Abstract
As a wearable and intelligent system, a lower limb exoskeleton rehabilitation robot can provide auxiliary rehabilitation training for patients with lower limb walking impairment/loss and address the existing problem of insufficient medical resources. One of the main elements of such a human—robot coupling system is a control system to ensure human—robot coordination. This review aims to summarise the development of human—robot coordination control and the associated research achievements and provide insight into the research challenges in promoting innovative design in such control systems. The patients’ functional disorders and clinical rehabilitation needs regarding lower limbs are analysed in detail, forming the basis for the human—robot coordination of lower limb rehabilitation robots. Then, human—robot coordination is discussed in terms of three aspects: modelling, perception and control. Based on the reviewed research, the demand for robotic rehabilitation, modelling for human—robot coupling systems with new structures and assessment methods with different etiologies based on multi-mode sensors are discussed in detail, suggesting development directions of human—robot coordination and providing a reference for relevant research.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Abbreviations
- COM:
-
Centre of mass
- COP:
-
Centre of pressure
- DOF:
-
Degree of freedom
- EEG:
-
Electroencephalography
- EMG:
-
Electromyography
- FFC:
-
Force-field control
- GRF:
-
Ground reaction force
- HRI:
-
Human—robot interaction
- IMU:
-
Inertial measurement unit
- MFC:
-
Moment-field control
- MIMO:
-
Multiple input multiple output
- PD:
-
Proportional-derivative
- SCI:
-
Spinal cord injury
- SEA:
-
Serial elastic actuator
- TUG:
-
Timed up and go
References
Ijspeert A J. Biorobotics: using robots to emulate and investigate agile locomotion. Science, 2014, 346(6206): 196–203
Shi D, Zhang W X, Zhang W, Ding X L. A review on lower limb rehabilitation exoskeleton robots. Chinese Journal of Mechanical Engineering, 2019, 32(1): 74
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
van Kammen K, Boonstra A M, van der Woude L H V, Visscher C, Reinders-Messelink H A, den Otter R. Lokomat guided gait in hemiparetic stroke patients: the effects of training parameters on muscle activity and temporal symmetry. Disability and Rehabilitation, 2020, 42(21): 2977–2985
Hidayah R, Bishop L, Jin X, Chamarthy S, Stein J, Agrawal S K. Gait adaptation using a cable-driven active leg exoskeleton (C-ALEX) with post-stroke participants. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020, 28(9): 1984–1993
Meuleman J, van Asseldonk E, van Oort G, Rietman H, van der Kooij H. LOPES II—design and evaluation of an admittance controlled gait training robot with shadow-leg approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2016, 24(3): 352–363
Huang R, Cheng H, Qiu J, Zhang J W. Learning physical human—robot interaction with coupled cooperative primitives for a lower exoskeleton. IEEE Transactions on Automation Science and Engineering, 2019, 16(4): 1566–1574
Zhou L B, Chen W H, Wang J H, Bai S P, Yu H Y, Zhang Y P. A novel precision measuring parallel mechanism for the closed-loop control of a biologically inspired lower limb exoskeleton. IEEE/ASME Transactions on Mechatronics, 2018, 23(6): 2693–2703
Shi D, Zhang W X, Zhang W, Ju L H, Ding X L. Human-centred adaptive control of lower limb rehabilitation robot based on human—robot interaction dynamic model. Mechanism and Machine Theory, 2021, 162: 104340
Long Y, Du Z J, Chen C F, Wang W D, He L, Mao X W, Xu G Q, Zhao G Y, Li X Q, Dong W. Development and analysis of an electrically actuated lower extremity assistive exoskeleton. Journal of Bionics Engineering, 2017, 14(2): 272–283
Wei D, Li Z J, Wei Q, Su H, Song B, He W, Li J Q. Human-in-the-loop control strategy of unilateral exoskeleton robots for gait rehabilitation. IEEE Transactions on Cognitive and Developmental Systems, 2021, 13(1): 57–66
Ding Y, Kim M, Kuindersma S, Walsh C J. Human-in-the-loop optimization of hip assistance with a soft exosuit during walking. Science Robotics, 2018, 3(15): eaar5438
