Research of intent recognition in rehabilitation robots: a systematic review

References

• Yang Z, Guo S, Liu Y, et al. An intention-based online bilateral training system for upper limb motor rehabilitation. Microsyst Technol. 2021;27(1):211–222.
   Web of Science ®Google Scholar
• Yang Q, Xie C, Tang R, et al. Hybrid active control with human intention detection of an upper-limb cable-driven rehabilitation robot. IEEE Access. 2020;8(1):195206–195215.
   Google Scholar
• Tonin L, Millan J, del R. Noninvasive brain-machine interfaces for robotic devices. Annual review of control, robotics, and autonomous systems, vol 4, 2021; annual reviews. Palo Alto. 2021;4(2):191–214.
   Google Scholar
• Deaconescu T, Deaconescu A. Pneumatic muscle actuated isokinetic equipment for the rehabilitation of patients with disabilities of the bearing joints. Imecs 2009: International Multi-Conference of Engineers and Computer Scientists, Vols I and II; Int Assoc Engineers-Iaeng: Hong Kong, 2009. 1823–1827.
   Google Scholar
• Fazekas G. Robotics in rehabilitation: successes and expectations. Int J Rehabil Res. 2013;36(2):95–96.
   PubMed Web of Science ®Google Scholar
• Driessen BJF, Evers HG, V Woerden JA. Manus—a wheelchair-mounted rehabilitation robot. Proc Inst Mech Eng H. 2001;215(3):285–290.
   PubMedGoogle Scholar
• Topping M, Smith J. The development of handy 1, a rehabilitation robotic system to assist the severely disabled. Ind Rob. 1998;25(5):316–320.
   Google Scholar
• Topping M. An overview of the development of handy 1, a rehabilitation robot to assist the severely disabled. J Intell Rob Syst. 2002;34(3):253–263.
   Web of Science ®Google Scholar
• Krebs HI, Celestino J, Williams D, et al. A wrist extension for MIT-MANUS. Adv Rehabil Robot. 2004;306(2):377–390.
   Google Scholar
• Zimmermann Y, Forino A, Riener R, et al. ANYexo: a versatile and dynamic upper-limb rehabilitation robot. IEEE Rob Autom Lett. 2019;4(4):3649–3656.
   Web of Science ®Google Scholar
• Frisoli A, Bergamasco M, Carboncini C, et al. Robotic assisted rehabilitation in virtual reality with the L-EXOS. Stud Health Technol Inform. 2009;145:40–54.
   PubMedGoogle Scholar
• Frisoli A, Borelli L, Montagner A, et al. Arm rehabilitation with a robotic exoskeleleton in virtual reality. 2007 IEEE 10th International Conference on Rehabilitation Robotics. 2007. 2(3),631–642.
   Google Scholar
• Nef T, Mihelj M, Riener R. ARMin: a robot for patient-cooperative arm therapy. Med Biol Eng Comput. 2007;45(9):887–900.
   PubMed Web of Science ®Google Scholar
• Mihelj M, Nef T, Riener R. ARMin II - 7 DoF rehabilitation robot: mechanics and kinematics. Proceedings 2007 IEEE International Conference on Robotics and Automation. 2007. 3(1),4120–4125.
   Google Scholar
• Nef T, Guidali M, Riener R. ARMin III – arm therapy exoskeleton with an ergonomic shoulder actuation. Appl Bionics Biomech. 2009;6(2):127–142.
   Google Scholar
• Perry JC, Rosen J, Burns S. Upper-limb powered exoskeleton design. IEEE/ASME Trans Mechatronics. 2007;12(4):408–417.
   Web of Science ®Google Scholar
• Galofaro E, D’Antonio E, Patane F. Three-dimensional assessment of upper limb proprioception via a wearable exoskeleton. Applied Sciences-Basel. 2021;11(6):1–12.
   Web of Science ®Google Scholar
• Balasubramanian S, Wei R, Perez M, et al. RUPERT: an exoskeleton robot for assisting rehabilitation of arm functions. 2008 Virtual Rehabilitation. 2008. 4(2),163–167.
   Google Scholar
• Brookes J, Kuznecovs M, Kanakis M, et al. Robots testing robots: ALAN-Arm, a humanoid arm for the testing of robotic rehabilitation systems. 2017 International Conference on Rehabilitation Robotics (ICORR). 2017. 6(2),676–681.
