Use of Robotic Devices in Post-Stroke Rehabilitation

This review addresses the use of robotic devices in the rehabilitation of poststroke and posttrauma patients as a rehabilitation technology which has developed rapidly over the last decade. The types of devices used are described – manipulators and exoskeletons – along with the clinical protocols for their use and the effectiveness of rehabilitation procedures. Particular attention is paid to the neurophysiological basis of the rehabilitation potential of this technology, including analysis of measures of plastic rearrangements of the brain. Results obtained from state-of-the-art rehabilitation technology using hand exoskeletons controlled by brain–computer interfaces based on kinesthetic motor imagery are considered.

[1]  T. Milner,et al.  HandCARE: A Cable-Actuated Rehabilitation System to Train Hand Function After Stroke , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[2]  J. Patton,et al.  Evaluation of robotic training forces that either enhance or reduce error in chronic hemiparetic stroke survivors , 2005, Experimental Brain Research.

[3]  Cuntai Guan,et al.  A Large Clinical Study on the Ability of Stroke Patients to Use an EEG-Based Motor Imagery Brain-Computer Interface , 2011, Clinical EEG and neuroscience.

[4]  J. Patton,et al.  Functional Restoration for the Stroke Survivor: Informing the Efforts of Engineers , 2008, Topics in stroke rehabilitation.

[5]  K. Y. Tong,et al.  An EMG-driven exoskeleton hand robotic training device on chronic stroke subjects: Task training system for stroke rehabilitation , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.

[6]  N. Hogan,et al.  Robot-aided neurorehabilitation. , 1998, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[7]  S. Hesse,et al.  Robot-assisted arm trainer for the passive and active practice of bilateral forearm and wrist movements in hemiparetic subjects. , 2003, Archives of physical medicine and rehabilitation.

[8]  Alexander A. Frolov,et al.  On the possibility of linear modelling the human arm neuromuscular apparatus , 2000, Biological Cybernetics.

[9]  Dean J Krusienski,et al.  Brain-computer interfaces in medicine. , 2012, Mayo Clinic proceedings.

[10]  D. Reinkensmeyer,et al.  Review of control strategies for robotic movement training after neurologic injury , 2009, Journal of NeuroEngineering and Rehabilitation.

[11]  R. Oostenveld,et al.  Independent EEG Sources Are Dipolar , 2012, PloS one.

[12]  A. Fugl-Meyer,et al.  The post-stroke hemiplegic patient. 1. a method for evaluation of physical performance. , 1975, Scandinavian journal of rehabilitation medicine.

[13]  S. Kirker,et al.  A new electromechanical trainer for sensorimotor rehabilitation of paralysed fingers: A case series in chronic and acute stroke patients , 2008, Journal of NeuroEngineering and Rehabilitation.

[14]  P. Jackson,et al.  The neural network of motor imagery: An ALE meta-analysis , 2013, Neuroscience & Biobehavioral Reviews.

[15]  P. Cordo,et al.  Assisted Movement With Enhanced Sensation (AMES): Coupling Motor and Sensory to Remediate Motor Deficits in Chronic Stroke Patients , 2009, Neurorehabilitation and neural repair.

[16]  Daniel P Ferris,et al.  The exoskeletons are here , 2009, Journal of NeuroEngineering and Rehabilitation.

[17]  Roberta Klatzky,et al.  Visual feedback distortion in a robotic environment for hand rehabilitation , 2008, Brain Research Bulletin.

[18]  E. Rocon,et al.  Locomotor training through a novel robotic platform for gait rehabilitation in pediatric population: short report , 2016, Journal of NeuroEngineering and Rehabilitation.

[19]  R. A. Bos,et al.  A structured overview of trends and technologies used in dynamic hand orthoses , 2016, Journal of NeuroEngineering and Rehabilitation.

[20]  F. L. D. Silva,et al.  Event-related EEG/MEG synchronization and desynchronization: basic principles , 1999, Clinical Neurophysiology.

[21]  S. Makeig,et al.  Imaging human EEG dynamics using independent component analysis , 2006, Neuroscience & Biobehavioral Reviews.

[22]  L. Chernikova,et al.  Brain–computer interface: The first experience of clinical use in Russia , 2016, Human Physiology.

[23]  Effie Chew,et al.  Is Motor‐Imagery Brain‐Computer Interface Feasible in Stroke Rehabilitation? , 2014, PM & R : the journal of injury, function, and rehabilitation.

[24]  S. Leonhardt,et al.  A survey on robotic devices for upper limb rehabilitation , 2014, Journal of NeuroEngineering and Rehabilitation.