Ding H, Yang X J, Zheng N N, Li M, Lai Y N, Wu H. Tri-co robot: a Chinese robotic research initiative for enhanced robot interaction capabilities. National Science Review, 2018, 5(6): 799–801
Meng W, Liu Q, Zhou Z D, Ai Q S, Sheng B, Xie S Q. Recent development of mechanisms and control strategies for robot-assisted lower limb rehabilitation. Mechatronics, 2015, 31: 132–145
Kalita B, Narayan J, Dwivedy S K. Development of active lower limb robotic-based orthosis and exoskeleton devices: a systematic review. International Journal of Social Robotics, 2021, 13(4): 775–793
Zhou J M, Yang S, Xue Q. Lower limb rehabilitation exoskeleton robot: a review. Advances in Mechanical Engineering, 2021, 13(4): 16878140211011862
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
Baud R, Manzoori A R, Ijspeert A, Bouri M. Review of control strategies for lower-limb exoskeletons to assist gait. Journal of NeuroEngineering and Rehabilitation, 2021, 18(1): 119
Ferris D P, Sawicki G S, Daley M A. A physiologist’s perspective on robotic exoskeletons for human locomotion. International Journal of Humanoid Robotics, 2007, 4(3): 507–528
Bohannon R W, Schaubert K. Long-term reliability of the timed up-and-go test among community-dwelling elders. Journal of Physical Therapy Science, 2005, 17(2): 93–96
Podsiadlo D, Richardson S. The timed “up & go”: a test of basic functional mobility for frail elderly persons. Journal of the American Geriatrics Society, 1991, 39(2): 142–148
Guralnik J M, Simonsick E M, Ferrucci L, Glynn R J, Berkman L F, Blazer D G, Scherr P A, Wallace R B. A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. Journal of Gerontology, 1994, 49(2): M85–M94
Berg K, Wood-Dauphine S, Williams J I, Gayton D. Measuring balance in the elderly: preliminary development of an instrument. Physiotherapy Canada, 1989, 41(6): 304–311
Ganz D A, Bao Y R, Shekelle P G, Rubenstein L Z. Will my patient fall? Journal of the American Medical Association, 2007, 297(1): 77–86
Lee T K M, Belkhatir M, Sanei S. A comprehensive review of past and present vision-based techniques for gait recognition. Multimedia Tools and Applications, 2014, 72(3): 2833–2869
Scheffer C, Cloete T. Inertial motion capture in conjunction with an artificial neural network can differentiate the gait patterns of hemiparetic stroke patients compared with able-bodied counterparts. Computer Methods in Biomechanics and Biomedical Engineering, 2012, 15(3): 285–294
Den Otter A R, Geurts A C H, Mulder T, Duysens J. Abnormalities in the temporal patterning of lower extremity muscle activity in hemiparetic gait. Gait & Posture, 2007, 25(3): 342–352
Pickle N T, Shearin S M, Fey N P. Dynamic neural network approach to targeted balance assessment of individuals with and without neurological disease during non-steady-state locomotion. Journal of NeuroEngineering and Rehabilitation, 2019, 16(1): 88
Swinnen E, Beckwée D, Meeusen R, Baeyens J P, Kerckhofs E. Does robot-assisted gait rehabilitation improve balance in stroke patients? A systematic review. Topics in Stroke Rehabilitation, 2014, 21(2): 87–100
Park J H, Kim Y, Lee K J, Yoon Y S, Kang S H, Kim H, Park H S. Artificial neural network learns clinical assessment of spasticity in modified ashworth scale. Archives of Physical Medicine and Rehabilitation, 2019, 100(10): 1907–1915
Pinto-Fernandez D, Torricelli D, Sanchez-Villamanan M D C, Aller F, Mombaur K, Conti R, Vitiello N, Moreno J C, Pons J L. Performance evaluation of lower limb exoskeletons: a systematic review. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020, 28(7): 1573–1583
Galen S S, Clarke C J, Allan D B, Conway B A. A portable gait assessment tool to record temporal gait parameters in SCI. Medical Engineering & Physics, 2011, 33(5): 626–632
Granat M H, Maxwell D J, Bosch C J, Ferguson A C B, Lees K R, Barbenel J C. A body-worn gait analysis system for evaluating hemiplegic gait. Medical Engineering & Physics, 1995, 17(5): 390–394
Neckel N, Pelliccio M, Nichols D, Hidler J. Quantification of functional weakness and abnormal synergy patterns in the lower limb of individuals with chronic stroke. Journal of NeuroEngineering and Rehabilitation, 2006, 3(1): 17
