discuss the future directions of the lower-limb rehabilitation robots.

Figures

Figure 1
Figure 1
Passive and active control modes [88].

References

    1. Leone S., Noera G., Bertolini A. Developments and new vistas in the field of melano-cortins. Biomolecular Concepts. 2015;6(5–6):962–969. doi: 10.1515/bmc-2015-0023.
    1. Williams G. R., Jiang J. G., Matchar D. B., Samsa G. P. Incidence and occurrence of total (first-ever and recurrent) stroke. Stroke. 1999;30(12):2523–2528. doi: 10.1161/01.STR.30.12.2523.
    1. Ochi M., Wada F., Saeki S., Hachisuka K. Gait training in subacute non-ambulatory stroke patients using a full weight-bearing gait-assistance robot: a prospective, randomized, open, blinded-endpoint trial. Journal of the Neurological Sciences. 2015;353(1-2):130–136. doi: 10.1016/j.jns.2015.04.033.
    1. Zhang T. Guidelines for rehabilitation of stroke rehabilitation in China (2011 full version) Chinese Journal of Rehabilitation Theory and Practice. 2012;18(4):310–318.
    1. Chen W. W., Gao R. L. China cardiovascular disease report 2013. Chinese Circulation Journal. 2014;29(7):487–491.
    1. Zhou Z., Meng W., Liu Q., Wu X., Ai Q. Practical velocity tracking control of parallel robot based on fuzzy adaptive algorithm. Advances in Mechanical Engineering. 2013;13(13):323–335.
    1. Nef T., Mihelj M., Kiefer G., Perndl C., Muller R., Riener R. ARM in exoskeleton for arm therapy in stroke patients. 2007 IEEE 10th international conference on rehabilitation robotics; 2007; New York. IEEE; pp. 68–74.
    1. Kwakkel G., Kollen B. J., Krebs H. I. Effects of robot-assisted therapy on upper limb recovery after stroke: a systematic review. Neurorehabilitation and Neural Repair. 2008;22(2):111–121. doi: 10.1177/1545968307305457.
    1. Zhang J. F., Dong Y. M., Yang C. J., Geng Y., Chen Y., Yang Y. 5-link model based gait trajectory adaption control strategies of the gait rehabilitation exoskeleton for post-stroke patients. Mechatronics. 2010;20(3):368–376. doi: 10.1016/j.mechatronics.2010.02.003.
    1. Duschau-Wicke A., Caprez A., Riener R. Patient-cooperative control increases active participation of individuals with SCI during robot-aided gait training. Neuroengineering Rehabilitation. 2010;7(1):p. 13. doi: 10.1186/1743-0003-7-43.
    1. Kazerooni H., Steger R., Huang L. H. Hybrid control of the Berkeley lower extremity exoskeleton (BLEEX) International Journal of Robotics Research. 2006;25(5–6):561–573. doi: 10.1177/0278364906065505.
    1. Veneman J. F., Kruidhof R., 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.2003.818185.
    1. Ekkelenkamp R., Veneman J. F., Van der Helm F. C. T., Van Asseldonk E. H. Selective control of a subtask of walking in a robotic gait trainer (LOPES). 2007 IEEE 10th International Conference on Rehabilitation Robotics; 2007; Noordwijk, Netherlands. pp. 841–848.
    1. Girone M., Burdea G., Bouzit M., Popescu V., Deutsch J. E. A Stewart platform based system for ankle telerehabilitation. Autonomous Robots. 2001;10(2):203–212. doi: 10.1023/A:1008938121020.
    1. Hesse S., Schmidt H., Werner C., Bardeleben A. Upper and lower extremity robotic devices for rehabilitation and for studying motor control. Current Opinion in Neurology. 2003;16(6):705–710. doi: 10.1097/01.wco.0000102630.16692.38.
    1. Freivogel S., Mehrholz J., Husak-Sotomayor T., Schmalohr D. Gait training with the newly developed ‘LokoHelp’-system is feasible for non-ambulatory patients after stroke, spinal cord and brain injury. A feasibility study. Brain Injury. 2008;22(7–8):625–632. doi: 10.1080/02699050801941771.