[25]  Jiping He,et al.  RUPERT: An exoskeleton robot for assisting rehabilitation of arm functions , 2008, 2008 Virtual Rehabilitation.

[26]  Domenico Formica,et al.  Modulation of brain plasticity in stroke: a novel model for neurorehabilitation , 2014, Nature Reviews Neurology.

[27]  T. Elbert,et al.  New treatments in neurorehabiliation founded on basic research , 2002, Nature Reviews Neuroscience.

[28]  J. Liu,et al.  Monitoring functional arm movement for home-based therapy after stroke , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[29]  S. Park,et al.  Feedback equilibrium control during human standing , 2005, Biological Cybernetics.

[30]  Rahsaan J. Holley,et al.  Development and pilot testing of HEXORR: Hand EXOskeleton Rehabilitation Robot , 2010, Journal of NeuroEngineering and Rehabilitation.

[31]  O. G. Pavlova,et al.  [Arm Motor Function Recovery during Rehabilitation with the Use of Hand Exoskeleton Controlled by Brain-Computer Interface: a Patient with Severe Brain Damage]. , 2016, Fiziologiia cheloveka.

[32]  F. Netter Atlas of Human Anatomy , 1967 .

[33]  Marc W Slutzky,et al.  Reducing Abnormal Muscle Coactivation After Stroke Using a Myoelectric-Computer Interface , 2014, Neurorehabilitation and neural repair.

[34]  Rieko Osu,et al.  Trajectory formation based on the minimum commanded torque change model using the Euler–Poisson equation , 2005 .

[35]  E. Bizzi,et al.  Arm trajectory formation in monkeys , 2004, Experimental Brain Research.

[36]  C. Häger,et al.  Kinematic analysis of the upper extremity after stroke – how far have we reached and what have we grasped? , 2015 .

[37]  T. Flash,et al.  Moving gracefully: quantitative theories of motor coordination , 1987, Trends in Neurosciences.

[38]  W. Rymer,et al.  Understanding and treating arm movement impairment after chronic brain injury: progress with the ARM guide. , 2014, Journal of rehabilitation research and development.

[39]  C. Braun,et al.  Motor learning elicited by voluntary drive. , 2003, Brain : a journal of neurology.

[40]  Cuntai Guan,et al.  Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke , 2014, Front. Neuroeng..

[41]  L. Pignolo,et al.  Upper limb rehabilitation after stroke: ARAMIS a “robo-mechatronic” innovative approach and prototype , 2012, 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob).

[42]  J. Ushiba,et al.  Event-related desynchronization reflects downregulation of intracortical inhibition in human primary motor cortex. , 2013, Journal of neurophysiology.

[43]  C. Burgar,et al.  MIME robotic device for upper-limb neurorehabilitation in subacute stroke subjects: A follow-up study. , 2006, Journal of rehabilitation research and development.

[44]  Staci McKay,et al.  Constraint-induced movement therapy for recovery of upper-limb function following traumatic brain injury. , 2005, Journal of rehabilitation research and development.

[45]  P. Bach-y-Rita,et al.  Computer-Assisted Motivating Rehabilitation (CAMR) for Institutional, Home, and Educational Late Stroke Programs , 2002, Topics in stroke rehabilitation.

[46]  Dušan Húsek,et al.  Sources of Electrical Brain Activity Most Relevant to Performance of Brain-Computer Interface Based on Motor Imagery , 2013 .

[47]  Gabor Fazekas,et al.  A novel robot training system designed to supplement upper limb physiotherapy of patients with spastic hemiparesis , 2006, International journal of rehabilitation research. Internationale Zeitschrift fur Rehabilitationsforschung. Revue internationale de recherches de readaptation.

[48]  W. Klimesch,et al.  EEG alpha oscillations: The inhibition–timing hypothesis , 2007, Brain Research Reviews.

[49]  C. Stinear,et al.  Prediction of recovery of motor function after stroke , 2010, The Lancet Neurology.

[50]  M. Merzenich,et al.  Use-dependent alterations of movement representations in primary motor cortex of adult squirrel monkeys , 1996, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[51]  P. Comon,et al.  Ica: a potential tool for bci systems , 2008, IEEE Signal Processing Magazine.

[52]  L. Der-Yeghiaian,et al.  Robot-based hand motor therapy after stroke. , 2007, Brain : a journal of neurology.

[53]  P. Bach-y-Rita Theoretical and Practical Considerations in the Restoration of Function After Stroke , 2001, Topics in stroke rehabilitation.