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(1): 19
Hu B H, Zhang X F, Mu J S, Wu M, Zhu Z J, Liu Z S, Wang Y. Spasticity measurement based on the HHT marginal spectrum entropy of sEMG using a portable system: a preliminary study. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018, 26(7): 1424–1434
Dewald J P A, Pope P S, Given J D, Buchanan T S, Rymer W Z. Abnormal muscle coactivation patterns during isometric torque generation at the elbow and shoulder in hemiparetic subjects. Brain, 1995, 118(2): 495–510
Shestakov M P. Balance of a multijoint biomechanical system in natural and artificial environments: a simulation model. Journal of Physiological Anthropology, 2007, 26(3): 419–423
Kasaoka K, Sankai Y. Predictive control estimating operator’s intention for stepping-up motion by exo-skeleton type power assist system HAL. In: Proceedings of 2001 IEEE/RSJ International Conference on Intelligent Robots & Systems. Maui: IEEE, 2001, 3: 1578–1583
Pei P, Pei Z C, Tang Z Y, Gu H. Position tracking control of PMSM based on fuzzy PID-variable structure adaptive control. Mathematical Problems in Engineering, 2018, (1): 5794067
Liu D F, Tang Z Y, Pei Z C. Variable structure compensation PID control of asymmetrical hydraulic cylinder trajectory tracking. Mathematical Problems in Engineering, 2015, (1): 890704
Zhang M M, Xie S Q, Li X L, Zhu G L, Meng W, Huang X L, Veale A J. Adaptive patient-cooperative control of a compliant ankle rehabilitation robot (CARR) with enhanced training safety. IEEE Transactions on Industrial Electronics, 2018, 65(2): 1398–1407
Shao Y X, Zhang W X, Su Y J, Ding X L. Design and optimisation of load-adaptive actuator with variable stiffness for compact ankle exoskeleton. Mechanism and Machine Theory, 2021, 161: 104323
Yu H Y, Huang S N, Chen G, Pan Y P, Guo Z. Human—robot interaction control of rehabilitation robots with series elastic actuators. IEEE Transactions on Robotics, 2015, 31(5): 1089–1100
Zhang W, Zhang W X, Shi D, Ding X L. Design of hip joint assistant asymmetric parallel mechanism and optimization of singularity-free workspace. Mechanism and Machine Theory, 2018, 122: 389–403
Wang D H, Lee K M, Ji J J. A passive gait-based weight-support lower extremity exoskeleton with compliant joints. IEEE Transactions on Robotics, 2016, 32(4): 933–942
Wang Y J, Wu C L, Yu L Q, Mei Y Y. Dynamics of a rolling robot of closed five-arc-shaped-bar linkage. Mechanism and Machine Theory, 2018, 121: 75–91
Bascetta L, Ferretti G, Scaglioni B. Closed form Newton—Euler dynamic model of flexible manipulators. Robotica, 2017, 35(5): 1006–1030
Sun Z B, Li F, Duan X Q, Jin L, Lian Y F, Liu S S, Liu K P. A novel adaptive iterative learning control approach and human-in-the-loop control pattern for lower limb rehabilitation robot in disturbances environment. Autonomous Robots, 2021, 45(4): 595–610
Zoss A, Kazerooni H. Design of an electrically actuated lower extremity exoskeleton. Advanced Robotics, 2006, 20(9): 967–988
Sun W, Lin J W, Su S F, Wang N, Er M J. Reduced adaptive fuzzy decoupling control for lower limb exoskeleton. IEEE Transactions on Cybernetics, 2021, 51(3): 1099–1109
Qiu S Y, Guo W, Caldwell D, Chen F. Exoskeleton online learning and estimation of human walking intention based on dynamical movement primitives. IEEE Transactions on Cognitive and Developmental Systems, 2021, 13(1): 67–79
Ghan J, Steger R, Kazerooni H. Control and system identification for the Berkeley lower extremity exoskeleton (BLEEX). Advanced Robotics, 2006, 20(9): 989–1014
Huang R, Cheng H, Guo H L, Lin X C, Zhang J W. Hierarchical learning control with physical human-exoskeleton interaction. Information Sciences, 2018, 432: 584–595
Ruiz Garate V, Parri A, Yan T F, Munih M, Molino Lova R, Vitiello N, Ronsse R. Walking assistance using artificial primitives: a novel bioinspired framework using motor primitives for locomotion assistance through a wearable cooperative exoskeleton. IEEE Robotics & Automation Magazine, 2016, 23(1): 83–95