   Google Scholar
• Mao Y, Agrawal SK. Transition from mechanical arm to human arm with CAREX: a cable-driven ARm EXoskeleton (CAREX) for neural rehabilitation. 2012 Ieee International Conference on Robotics and Automation (ICRA).2012. 1(1) 2457–2462.
   Google Scholar
• Gopura RARC, Kiguchi K, Li Y. SUEFUL-7: a 7DOF upper-limb exoskeleton robot with muscle-model-oriented emg-based control. 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. 2009. 11(3),1126–1131.
   Google Scholar
• Madani T, Daachi B, Djouani K. Modular-controller-design-based fast terminal sliding mode for articulated exoskeleton systems. IEEE Trans Control Syst Technol. 2017;25(3):1133–1140.
   Web of Science ®Google Scholar
• De Oliveira AC, Sulzer JS, Deshpande AD. Assessment of upper-extremity joint angles using harmony exoskeleton. IEEE Trans Neural Syst Rehabil Eng. 2021;29:916–925.
   PubMed Web of Science ®Google Scholar
• Oliveira AC, Warburton K, Yun Y, et al. Arm kinematics estimation with the harmony exoskeleton. 2017 International Symposium on Wearable Robotics and Rehabilitation (WEROB). 2017. 5(1), p. 47–48.
   Google Scholar
• Vlachos E, Jochum E, Demers L-P. HEAT: the harmony exoskeleton self - assessment test. 2018 27th Ieee International Symposium on Robot and Human Interactive Communication (IEEE Ro-Man 2018). 2018. 12(2),577–582.
   Google Scholar
• Ogden EM, Chiu D, Clearman R, et al. Evaluation of the harmony exoskeleton as an upper extremity rehabilitation tool after stroke 2017 International Symposium on Wearable Robotics and Rehabilitation (WEROB). 2017. 4(2), p. 38–39.
   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;15(4):560–569.
   PubMed Web of Science ®Google Scholar
• Stefano M, Patrizia P, Mario A, et al. Robotic upper limb rehabilitation after acute stroke by NeReBot: evaluation of treatment costs. Biomed Res Int. 2014, 2014;2014:265634.
   PubMed Web of Science ®Google Scholar
• Zheng J, Shi P, Fan M, et al. Effects of passive and active training modes of upper-limb rehabilitation robot on cortical activation: a functional near-infrared spectroscopy study. NeuroReport. 2021;32(6):479–488.
   PubMed Web of Science ®Google Scholar
• Hidler J, Wisman W, Neckel N. Kinematic trajectories while walking within the lokomat robotic gait-orthosis. Clin Biomech. 2008;23(10):1251–1259.
   PubMed Web of Science ®Google Scholar
• Lum PS, Burgar CG, Shor PC. Evidence for improved muscle activation patterns after retraining of reaching movements with the MIME robotic system in subjects with post-stroke hemiparesis. IEEE Trans Neural Syst Rehabil Eng. 2004;12(2):186–194.
   PubMed Web of Science ®Google Scholar
• Lum PS, Burgar CG, Van der Loos M, et al. MIME robotic device for upper-limb neurorehabilitation in subacute stroke subjects: a follow-up study. J Rehabil Res Dev. 2006;43(5):631–642.
   PubMed Web of Science ®Google Scholar
• Arz G, Toth A. REHAROB: a project and a system for motion diagnosis and robotized physiotherapy delivery. Integration of Assistive Technology in the Information Age. 2001;9(1):93–100.
   Google Scholar
• Shi D, Zhang W, Zhang W, et al. A review on lower limb rehabilitation exoskeleton robots. Chin J Mech Eng. 2019;32(1):74.
   Web of Science ®Google Scholar
• Freivogel S, Mehrholz J, Husak-Sotomayor T, et al. 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 Inj. 2008;22(7-8):625–632.
   PubMed Web of Science ®Google Scholar
• Veneman JF, Kruidhof R, Hekman EEG, et al. Design and evaluation of the LOPES exoskeleton robot for interactive gait rehabilitation. IEEE Trans Neural Syst Rehabil Eng. 2007;15(3):379–386.
   PubMed Web of Science ®Google Scholar
• Meuleman J, van Asseldonk E, van Oort G, et al. LOPES II—design and evaluation of an admittance controlled gait training robot with shadow-leg approach. IEEE Trans Neural Syst Rehabil Eng. 2016;24(3):352–363.