    1. 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.
    1. Tufekciler N., van Asseldonk E. H., van der Kooij H. Velocity-dependent reference trajectory generation for the LOPES gait training robot. IEEE International Conference on Rehabilitation Robotics. 2011;2011:1–5. doi: 10.1109/ICORR.2011.5975414.5975414
    1. Vladareanu L., Melinte O., Bruja A., et al. Haptic interfaces for the rescue walking robots motion in the disaster areas. Ukacc International Conference on Control; 2014; Loughborough, UK. pp. 498–503.
    1. 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.
    1. Sawicki G., Ferris D. A pneumatically powered Knee-Ankle-Foot Orthosis (KAFO) with myoelectric activation and inhibition. Journal of Neuroengineering and Rehabilitation. 2009;6(1):p. 23. doi: 10.1186/1743-0003-6-23.
    1. Sankai Y. HAL: Hybrid Assistive Limb based on cybernics, Robotics research. Heidelberg: Springer; 2011. pp. 25–34.
    1. Chu A., Kazerooni H., Zoss A. On the biomimetic design of the Berkeley Lower Extremity Exoskeleton (BLEEX). IEEE International Conference on Robotics and Automation; 2006; Orlando, FL, USA. pp. 4353–4360.
    1. 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.
    1. Girone M., Burdea G., Bouzit M., Popescu V., Deutsch J. E. A Stewart platform-based system for ankle telerehabilitation. Autonomous Robots. 2001;10(2):203–212. doi: 10.1023/A:1008938121020.
    1. Saglia J. A., Tsagarakis N. G., Dai J. S., Caldwell D. G. A high-performance redundantly actuated parallel mechanism for ankle rehabilitation. International Journal of Robotics Research. 2009;28(9):1216–1227. doi: 10.1177/0278364909104221.
    1. Saglia J. A., Tsagarakis N. G., Dai J. S., Caldwell D. G. Control strategies for patient-assisted training using the Ankle Rehabilitation Robot (ARBOT) IEEE/ASME Transactions on Mechatronics. 2012;18(6):1799–1808.
    1. Tsoi Y. H., Xie S. Q., Mallinson G. D. Joint force control of parallel robot for ankle rehabilitation. 2009 IEEE international conference on control and automation; ICCA; 2009. pp. 1856–1861.
    1. Xie S. Q., Jamwal P. K. An iterative fuzzy controller for pneumatic muscle driven rehabilitation robot. Expert Systems with Applications. 2011;38(7):8128–8137. doi: 10.1016/j.eswa.2010.12.154.
    1. Schmidt H., Werner C., Bernhardt R., Hesse S., Krüger J. Gait rehabilitation machines based on programmable footplates. Neuroengineering Rehabilitation. 2007;4(1):p. 2. doi: 10.1186/1743-0003-4-2.
    1. Agrawal S. K., Herder J. An approach called “complementary limb motion estimation” was implemented on the “LOPES” gait rehabilitation robot. IEEE Transactions on Neural Systems & Rehabilitation Engineering a Publication of the IEEE Engineering in Medicine & Biology Society. 2009;17(1):p. 1.
    1. Hesse S., Tomelleri C., Bardeleben A., Werner C., Waldner A. Robot-assisted practice of gait and stair climbing in nonambulatory stroke patients. Rehabilitation Research and Development. 2012;49(4):613–622.
    1. Lefeber D. Novel compliant actuator for safe and ergonomic rehabilitation robots – design of a powered elbow orthosis. 2007 IEEE 10th International Conference on Rehabilitation Robotics; 2007; Noordwijk, Netherlands. pp. 790–797.
    1. Backus D., Winchester P., Tefertiller C. Translating research into clinical practice: integrating robotics into neurorehabilitation for stroke survivors. Topics in Stroke Rehabilitation. 2010;17(5):p. 362. doi: 10.1080/02699050801941771.