Xu J J, Li Y F, Xu L S, Peng C, Chen S Q, Liu J F, Xu C C, Cheng G X, Xu H, Liu Y, Chen J. A multi-mode rehabilitation robot with magnetorheological actuators based on human motion intention estimation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019, 27(10): 2216–2228
Meijneke C, van Oort G, Sluiter V, van Asseldonk E, Tagliamonte N L, Tamburella F, Pisotta I, Masciullo M, Arquilla M, Molinari M, Wu A R, Dzeladini F, Ijspeert A J, van der Kooij H. Symbitron exoskeleton: design, control, and evaluation of a modular exoskeleton for incomplete and complete spinal cord injured individuals. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2021, 29: 330–339
Song S, Geyer H. A neural circuitry that emphasizes spinal feedback generates diverse behaviours of human locomotion. The Journal of Physiology, 2015, 593(16): 3493–3511
Aguirre-Ollinger G, Nagarajan U, Goswami A. An admittance shaping controller for exoskeleton assistance of the lower extremities. Autonomous Robots, 2016, 40(4): 701–728
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
He W, Li Z J, Chen C L P. A survey of human-centered intelligent robots: issues and challenges. IEEE/CAA Journal of Automatica Sinica, 2017, 4(4): 602–609
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
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
Kao P C, Lewis C L, Ferris D P. Invariant ankle moment patterns when walking with and without a robotic ankle exoskeleton. Journal of Biomechanics, 2010, 43(2): 203–209
Kilicarslan A, Grossman R G, Contreras-Vidal J L. A robust adaptive denoising framework for real-time artifact removal in scalp EEG measurements. Journal of Neural Engineering, 2016, 13(2): 026013
Bulea T C, Prasad S, Kilicarslan A, Contreras-Vidal J L. Sitting and standing intention can be decoded from scalp EEG recorded prior to movement execution. Frontiers in Neuroscience, 2014, 8: 376
Liu D, Chen W H, Pei Z C, Wang J H. A brain-controlled lower-limb exoskeleton for human gait training. Review of Scientific Instruments, 2017, 88(10): 104302
Lyu M X, Chen W H, Ding X L, Wang J H, Pei Z C, Zhang B C. Development of an EMG-controlled knee exoskeleton to assist home rehabilitation in a game context. Frontiers in Neurorobotics, 2019, 13: 67
Huang L P, Zheng J B, Hu H C. Online gait phase detection in complex environment based on distance and multi-sensors information fusion using inertial measurement units. International Journal of Social Robotics, 2022, 14(2): 413–428
Kang I, Molinaro D D, Duggal S, Chen Y R, Kunapuli P, Young A J. Real-time gait phase estimation for robotic hip exoskeleton control during multimodal locomotion. IEEE Robotics and Automation Letters, 2021, 6(2): 3491–3497
Wang J B, Fei Y Q, Chen W D. Integration, sensing, and control of a modular soft-rigid pneumatic lower limb exoskeleton. Soft Robotics, 2020, 7(2): 140–154
Seel T, Raisch J, Schauer T. IMU-based joint angle measurement for gait analysis. Sensors, 2014, 14(4): 6891–6909
Beravs T, Reberšek P, Novak D, Podobnik J, Munih M. Development and validation of a wearable inertial measurement system for use with lower limb exoskeletons. In: Proceedings of 2011 the 11th IEEE-RAS International Conference on Humanoid Robots. Bled: IEEE, 2011, 212–217
Ji J C, Song T, Guo S, Xi F F, Wu H. Robotic-assisted rehabilitation trainer improves balance function in stroke survivors. IEEE Transactions on Cognitive and Developmental Systems, 2020, 12(1): 43–53
Chen Z L, Guo Q, Xiong H Y, Jiang D, Yan Y. Control and implementation of 2-DOF lower limb exoskeleton experiment platform. Chinese Journal of Mechanical Engineering, 2021, 34(1): 22
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
Casas J, Senft E, Gutiérrez L F, Rincón-Rocancio M, Múnera M, Belpaeme T, Cifuentes C A. Social assistive robots: assessing the impact of a training assistant robot in cardiac rehabilitation. International Journal of Social Robotics, 2021, 13(6): 1189–1203
Billinger S A, Arena R, Bernhardt J, Eng J J, Franklin B A, Johnson C M, MacKay-Lyons M, Macko R F, Mead G E, Roth E J, Shaughnessy M, Tang A. Physical activity and exercise recommendations for stroke survivors: a statement for healthcare professionals from the American Heart Association/American Stroke Association. Stroke, 2014, 45(8): 2532–2553