   PubMed Web of Science ®Google Scholar
• Agrawal SK, Banala SK, Fattah A, et al. Assessment of motion of a swing leg and gait rehabilitation with a gravity balancing exoskeleton. IEEE Trans Neural Syst Rehabil Eng. 2007;15(3):410–420.
   PubMed Web of Science ®Google Scholar
• Jalgaonkar N, Kim A, Awtar S. Design of an ankle rehab robot with a compliant parallel kinematic mechanism. J Mech Rob. 2021;13(3):13.
   Google Scholar
• Gomez-Vargas D, Pinto-Betnal MJ, Ballén-Moreno F, et al. Therapy with T-FLEX ankle-exoskeleton for motor recovery: a case study with a stroke survivor. 2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob). 2020. 8(5),491–496.
   Google Scholar
• Hidayah R, Bishop L, Jin X, et al. Gait adaptation using a cable-driven active leg exoskeleton (C-ALEX) with post-stroke participants. IEEE Trans Neural Syst Rehabil Eng. 2020;28(9):1984–1993.
   PubMed Web of Science ®Google Scholar
• Jin X, Cui X, Agrawal SK. Design of a cable-driven active leg exoskeleton (C-ALEX) and gait training experiments with human subjects. 2015 Ieee International Conference on Robotics and Automation (ICRA). 2015. 3(2), p. 5578–5583.
   Google Scholar
• Girone M, Burdea G, Bouzit M, et al. A Stewart platform-based system for ankle telerehabilitation. Autonomous Robots. 2001;10(2):203–212.
   Web of Science ®Google Scholar
• Lim B, Jang J, Lee J, et al. Delayed output feedback control for gait assistance and resistance using a robotic exoskeleton. IEEE Rob Autom Lett. 2019;4(4):3521–3528.
   Web of Science ®Google Scholar
• Choi B, Lee Y, Lee J, et al. Development of adjustable knee assist device for wearable robot based on linkage and rolling joint. 2019 Ieee/Rsj International Conference on Intelligent Robots and Systems (IROS), 2019. 4043–4050.
   Google Scholar
• Kai Y, Kaneta A, Ikeda K, et al. Development of a new compact and light velocity-based mechanical safety device for a rehabilitation assist suit. J. Adv. Mech. Des. Syst. Manuf. 2020;14(7):20–00212.
   Web of Science ®Google Scholar
• Agarwal P, Deshpande AD. Subject-specific assist-as-needed controllers for a hand exoskeleton for rehabilitation. IEEE Rob Autom Lett. 2018;3(1):508–515.
   Web of Science ®Google Scholar
• Zeilig G, Weingarden H, Zwecker M, et al. Safety and tolerance of the ReWalkTM exoskeleton suit for ambulation by people with complete spinal cord injury: a pilot study. Journ Spinal Cord Med. 2012;35(2):96–101.
   PubMed Web of Science ®Google Scholar
• Esquenazi A, Talaty M, Packel A, et al. The ReWalk powered exoskeleton to restore ambulatory function to individuals with thoracic-level motor-complete spinal cord injury. Am J Phys Med Rehabil. 2012;91(11):911–921.
   PubMed Web of Science ®Google Scholar
• Zoss AB, Kazerooni H, Chu A. Biomechanical Design of the berkeley lower extremity exoskeleton (BLEEX). IEEE/ASME Trans Mechatronics. 2006;11(2):128–138.
   Web of Science ®Google Scholar
• Kazerooni H. Human Augmentation and exoskeleton systems in berkeley. Int. J. Human. Robot. 2007;04(03):575–605.
   Google Scholar
• Marconi D, Baldoni A, McKinney Z, et al. A Novel hand exoskeleton with series elastic actuation for modulated torque transfer. Mechatronics. 2019;61:69–82.
   Web of Science ®Google Scholar
• Bai S, Christensen S, Islam MRU. An upper-body exoskeleton with a novel shoulder mechanism for assistive applications 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), 2017. p. 1041–1046.
   Google Scholar
• Norris JA, Granata KP, Mitros MR, et al. Effect of augmented plantarflexion power on preferred walking speed and economy in young and older adults. Gait & Posture. 2007;25(4):620–627.
   PubMed Web of Science ®Google Scholar
• Bougrinat Y, Achiche S, Raison M. Design and development of a lightweight ankle exoskeleton for human walking augmentation. Mechatronics. 2019;64:102297.