    1. Freivogel S., Schmalohr D., Mehrholz J. Improved walking ability and reduced therapeutic stress with an electrome-chanical gait device. Journal of Rehabilitation Medicine. 2009;41(9):734–739. doi: 10.2340/16501977-0422.
    1. .
    1. Ekkelenkamp R., Veneman J., van der Kooij H. LOPES: selective control of gait functions during the gait rehabilitation of CVA patients. Rehabilitation Robotics, ICORR, 9th International Conference; 2005; Chicago, IL, USA. pp. 361–364.
    1. Veneman J. F., Kruidhof R., Hekman E. E., Ekkelenkamp R., Van Asseldonk E. H., Van Der Kooij H. Design and evaluation of the LOPES exoskeleton robot for interactive gait rehabilitation. IEEE Transactions on Neural Systems & Rehabilitation Engineering a Publication of the IEEE Engineering in Medicine & Biology Society. 2007;15(3):379–386. doi: 10.1109/TNSRE.2007.903919.
    1. Banala S. K., Agrawal S. K., Scholz J. P. Active Leg Exoskeleton (ALEX) for gait rehabilitation of motor-impaired patients. Proceedings of the 2007IEEE 10th International Conference on Rehabilitation Robotics, June 12–15, 2007; Noordwijk, Netherlands. IEEE; 2007. pp. 401–407.
    1. 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.
    1. .
    1. Dollar A. M., Herr H. Lower extremity exoskeletons and active orthoses: challenges and state of the art. IEEE Transactions on Robotics. 2008;24(1):144–158. doi: 10.1109/TRO.2008.915453.
    1. 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.
    1. Sunil K., Jagan D., Shaktidev M. Advances in intelligent systems and computing. ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India-Vol II; 2014; Hyderabad, Telangana, India. pp. 577–583.
    1. .
    1. Dai J. S., Zoppi M., Kong X. Advances in Reconfigurable Mechanisms and Robots. London: Springer; 2012.
    1. Kawamoto H., Sankai Y. Power assist system HAL-3 for gait disorder person. Proceedings of the 8th International Conference on Computers Helping People with Special Needs; London, UK. Springer-Verlag; 2002. pp. 196–203.
    1. 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.
    1. Meng W., Liu Q., Zhou Z., Ai Q., Sheng B., Xie S. S. Recent development of mechanisms and control strategies for robot-assisted lower limb rehabilitation. Mechatronics. 2015;31(4):132–145. doi: 10.1016/j.mechatronics.2015.04.005.
    1. Werner C., von Frankenberg S., Treig T., Konrad M., Hesse S. Treadmill training with partial body weight support and an electromechanical gait trainer for restoration of gait in subacute stroke patients—a randomized crossover study. Stroke. 2002;33(12):2895–2901. doi: 10.1161/01.STR.0000035734.61539.F6.
    1. Pohl M., Werner C., Holzgraefe M., et al. Repetitive locomotor training and physiotherapy improve walking and basic activities of daily living after stroke: a single-blind, randomized multicentre trial (DEutsche GAngtrainer Studie, DEGAS) Clinical Rehabilitation. 2007;21(1):17–27. doi: 10.1177/0269215506071281.
    1. Peurala S. H., Airaksinen O., Huuskonen P., et al. Effects of intensive therapy using gait trainer or floor walking exercises early after stroke. Journal of Rehabilitation Medicine. 2009;41(3):166–173. doi: 10.2340/16501977-0304.
    1. Smania N., Bonetti P., Gandolfi M., et al. Improved gait after repetitive locomotor training in children with cerebral palsy. American Journal of Physical Medicine & Rehabilitation. 2011;90(2):137–149. doi: 10.1097/PHM.0b013e318201741e.
    1. Iosa M., Morone G., Bragoni M., et al. Driving electromechanically assisted gait trainer for people with stroke. Journal of Rehabilitation Research and Development. 2011;48(2):135–145. doi: 10.1682/JRRD.2010.04.0069.
    1. Hesse S., Tomelleri C., Bardeleben A., Werner C., Waldner A. Robot-assisted practice of gait and stair climbing in nonambulatory stroke patients. Rehabilitation Research and Development. 2012;49(4):613–622.