Maggioni S, Melendez-Calderon A, van Asseldonk E, Klamroth-Marganska V, Lünenburger L, Riener R, van der Kooij H. Robot-aided assessment of lower extremity functions: a review. Journal of NeuroEngineering and Rehabilitation, 2016, 13(1): 72
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(3): 655–665
Yu X B, Li B, He W, Feng Y H, Cheng L, Silvestre C. Adaptive-constrained impedance control for human—robot co-transportation. IEEE Transactions on Cybernetics, 2021 (in press)
Shi D, Zhang W X, Zhang W, Ding X L. Assist-as-needed attitude control in three-dimensional space for robotic rehabilitation. Mechanism and Machine Theory, 2020, 154: 104044
Shi D, Zhang W X, Zhang W, Ding X L. Force field control for the three-dimensional gait adaptation using a lower limb rehabilitation robot. In: Uhl T, ed. Advances in Mechanism and Machine Science. IFToMM WC 2019. Mechanisms and Machine Science, vol 73. Cham: Springer International Publishing, 2019, 73: 1919–1928
Wang L T, Wang S Q, van Asseldonk E H F, van der Kooij H. Actively controlled lateral gait assistance in a lower limb exoskeleton. In: Proceedings of 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. Tokyo: IEEE, 2013, 965–970
Tsukahara A, Hasegawa Y, Eguchi K, Sankai Y. Restoration of gait for spinal cord injury patients using HAL with intention estimator for preferable swing speed. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2015, 23(2): 308–318
Duschau-Wicke A, von Zitzewitz J, Caprez A, Lunenburger L, Riener R. Path control: a method for patient-cooperative robot-aided gait rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2010, 18(1): 38–48
Riener R, Lunenburger L, Jezernik S, Anderschitz M, Colombo G, Dietz V. Patient-cooperative strategies for robot-aided treadmill training: first experimental results. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2005, 13(3): 380–394
Niu X M, Gao G Q, Liu X J, Fang Z M. Decoupled sliding mode control for a novel 3-DOF parallel manipulator with actuation redundancy. International Journal of Advanced Robotic Systems, 2015, 12(5): 64
Mohanta J K, Santhakumar M, Kurtenbach S, Corves B, Hüsing M. Augmented PID control of a 2PPR-2PRP planar parallel manipulator for lower limb rehabilitation applications. In: Corves B, Lovasz E C, Hüsing M, Maniu I, Gruescu C, eds. New Advances in Mechanisms, Mechanical Transmissions and Robotics Mechanisms and Machine Science, vol 46. Cham: Springer International Publishing, 2017, 46: 391–399
Luo L C, Peng L, Wang C, Hou Z G. A greedy assist-as-needed controller for upper limb rehabilitation. IEEE Transactions on Neural Networks and Learning Systems, 2019, 30(11): 3433–3443
Li Y, Ge S S. Human—robot collaboration based on motion intention estimation. IEEE/ASME Transactions on Mechatronics, 2014, 19(3): 1007–1014
Emken J L, Bobrow J E, Reinkensmeyer D J. Robotic movement training as an optimization problem: designing a controller that assists only as needed. In: Proceedings of the 9th International Conference on Rehabilitation Robotics (ICORR). Chicago: IEEE, 2005, 307–312
Zanotto D, Stegall P, Agrawal S K. 2014. Adaptive assist-as-needed controller to improve gait symmetry in robot-assisted gait training. In: Proceedings of 2014 IEEE International Conference on Robotics and Automation (ICRA). Hong Kong: IEEE, 2014, 724–729
Fineberg D B, Asselin P, Harel N Y, Agranova-Breyter I, Kornfeld S D, Bauman W A, Spungen A M. Vertical ground reaction force-based analysis of powered exoskeleton-assisted walking in persons with motor-complete paraplegia. The Journal of Spinal Cord Medicine, 2013, 36(4): 313–321
Long Y, Du Z J, Wang W D, Dong W. Human motion intent learning based motion assistance control for a wearable exoskeleton. Robotics and Computer-Integrated Manufacturing, 2018, 49: 317–327
Shi D, Zhang W, Wang L D, Zhang W X, Feng Y G, Ding X L. Joint angle adaptive coordination control of a serial parallel lower limb rehabilitation exoskeleton. IEEE Transactions on Medical Robotics and Bionics, 2022 (in press)
Huang R, Cheng H, Chen Y, Chen Q M, Lin X C, Qiu J. Optimisation of reference gait trajectory of a lower limb exoskeleton. International Journal of Social Robotics, 2016, 8(2): 223–235