[54]  W. Harwin,et al.  Multivariate analysis of the Fugl-Meyer outcome measures assessing the effectiveness of GENTLE/S robot-mediated stroke therapy , 2007, Journal of NeuroEngineering and Rehabilitation.

[55]  J. Krakauer,et al.  The interaction between training and plasticity in the poststroke brain. , 2013, Current opinion in neurology.

[56]  W. Klimesch Alpha-band oscillations, attention, and controlled access to stored information , 2012, Trends in Cognitive Sciences.

[57]  H.I. Krebs,et al.  Design, Characterization, and Impedance Limits of a Hand Robot , 2007, 2007 IEEE 10th International Conference on Rehabilitation Robotics.

[58]  Mitsuo Kawato,et al.  Equilibrium-Point Control Hypothesis Examined by Measured Arm Stiffness During Multijoint Movement , 1996, Science.

[59]  G. Kwakkel,et al.  Effects of robotic therapy of the arm after stroke , 2014, Lancet Neurology.

[60]  Silvestro Micera,et al.  A Simple Robotic System for Neurorehabilitation , 2005, Auton. Robots.

[61]  A. Frolov,et al.  Brain Computer Interface Enhancement by Independent Component Analysis , 2011, IHCI.

[62]  Silvestro Micera,et al.  MUNDUS project: MUltimodal Neuroprosthesis for daily Upper limb Support , 2013, Journal of NeuroEngineering and Rehabilitation.

[63]  G. Pfurtscheller,et al.  Patterns of cortical activation during planning of voluntary movement. , 1989, Electroencephalography and clinical neurophysiology.

[64]  G. Gini,et al.  An EMG-controlled exoskeleton for hand rehabilitation , 2005, 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005..

[65]  Frans C. T. van der Helm,et al.  Dampace: Design of an Exoskeleton for Force-Coordination Training in Upper-Extremity Rehabilitation , 2009 .

[66]  N. Hogan,et al.  Customized interactive robotic treatment for stroke: EMG-triggered therapy , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[67]  M. Bergamasco,et al.  Arm rehabilitation with a robotic exoskeleleton in Virtual Reality , 2007, 2007 IEEE 10th International Conference on Rehabilitation Robotics.

[68]  E. Biryukova,et al.  Principles of motor recovery in post-stroke patients using hand exoskeleton controlled by the brain-computer interface based on motor imagery , 2017 .

[69]  M. Jeannerod The representing brain: Neural correlates of motor intention and imagery , 1994, Behavioral and Brain Sciences.

[70]  E. Schneider,et al.  Real-time computer-based visual feedback improves visual acuity in downbeat nystagmus – a pilot study , 2016, Journal of NeuroEngineering and Rehabilitation.

[71]  Gert Pfurtscheller,et al.  EEG event-related desynchronization (ERD) and synchronization (ERS) , 1997 .

[72]  S. Wolf,et al.  A pneumatic muscle hand therapy device , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[73]  J. Decety,et al.  Functional anatomy of execution, mental simulation, observation, and verb generation of actions: A meta‐analysis , 2001, Human brain mapping.

[74]  M. Jeannerod,et al.  Mental imaging of motor activity in humans , 1999, Current Opinion in Neurobiology.

[75]  Akio Kimura,et al.  Brain-computer interface with somatosensory feedback improves functional recovery from severe hemiplegia due to chronic stroke , 2014, Front. Neuroeng..

[76]  Fethi Ben Ouezdou,et al.  Adjustment of the human arm viscoelastic properties to the direction of reaching , 2006, Biological Cybernetics.

[77]  Л.А. Черникова,et al.  ОСНОВАННЫЙ НА ВООБРАЖЕНИИ ДВИЖЕНИЯ ИНТЕРФЕЙС МОЗГ – КОМПЬЮТЕР В РЕАБИЛИТАЦИИ ПАЦИЕНТОВ С ГЕМИПАРЕЗОМ , 2013 .

[78]  Robert Riener,et al.  ARMin - Exoskeleton Robot for Stroke Rehabilitation , 2009 .

[79]  Tzyy-Ping Jung,et al.  Independent Component Analysis of Electroencephalographic Data , 1995, NIPS.

[80]  Rieko Osu,et al.  Trajectory formation based on the minimum commanded torque change model using the Euler-Poisson equation , 2005, Systems and Computers in Japan.