    1. Hesse S., Waldner A., Tomelleri C. Innovative gait robot for the repetitive practice of floor walking and stair climbing up and down in stroke patients. Neuroengineering Rehabilitation. 2010;7(1):p. 30. doi: 10.1186/1743-0003-7-30.
    1. Yoon J., Novandy B., Yoon C. H., Park K. J. A 6-DOF gait rehabilitation robot with upper and lower limb connections that allows walking velocity updates on various terrains. IEEE/ASME Transactions on Mechatronics. 2010;15(2):201–215. doi: 10.1109/TMECH.2010.2040834.
    1. Liu Q., Dong L., Meng W., Zhou Z., Ai Q. Fuzzy sliding mode control of a multi-DOF parallel robot in rehabilitation environment. International Journal of Humanoid Robotics. 2014;11(1):p. 1450004.
    1. Girone M., Burdea G., Bouzit M., Popescu V., Deutsch J. E. A Stewart platform-based system for ankle telerehabilitation. Autonomous Robots. 2001;10(2):203–212. doi: 10.1023/A:1008938121020.
    1. Lo H. S., Xie S. Q. Exoskeleton robots for upper-limb rehabilitation: state of the art and future prospects. Medical Engineering & Physics. 2012;34(3):261–268. doi: 10.1016/j.medengphy.2011.10.004.
    1. Zhang F., Li P., Hou Z.-G., et al. SEMG-based continuous estimation of joint angles of human legs by using BP neural network. Neurocomputing. 2012;78(1):139–148. doi: 10.1016/j.neucom.2011.05.033.
    1. Riener R., Luenenberger L., Colombo G. Human-centered robotics applied to gait training and assessment. Rehabilitation Research & Development. 2006;43(5):679–693.
    1. Pinzon-Morales R.-D., Hirata Y. The number of granular cells in a cerebellar neuronal network model engaged during robot control increases with the complexity of the motor task. Pinzon-Morales and Hirata BMC Neuroscience. 2014;15(Supplement 1):143–145. doi: 10.1186/1471-2202-15-S1-P143.
    1. Adrián P. A., Bjørg E. K., Alexa R. Relating firing rate and spike time irregularity in motor cortical neurons. Eighteenth Annual Computational Neuroscience Meeting: CNS 2009; 2009; Berlin, Germany. pp. 18–23.
    1. Sellin A., Niglas A., Õunapuu-Pikas E., Kupper P. Rapid and long-term effects of water deficit on gas exchange and hydraulic conductance of silver birch trees grown under varying atmospheric humidity. BMC Plant Biology. 2014;14(1):72–83. doi: 10.1186/1471-2229-14-72.
    1. Lance J. M., Zeynep E., Madeleine M. Time and frequency domain methods for quantifying common modulation of motor unit firing patterns. Journal of Neuro Engineering and Rehabilitation. 2004;1(1):2–13. doi: 10.1186/1743-0003-1-2.
    1. Goffredo M., Bernabucci I., Schmid M., Conforto S. A neural tracking and motor control approach to improve rehabilitation of upper limb movements. Journal of Neuro Engineering and Rehabilitation. 2008;5(1):5–16. doi: 10.1186/1743-0003-5-5.
    1. Vegter R. J. K., Lamoth C. J., de Groot S., Veeger D. H., van der Woude L. H. Variability in bimanual wheelchair propulsion: consistency of two instrumented wheels during handrim wheelchair propulsion on a motor driven treadmill. Journal of Neuroengineering and Rehabilitation. 2013;10(1):9–20. doi: 10.1186/1743-0003-10-9.
    1. De Asha A. R., Munjal R., Kulkarni J., Buckley J. G. Walking speed related joint kinetic alterations in trans-tibial amputees: impact of hydraulic ‘ankle’ damping. Journal of Neuro Engineering and Rehabilitation. 2013;10(1):107–121. doi: 10.1186/1743-0003-10-107.