   Web of Science ®Google Scholar
• Mooney LM, Herr HM. Biomechanical walking mechanisms underlying the metabolic reduction caused by an autonomous exoskeleton. J Neuroeng Rehab. 2016;13:4.
   PubMed Web of Science ®Google Scholar
• Bianchi M, Cempini M, Conti R, et al. Design of a series elastic transmission for hand exoskeletons. Mechatronics. 2018;51:8–18.
   Web of Science ®Google Scholar
• Hadi A, Alipour K, Kazeminasab S, et al. ASR glove: a wearable glove for hand assistance and rehabilitation using shape memory alloys. J Intell Mater Syst Struct. 2018;29(8):1575–1585.
   Web of Science ®Google Scholar
• Guo Y, Xu F, Song Y, et al. A soft robotic glove for hand rehabilitation using pneumatic actuators with variable stiffness. J Intell Robot Syst. 2019;15(2):119–129.
   Google Scholar
• Chen X, Gong L, Wei L, et al. A wearable hand rehabilitation system with soft gloves. IEEE Trans Ind Informat. 2021;17(2):943–952.
   Web of Science ®Google Scholar
• Awad LN, Esquenazi A, Francisco GE, et al. The ReWalk ReStore (TM) soft robotic exosuit: a multi-site clinical trial of the safety, reliability, and feasibility of exosuit-augmented post-stroke gait rehabilitation. j neuroeng rehab. 2020;17(1):80.
   PubMed Web of Science ®Google Scholar
• Bae J, Siviy C, Rouleau M, et al. A lightweight and efficient portable soft exosuit for paretic ankle assistance in walking After stroke 2018 IEEE International Conference on Robotics and Automation (ICRA). 2018. 2018:2820–2827.
   Google Scholar
• Yurkewich A, Hebert D, Wang RH, et al. Hand extension robot orthosis (HERO) glove: development and testing with stroke survivors with severe hand impairment. IEEE Trans Neural Syst Rehabil Eng. 2019;27(5):916–926.
   PubMed Web of Science ®Google Scholar
• Schabowsky CN, Godfrey SB, Holley RJ, et al. Development and pilot testing of HEXORR: hand EXOskeleton rehabilitation robot. J NeuroEng Rehabil. 2010;7(1):36.
   PubMedGoogle Scholar
• Varol HA, Sup F, Goldfarb M. Multiclass real-time intent recognition of a powered lower limb prosthesis. IEEE Trans Biomed Eng. 2010;57(3):542–551.
   PubMed Web of Science ®Google Scholar
• De Pauw K, Cherelle P, Roelands B, et al. The efficacy of the ankle mimicking prosthetic foot prototype 4.0 during walking: physiological determinants. Prosthet Orthot Int. 2018;42(5):504–510.
   PubMed Web of Science ®Google Scholar
• Cherelle P, Grosu V, Cestari M, et al. The AMP-Foot 3, new generation propulsive prosthetic feet with explosive motion characteristics: design and validation. Biomed Eng Online. 2016;15(Suppl 3):145.
   PubMedGoogle Scholar
• Cherelle P, Grosu V, Matthys A, et al. Design and validation of the ankle mimicking prosthetic (AMP-) foot 2.0. IEEE Trans Neural Syst Rehabil Eng. 2014;22(1):138–148.
   PubMed Web of Science ®Google Scholar
• Young AJ, Simon AM, Fey NP, et al. Intent recognition in a powered lower limb prosthesis using time history information. Ann. Biomed. Eng. 2014;42(3):631–641.
   PubMed Web of Science ®Google Scholar
• Aghili F, Haghpanahi M. Use of a pattern recognition technique to control a multifunctional prosthesis. Med Biol Eng Comput. 1995;33(3 Spec No):504–508.
   PubMed Web of Science ®Google Scholar
• Su B-Y, Wang J, Liu S-Q, et al. A CNN-based method for intent recognition using inertial measurement units and intelligent lower limb prosthesis. IEEE Trans Neural Syst Rehabil Eng. 2019;27(5):1032–1042.
   PubMed Web of Science ®Google Scholar
• Simon AM, Ingraham KA, Spanias JA, et al. Delaying ambulation mode transition decisions improves accuracy of a flexible control system for powered knee-ankle prosthesis. IEEE Trans Neural Syst Rehabil Eng. 2017;25(8):1164–1171.