Liu D X, Wu X Y, Du W B, Wang C, Chen C J, Xu T T. Deep spatial-temporal model for rehabilitation gait: optimal trajectory generation for knee joint of lower-limb exoskeleton. Assembly Automation, 2017, 37(3): 369–378
Kwakkel G, Kollen B, Lindeman E. Understanding the pattern of functional recovery after stroke: facts and theories. Restorative Neurology and Neuroscience, 2004, 22(3–5): 281–299
Langhorne P, Bernhardt J, Kwakkel G. Stroke rehabilitation. The Lancet, 2011, 377(9778): 1693–1702
Barbeau H, Ladouceur M, Mirbagheri M M, Kearney R E. The effect of locomotor training combined with functional electrical stimulation in chronic spinal cord injured subjects: walking and reflex studies. Brain Research Reviews, 2002, 40(1–3): 274–291
Yang Y R, Wang R Y, Lin K H, Chu M Y, Chan R C. Task-oriented progressive resistance strength training improves muscle strength and functional performance in individuals with stroke. Clinical Rehabilitation, 2006, 20(10): 860–870
Morawietz C, Moffat F. Effects of locomotor training after incomplete spinal cord injury: a systematic review. Archives of Physical Medicine and Rehabilitation, 2013, 94(11): 2297–2308
Herbert R D, Taylor J L, Lord S R, Gandevia S C. Prevalence of motor impairment in residents of New South Wales, Australia aged 55 years and over: cross-sectional survey of the 45 and Up cohort. BMC Public Health, 2020, 20(1): 1353
Chen F X, Zhang C, Chen J H, Yang G L. Accurate subdomain model for computing magnetic field of short moving-magnet linear motor with Halbach array. IEEE Transactions on Magnetics, 2020, 56(9): 1–9
Fang Y, Lerner Z F. Feasibility of augmenting ankle exoskeleton walking performance with step length biofeedback in individuals with cerebral palsy. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2021, 29: 442–449
Kim Y, Chortos A, Xu W T, Liu Y X, Oh J Y, Son D, Kang J, Foudeh A M, Zhu C X, Lee Y, Niu S M, Liu J, Pfattner R, Bao Z, Lee T W. A bioinspired flexible organic artificial afferent nerve. Science, 2018, 360(6392): 998–1003
Huang Y C, He Z X, Liu Y X, Yang R Y, Zhang X F, Cheng G, Yi J G, Ferreira J P, Liu T. Real-time intended knee joint motion prediction by deep-recurrent neural networks. IEEE Sensors Journal, 2019, 19(23): 11503–11509
Ugartemendia A, Rosquete D, Gil J J, Diaz I, Borro D. Machine learning for active gravity compensation in robotics: application to neurological rehabilitation systems. IEEE Robotics & Automation Magazine, 2020, 27(2): 78–86
Fang W, An Z W. A scalable wearable AR system for manual order picking based on warehouse floor-related navigation. The International Journal of Advanced Manufacturing Technology, 2020, 109(7–8): 2023–2037
Oppezzo M, Schwartz D L. Give your ideas some legs: the positive effect of walking on creative thinking. Journal of Experimental Psychology: Learning, Memory, and Cognition, 2014, 40(4): 1142–1152
Hidayah R, Chamarthy S, Shah A, Fitzgerald-Maguire M, Agrawal S K. Walking with augmented reality: a preliminary assessment of visual feedback with a cable-driven active leg exoskeleton (C-ALEX). IEEE Robotics and Automation Letters, 2019, 4(4): 3948–3954
Acknowledgements
This paper was funded by the National Natural Science Foundation of China (Grant Nos. 91848104, 91748201, and 52105004). The authors thank Yushuang Duan and Hongqian Zhang for their contributions to this study.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution, and reproduction in any medium or format as long as appropriate credit is given to the original author(s) and source, a link to the Creative Commons license is provided, and the changes made are indicated.
The images or other third-party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Visit http://creativecommons.org/licenses/by/4.0/ to view a copy of this license.
About this article
Cite this article
Shi, D., Wang, L., Zhang, Y. et al. Review of human—robot coordination control for rehabilitation based on motor function evaluation. Front. Mech. Eng. 17, 28 (2022). https://doi.org/10.1007/s11465-022-0684-4
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11465-022-0684-4