    1. Yan L., Alam M., Guo S., Ting K. H., He J. Electronic bypass of spinal lesions: activation of lower motor neurons directly driven by cortical neural signals. Journal of Neuro Engineering and Rehabilitation. 2014;11(1):107–118. doi: 10.1186/1743-0003-11-107.
    1. Yuan H., Lu Y., Abu I. M., He Z. Bio-electrochemical production of hydrogen in an innovative pressure retarded osmosis/microbial electrolysis cell system: experiments and modeling. Biotechnology for Biofuels. 2015;8(1):116–127. doi: 10.1186/s13068-015-0305-0.
    1. Agrawal S. K., Banala S. K., Fattah A., et al. Assessment of motion of a swing leg and gait rehabilitation with a gravity balancing exoskeleton. IEEE Transactions on Neural Systems & Rehabilitation Engineering. 2007;15(3):410–420. doi: 10.1109/TNSRE.2007.903930.
    1. Zhang J. F., Dong Y. M., Yang C. J., Geng Y., Chen Y., Yang Y. 5-link model based gait trajectory adaption control strategies of the gait rehabilitation exoskeleton for post-stroke patients. Mechatronics. 2010;20(3):368–376. doi: 10.1016/j.mechatronics.2010.02.003.
    1. Pons J. L. Wearable Robots: Biomechatronic Exoskeletons. Hoboken, USA: John Wiley and Sons; 2008. pp. 127–149.
    1. Raibert M. H., Craig J. J. Hybrid position/force control of manipulators. Journal of Dynamic Systems, Measurement and Control. 1981;103(2):126–133. doi: 10.1115/1.3139652.
    1. Kumar N., Panwar V., Sukavanam N., Sharma S. P., Borm J. H. Neural network based hybrid force/position control for robot manipulators. International Journal of Precision Engineering and Manufacturing. 2011;12(3):419–426. doi: 10.1007/s12541-011-0054-3.
    1. Bernhardt M., Frey M., Colombo G., Riener R. Hybrid force-position control yields cooperative behaviour of the rehabilitation robot Lokomat. Proceedings of the 9th International Conference on Rehabilitation Robotics; 2008; Chicago, USA. IEEE; pp. 536–539.
    1. Tsoi Y. H., Xie S. Q. Impedance control of ankle rehabilitation robot. Proceedings of the 2008 IEEE International Conference on Robotics and Biomimetics; 2009; Bangkok. IEEE; pp. 840–845.
    1. Hogan N. Impedance control—an approach to manipulation, part I-theory, part II-implementation, part III-applications. Journal of Dynamic Systems Measurementand Control-Transactions of the ASME. 1985;107(1):1–24.
    1. Yang Y., Wang L., Tong J., Zhang L. Arm rehabilitation robot impedance control and experimentation. Proceedings of the 2006 IEEE International Conference on Robotics and Biomimetics; Kunming, China. IEEE; 2006. pp. 914–918.
    1. Jung S., Hsia T. Neural network impedance force control of robot manipulator. IEEE Transactions on Industrial Electronics. 1998;45(3):451–461. doi: 10.1109/41.679003.
    1. Hussein S., Schmidt H., Krueger J. Adaptive control of an end-effector based electromechanical gait rehabilitation device. Proceedings of the 2009 IEEE International Conference on Rehabilitation Robotics; Kyoto, Japan. IEEE; 2009. pp. 425–430.
    1. Robertson G., Caldwell G., Hamill J., Kamen G., Whittlesey S. Research Methods in Biomechanics. Champaign, USA: Human Kinetics; 2013. pp. 179–182.
    1. De Luca C. The use of surface electromyography in biomechanics. Journal of Applied bio-Mechanics. 1997;13(2):135–163.
    1. Niedermeyer E., da Silva F. L. Electroencephalography: Basic Principles, Clinical Application-s, and Related Fields. Philadelphia, USA: Lippincott Williams and Wilkins; 2005. pp. 18–28.