   PubMed Web of Science ®Google Scholar
• Rampichini S, Vieira TM, Castiglioni P, et al. Complexity analysis of surface electromyography for assessing the myoelectric manifestation of muscle fatigue: a review. Entropy. 2020;22(5):529.
   PubMed Web of Science ®Google Scholar
• Fleischer C, Hommel G. A human–exoskeleton interface utilizing electromyography. IEEE Trans Robot. 2008;24(4):872–882.
   Web of Science ®Google Scholar
• Buchanan TS, Lloyd DG, Manal K, et al. Neuromusculoskeletal modeling: estimation of muscle forces and joint moments and movements from measurements of neural command. J Appl Biomech. 2004;20(4):367–395.
   PubMed Web of Science ®Google Scholar
• Hunt KJ, Munih M, Donaldson N, et al. Investigation of the hammerstein hypothesis in the modeling of electrically stimulated muscle. IEEE Trans Biomed Eng. 1998;45(8):998–1009.
   PubMed Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• Jiang N, Vujaklija I, Rehbaum H, et al. Is accurate mapping of EMG signals on kinematics needed for precise online myoelectric control? IEEE Trans Neural Syst Rehabil Eng. 2014;22(3):549–558.
   PubMed Web of Science ®Google Scholar
• Li QL, Song Y, Hou ZG. Estimation of lower limb periodic motions from SEMG using least squares support vector regression. Neural Process Lett. 2015;41(3):371–388.
   Web of Science ®Google Scholar
• Adewuyi AA, Hargrove LJ, Kuiken TA. Evaluating EMG feature and classifier selection for application to partial-hand prosthesis control. Front Neurorobot. 2016;10(2):15–15.
   PubMedGoogle Scholar
• Adewuyi AA, Hargrove LJ, Kuiken TA. Resolving the effect of wrist position on myoelectric pattern recognition control. J Neuroeng Rehab. 2017;14(1):39.
   PubMedGoogle Scholar
• Adewuyi AA, Hargrove LJ, Kuiken TA. An analysis of intrinsic and extrinsic hand muscle EMG for improved pattern recognition control. IEEE Trans Neural Syst Rehabil Eng. 2016;24(4):485–494.
   PubMed Web of Science ®Google Scholar
• Watanabe H, Marushima A, Kadone H, et al. Effects of gait treatment with a single-leg hybrid assistive limb system after acute stroke: a non-randomized clinical trial. Front Neurosci. 2019;13(2):1389.
   PubMedGoogle Scholar
• Tanaka H, Nankaku M, Nishikawa T, et al. Spatiotemporal gait characteristic changes with gait training using the hybrid assistive limb for chronic stroke patients. Gait & Posture. 2019;7(2):205–210.
   Google Scholar
• Kawamoto H, Lee S, Kanbe S, et al. Power assist method for HAL-3 using EMG-based feedback controller. 2003 IEEE International Conference on Systems, Man and Cybernetics, Vols 1–5, Conference Proceedings. 2003. 5(2): 1648–1653.
   Google Scholar
• McCabe JP, Henniger D, Perkins J, et al. Feasibility and clinical experience of implementing a myoelectric upper limb orthosis in the rehabilitation of chronic stroke patients: a clinical case series report. PLOS One. 2019;14(4):e0215311.
   PubMed Web of Science ®Google Scholar
• Kannape OA, Herr HM. Volitional control of ankle plantar flexion in a powered transtibial prosthesis during stair-ambulation. 2014 36th Annual International Conference of the Ieee Engineering in Medicine and Biology Society (EMBC). 2014. 2 (1):1662–1665.
   Google Scholar
• Kannape OA, Herr HM. Split-belt adaptation and gait symmetry in transtibial amputees walking with a hybrid EMG controlled ankle-foot prosthesis. 2016 38th Annual International Conference of the Ieee Engineering in Medicine and Biology Society (EMBC). 2016. 3(4):5469–5472.
   Google Scholar
• Wang J, Kannape OA, Herr HM. Proportional EMG control of ankle plantar flexion in a powered transtibial prosthesis. 2013 Ieee 13th international conference on rehabilitation robotics (ICORR). 2013;2013:1–5.
   Google Scholar
• Au S, Berniker M, Herr H. Powered ankle-foot prosthesis to assist level-ground and stair-descent gaits. Neural Netw. 2008;21(4):654–666.