[81]  A. Frolov,et al.  Sources of electrophysiological and foci of hemodynamic brain activity most relevant for controlling a hybrid brain–Computer interface based on classification of EEG patterns and near-infrared spectrography signals during motor imagery , 2016, Human Physiology.

[82]  N. Birbaumer,et al.  Resting State Changes in Functional Connectivity Correlate With Movement Recovery for BCI and Robot-Assisted Upper-Extremity Training After Stroke , 2013, Neurorehabilitation and neural repair.

[83]  Robert Riener,et al.  Robot-aided neurorehabilitation of the upper extremities , 2005, Medical and Biological Engineering and Computing.

[84]  Alexander A. Frolov,et al.  Closed-loop and open-loop control of posture and movement during human trunk bending , 2011, Biological Cybernetics.

[85]  M. Chen,et al.  An intention driven hand functions task training robotic system , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[86]  R. Peterka Sensorimotor integration in human postural control. , 2002, Journal of neurophysiology.

[87]  Alireza Gharabaghi,et al.  Brain state-dependent robotic reaching movement with a multi-joint arm exoskeleton: combining brain-machine interfacing and robotic rehabilitation , 2015, Front. Hum. Neurosci..

[88]  L. Cohen,et al.  Brain–machine interfaces in neurorehabilitation of stroke , 2015, Neurobiology of Disease.

[89]  L. Cohen,et al.  Brain–machine interface in chronic stroke rehabilitation: A controlled study , 2013, Annals of neurology.

[90]  Moritz Grosse-Wentrup,et al.  Using brain–computer interfaces to induce neural plasticity and restore function , 2011, Journal of neural engineering.

[91]  D.J. Reinkensmeyer,et al.  Control of a Pneumatic Orthosis for Upper Extremity Stroke Rehabilitation , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[92]  M. Reding,et al.  Effect of Lesion Location on Upper Limb Motor Recovery After Stroke , 2001, Stroke.

[93]  Soo-Jin Lee,et al.  Current hand exoskeleton technologies for rehabilitation and assistive engineering , 2012 .

[94]  S. Black,et al.  The Fugl-Meyer Assessment of Motor Recovery after Stroke: A Critical Review of Its Measurement Properties , 2002, Neurorehabilitation and neural repair.

[95]  Cuntai Guan,et al.  A Randomized Controlled Trial of EEG-Based Motor Imagery Brain-Computer Interface Robotic Rehabilitation for Stroke , 2015, Clinical EEG and neuroscience.

[96]  G.C. Burdea,et al.  Virtual reality-enhanced stroke rehabilitation , 2001, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[97]  Dušan Húsek,et al.  Sources of EEG activity most relevant to performance of brain-computer interface based on motor imagery , 2012 .

[98]  J. Massion,et al.  Forearm postural control during unloading: anticipatory changes in elbow stiffness , 1999, Experimental Brain Research.

[99]  P. Gallina,et al.  Design, Implementation and Clinical Tests of a Wire-Based Robot for Neurorehabilitation , 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[100]  Eberhard E. Fetz,et al.  Sequential activation of premotor, primary somatosensory and primary motor areas in humans during cued finger movements , 2015, Clinical Neurophysiology.

[101]  Grant D. Huang,et al.  Robot-assisted therapy for long-term upper-limb impairment after stroke. , 2010, The New England journal of medicine.

[102]  Hyung-Soon Park,et al.  Developing a whole-arm exoskeleton robot with hand opening and closing mechanism for upper limb stroke rehabilitation , 2009, 2009 IEEE International Conference on Rehabilitation Robotics.

[103]  L Saltuari,et al.  [ARMOR: an electromechanical robot for upper limb training following stroke. A prospective randomised controlled pilot study]. , 2008, Handchirurgie, Mikrochirurgie, plastische Chirurgie : Organ der Deutschsprachigen Arbeitsgemeinschaft fur Handchirurgie : Organ der Deutschsprachigen Arbeitsgemeinschaft fur Mikrochirurgie der Peripheren Nerven und Gefasse : Organ der V....

[104]  M. Levin Interjoint coordination during pointing movements is disrupted in spastic hemiparesis. , 1996, Brain : a journal of neurology.

[105]  O. G. Pavlova,et al.  [Rehabilitation of post stroke patients using a bioengineering system "brain-computer interface + exoskeleton"]. , 2014, Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova.

[106]  Khairul Anam,et al.  Active Exoskeleton Control Systems: State of the Art , 2012 .

[107]  Ethan R. Buch,et al.  Think to Move: a Neuromagnetic Brain-Computer Interface (BCI) System for Chronic Stroke , 2008, Stroke.