    1. Sellin A., Niglas A., Õunapuu-Pikas E., Kupper P. Rapid and long-term effects of water deficit on gas exchange and hydraulic conductance of silver birch trees grown under varying atmospheric humidity. BMC Plant Biology. 2014;14(1):72–83. doi: 10.1186/1471-2229-14-72.
    1. Schmidt H., Werner C., Bernhardt R., Hesse S., Krüger J. Gait rehabilitation machines based on programmable footplates. Neuroengineering Rehabilitation. 2007;4(1):p. 2. doi: 10.1186/1743-0003-4-2.
    1. Pittaccio S., Viscuso S. An EMG-controlled SMA device for the rehabilitation of the ankle joint in post-acute stroke. Mater Eng Perform. 2011;20:666–670.
    1. Goffredo M., Bernabucci I., Schmid M., Conforto S. A neural tracking and motor control approach to improve rehabilitation of upper limb movements. Journal of Neuro Engineering and Rehabilitation. 2008;5(1):5–16. doi: 10.1186/1743-0003-5-5.
    1. Jamwal P. K., Xie S. Q., Hussain S., Parsons J. G. An adaptive wearable parallel robot for the treatment of ankle injuries. IEEE/ASME Transactions on Mechatronics. 2014;19(1):64–75. doi: 10.1109/TMECH.2012.2219065.
    1. Hussain S., Xie S. Q., Jamwal P. K. Robust nonlinear control of an intrinsically compliant robotic gait training orthosis. IEEE Transactions on Systems, man, and Cybernetics: Systems. 2013;43:655–665. doi: 10.1109/TSMCA.2012.2207111.
    1. Shahbazi M., Atashzar S. F., Tavakoli M., Patel R. V. Robotics-assisted mirror rehabilitation therapy: a therapist-in-the-loop assist-as-needed architecture. IEEE/ASME Transactions on Mechatronics. 2016;21(4):1954–1965. doi: 10.1109/TMECH.2016.2551725.
    1. Koopman B., van Asseldonk E. H., van der Kooij H., Van Dijk W., Ronsse R. Rendering potential wearable robot designs with the LOPES gait trainer. IEEE International Conference on Rehabilitation Robotics; 2011; Zürich, Switzerland. pp. 1–6.
    1. Meuleman J. H. Design of a Robot-Assisted Gait Trainer. Netherlands: LOPES II; 2015.
    1. Saglia J. A., Tsagarakis N. G., Dai J. S., Caldwell D. G. Control strategies for patient-assisted training using the ankle rehabilitation robot (ARBOT) IEEE/ASME Transactions on Mechatronics. 2012;18(6):1799–1808.
    1. Ai Q., Ding B., Liu Q., Meng W. A subject-specific EMG-driven musculoskeletal model for applications in lower-limb rehabilitation robotics. International Journal of Humanoid Robotics. 2016;13(3):p. 1650005.
    1. Duvinage M., Castermans T., Petieau M., et al. A subjective assessment of a P300 BCI system for lower-limb rehabilitation purposes. 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2012; San Diego, CA, USA. pp. 3845–3849.
    1. Wang H., Zhang X., Chen J., Wang Y. Realization of human-computer interaction of lower limbs rehabilitation robot based on sEMG. IEEE, International Conference on Cyber Technology in Automation, Control, and Intelligent Systems; 2014; Hong Kong. pp. 1889–1898.
    1. Zhang X., Xu G., Xie J., Li M., Pei W., Zhang J. An EEG-driven lower limb rehabilitation training system for active and passive co-stimulation Embc. Conf Proc IEEE Eng Med Biol Soc; 2015; Milan, Italy. pp. 4582–4585.
    1. Maria D. R., Stefano M. V., Lenzi T., et al. Sensing pressure distribution on lower-limb exoskeleton physical human-machine Interface. Sensors. 2011;11(1):207–227. doi: 10.3390/s110100207.
    1. Barroso F., Santos C., Moreno J. C. Influence of the robotic exoskeleton Lokomat on the control of human gait: an electromyographic and kinematic analysis. Portuguese Meeting in Bioengineering; 2013; Braga, Portugal. pp. 1–6.

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