   PubMed Web of Science ®Google Scholar
• Hargrove LJ, Simon AM, Lipschutz R, et al. Non-weight-bearing neural control of a powered transfemoral prosthesis. J Neuroeng Rehab. 2013;10(1):62.
   PubMedGoogle Scholar
• Huang S, Wensman JP, Ferris DP. An experimental powered lower limb prosthesis using proportional myoelectric control. J Med Devices. 2014;8(2):285–294.
   Google Scholar
• Huang S, Wensman JP, Ferris DP. Locomotor adaptation by transtibial amputees walking with an experimental powered prosthesis under continuous myoelectric control. IEEE Trans Neural Syst Rehabil Eng. 2016;24(5):573–581.
   PubMed Web of Science ®Google Scholar
• Chen B, Wang Q, Wang L. Adaptive slope walking with a robotic transtibial prosthesis based on volitional EMG control. IEEE/ASME Trans Mechatronics. 2015;20(5):2146–2157.
   Web of Science ®Google Scholar
• Chen B, Wang Q. Combining Human volitional control with intrinsic controller on robotic prosthesis: a case study on adaptive slope walking. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2015. 61(2):4777–4780.
   Google Scholar
• Thatte N, Geyer H. Toward balance recovery with leg prostheses using neuromuscular model control. IEEE Trans Biomed Eng. 2016;63(5):904–913.
   PubMed Web of Science ®Google Scholar
• Thatte N, Geyer H. Towards local reflexive control of a powered transfemoral prosthesis for robust amputee push and trip recovery. 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. 2014. 5(4): 2069–2074.
   Google Scholar
• Zhang C, Ai Q, Meng W, et al. A subject-specific EMG-driven musculoskeletal model for the estimation of moments in ankle plantar-dorsiflexion movement. NeuralIPS. 2017;61(3):685–693.
   Google Scholar
• Ghazaei G, Alameer A, Degenaar P, et al. Deep learning-based artificial vision for grasp classification in myoelectric hands. J. Neural Eng. 2017;14(3):25–36.
   Web of Science ®Google Scholar
• Zhan G, Chen S, Ji Y, et al. EEG-based brain network analysis of chronic stroke patients after BCI rehabilitation training. Front Hum Neurosci. 2022;16:1-16.
   Web of Science ®Google Scholar
• Pfurtscheller G, Aranibar A. Evaluation of event-related desynchronization (ERD) preceding and following voluntary self-paced movement. Electroencephalogr Clin Neurophysiol. 1979;46(2):138–146.
   PubMedGoogle Scholar
• Chapin JK, Moxon KA, Markowitz RS, et al. Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex. Nat Neurosci. 1999;2(7):664–670.
   PubMed Web of Science ®Google Scholar
• Daly JJ, Wolpaw JR. Brain–computer interfaces in neurological rehabilitation. Lancet Neurol. 2008;7(11):1032–1043.
   PubMed Web of Science ®Google Scholar
• Donati ARC, Shokur S, Morya E, et al. Long-term training with a brain-machine interface-based gait protocol induces partial neurological recovery in paraplegic patients. Sci Rep. 2016;6(1):30383.
   PubMedGoogle Scholar
• Kalcher J, Flotzinger D, Pfurtscheller G. A new approach to a brain–computer-interface (BCI) based on learning vector quantization (LVQ3). 1992 14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 1992. 4 (4):1658–1659.
   Google Scholar
• Roberts SJ, Penny WD. Real-time brain–computer interfacing: a preliminary study using bayesian learning. Med Biol Eng Comput. 2000;38(1):56–61.
   PubMed Web of Science ®Google Scholar
• Pfurtscheller G, Flotzinger D, Kalcher J. Brain–computer interface—a new communication device for handicapped persons. J Microcomput Appl. 1993;16(3):293–299.
   Google Scholar
• Antelis JM, Gudiño-Mendoza B, Falcón LE, et al. Dendrite morphological neural networks for motor task recognition from electroencephalographic signals. Biomed Signal Process Control. 2018;44:12–24.
   Web of Science ®Google Scholar
• Frisoli A, Loconsole C, Leonardis D, et al. A new gaze-bci-driven control of an upper limb exoskeleton for rehabilitation in real-world tasks. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews). 2012;42(6):1169–1179.
   PubMed Web of Science ®Google Scholar
• Mueller-Putz G, Scherer R, Pfurtscheller G, et al. Temporal coding of brain patterns for direct limb control in humans. Front Neurosci. 2010;4:34.
   PubMed Web of Science ®Google Scholar
• Lotze M, Braun C, Birbaumer N, et al. Motor learning elicited by voluntary drive. Brain. 2003;126(Pt 4):866–872.
   PubMedGoogle Scholar
• Fragkiadaki K, Levine S, Felsen P, et al. Recurrent network models for human dynamics. 2015 IEEE International Conference on Computer Vision (ICCV). 2015. 3 (2):4346–4354.
   Google Scholar
• Zunino A, Cavazza J, Koul A, et al. Predicting human intentions from motion cues only: a 2D + 3D fusion approach. Proceedings of the 25th ACM international conference on Multimedia. 2017. 6 (4):591–599.
   Google Scholar
• Liu R, Liu C. Human motion prediction using adaptable recurrent neural networks and inverse kinematics. 2021 American Control Conference (ACC). 2021;3(4):3725–3730.
   Google Scholar
• Cavallo A, Koul A, Ansuini C, et al. Decoding intentions from movement kinematics. Sci Rep. 2016;6(1):37036.
   PubMedGoogle Scholar
• Wei P, Xie R, Tang R, et al. SEMG-based gait phase recognition for children with spastic cerebral palsy. Ann Biomed Eng. 2019;47(1):223–230.
   PubMed Web of Science ®Google Scholar
• Duan F, Dai L. Recognizing the gradual changes in SEMG characteristics based on incremental learning of wavelet neural network ensemble. IEEE Trans Ind Electron. 2017;64(5):4276–4286.
   Web of Science ®Google Scholar
• Duan F, Dai L, Chang W, et al. SEMG-based identification of hand motion commands using wavelet neural network combined with discrete wavelet transform. IEEE Trans Ind Electron. 2016;63(3):1923–1934.
   Web of Science ®Google Scholar
• Sun X, Zhang X, Lu Z, et al. SEMG-based continuous estimation of wrist joint angle using BP neural network In 2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), 2019. 63 (3), p. 221–225.
   Google Scholar
• Manal K, Gonzalez RV, Lloyd DG, et al. A Real-time EMG-driven virtual arm. Comput Biol Med. 2002;32(1):25–36.
   PubMed Web of Science ®Google Scholar
• Liu M, Peng B, Shang M. Lower limb movement intention recognition for rehabilitation robot aided with projected recurrent neural network. Complex Intell. Syst. 2021;4(1):23–29.
   Google Scholar
• Xu D, Wu Q, Zhu Y. Development of a SEMG-based joint torque estimation strategy using hill-type muscle model and neural network. J. Med. Biol. Eng. 2021;41(1):34–44.
   Web of Science ®Google Scholar
• Perreault EJ, Sandercock TG, Heckman CJ. Hill muscle model performance during natural activation and electrical stimulation. 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2001.2: 1248–1251.
   Google Scholar
• Venture G, Yamane K, Nakamura Y. Identifying musculo-tendon parameters of human body based on the musculo-skeletal dynamics computation and Hill-Stroeve muscle model. 2005 5th Ieee-Ras International Conference on Humanoid Robots. 2005. 5:351–356.
   Google Scholar
• Stamatescu G, White LB, Bruce-Doust R. Track extraction with hidden reciprocal chains. IEEE Trans Autom Control. 2018;63(4):1097–1104.
   Web of Science ®Google Scholar
• Ding QC, Xiong AB, Zhao XG, et al. A Novel EMG-Driven state space model for the estimation of continuous joint movements 2011 IEEE International Conference on Systems, Man, and Cybernetics, 2011. p. 2891–2897.
   Google Scholar
• Han J, Ding Q, Xiong A, et al. A state-space EMG model for the estimation of continuous joint movements. IEEE Trans Ind Electron. 2015;62(7):4267–4275.
   Web of Science ®Google Scholar
• Tsinganos P, Cornelis B, Cornelis J, et al Improved gesture recognition based on SEMG signals and TCN. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019.2019:1169–1173.
   Google Scholar
• Xia P, Hu J, Peng Y. EMG-based estimation of limb movement using deep learning with recurrent convolutional neural networks. Artif Organs. 2018;42(5):E67–E77.
   PubMed Web of Science ®Google Scholar
• Boonstra T. The potential of corticomuscular and intermuscular coherence for research on human motor control. Front Hum Neurosci. 2013;7:1–2.
   PubMed Web of Science ®Google Scholar
• Guo Z, Zhou S, Ji K, et al. Corticomuscular integrated representation of voluntary motor effort in robotic control for wrist-hand rehabilitation after stroke. J. Neural Eng. 2022;19(2):026004.
   Web of Science ®Google Scholar
• Mrachacz-Kersting N, Jiang N, Stevenson AJT, et al. Efficient neuroplasticity induction in chronic stroke patients by an associative brain–computer interface. J Neurophysiol. 2016;115(3):1410–1421.
   PubMed Web of Science ®Google Scholar
• Ge S, Wang R, Leng Y, et al. A double-partial least-squares model for the detection of steady-state visual evoked potentials. IEEE J Biomed Health Inform. 2017;21(4):897–903.
   PubMed Web of Science ®Google Scholar
• Leeb R, Lee F, Keinrath C, et al. Brain–computer communication: motivation, aim, and impact of exploring a virtual apartment. IEEE Trans Neural Syst Rehabil Eng. 2007;15(4):473–482.
   PubMed Web of Science ®Google Scholar
• Kiguchi K, Lalitharatne TD, Hayashi Y. Estimation of forearm supination/pronation motion based on EEG signals to control an artificial arm. JAdv Mech Des, Syst Manuf. 2013;7(1):74–81.
   Web of Science ®Google Scholar
• Kiguchi K, Hayashi Y. Motion estimation based on EMG and EEG signals to control wearable robots. 2013 IEEE International Conference on Systems, Man, and Cybernetics2013. p. 4213–4218.
   Google Scholar
• Shafiul Hasan SM, Siddiquee MR, Atri R, et al. Prediction of gait intention from pre-movement EEG signals: a feasibility study. J Neuroeng Rehab. 2020;17(1):50.
   PubMed Web of Science ®Google Scholar
• Yang D, Nguyen T-H, Chung W-Y. A synchronized hybrid brain-computer interface system for simultaneous detection and classification of fusion EEG signals. Complexity. 2020; e4137283:1-11.
   Web of Science ®Google Scholar
• Sivasankari K, Thanushkodi K. An improved EEG signal classification using neural network with the consequence of Ica and STFT. J Electr Eng Technol. 2014;9(3):1060–1071.
   Web of Science ®Google Scholar
• Lemm S, Blankertz B, Curio G, et al. Spatio-spectral filters for improving the classification of single trial EEG. IEEE Trans Biomed Eng. 2005;52(9):1541–1548.
   PubMed Web of Science ®Google Scholar
• Wang W, Zhang J, Wang X, et al. Motion intensity modeling and trajectory control of upper limb rehabilitation exoskeleton robot based on multi-modal information. Complex Intell. Syst. 2022;8(3):2091–2103.
   Web of Science ®Google Scholar
• Wendong W, Hanhao L, Menghan X, et al. Design and verification of a human–robot interaction system for upper limb exoskeleton rehabilitation. Med Eng Phys. 2020;79:19–25.
   PubMed Web of Science ®Google Scholar
• Colamarino E, de Seta V, Masciullo M, et al. Corticomuscular and intermuscular coupling in simple hand movements to enable a hybrid brain–computer interface. Int. J. Neur. Syst. 2021;31(11):2150052.
   PubMed Web of Science ®Google Scholar
• Kitisomprayoonkul W, Bhodhiassana P, Sangveraphunsiri V. Upper extremity training with CUREs robot in subacute stroke: a pilot study. In Converging Clinical and Engineering Research on Neurorehabilitation II. 2017;10(2):317–322.
   Google Scholar
• Zheng E, Chen B, Wei K, et al. Lower limb wearable capacitive sensing and its applications to recognizing human gaits. Sensors. 2013;13(10):13334–13355.
   PubMed Web of Science ®Google Scholar
• Zheng E, Wang L, Wei K, et al. A noncontact capacitive sensing system for recognizing locomotion modes of transtibial amputees. IEEE Trans Biomed Eng. 2014;61(12):2911–2920.
   PubMed Web of Science ®Google Scholar
• Chen B, Zheng E, Wang Q. A locomotion intent prediction system based on multi-sensor fusion. Sensors. 2014;14(7):12349–12369.
   PubMed Web of Science ®Google Scholar
• Park U, Mallipeddi R, Lee M. Human implicit intent discrimination using EEG and eye movement. Neural Information Processing. 2014;7(3):11–18.
   Google Scholar