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Background Effectiveness of robotic therapy and transcranial direct current stimulation is conventionally assessed with clinical measures. Robotic metrics may be more objective and sensitive for measuring the efficacy of interventions on stroke survivor’s motor recovery. This study investigated if robotic metrics detect a difference in outcomes, not seen in clinical measures, in a study of transcranial direct current stimulation (tDCS) preceding robotic therapy. Impact of impairment severity on intervention response was also analyzed to explore optimization of outcomes by targeting patient sub-groups. Methods This 2020 study analyzed data from a double-blind, sham-controlled, randomized multi-center trial conducted from 2012 to 2016, including a six-month follow-up. 82 volunteers with single chronic ischemic stroke and right hemiparesis received anodal tDCS or sham stimulation, prior to robotic therapy. Robotic therapy involved 1024 repetitions, alternating shoulder-elbow and wrist robots, for a total of 36 sessions. Shoulder-elbow and wrist kinematic and kinetic metrics were collected at admission, discharge, and follow-up. Results No difference was detected between the tDCS or sham stimulation groups in the analysis of robotic shoulder-elbow or wrist metrics. Significant improvements in all metrics were found for the combined group analysis. Novel wrist data showed smoothness significantly improved (P < ·001) while submovement number trended down, overlap increased, and interpeak interval decreased. Post-hoc analysis showed only patients with severe impairment demonstrated a significant difference in kinematics, greater for patients receiving sham stimulation. Conclusions Robotic data confirmed results of clinical measures, showing intensive robotic therapy is beneficial, but no additional gain from tDCS. Patients with severe impairment did not benefit from the combined intervention. Wrist submovement characteristics showed a delayed pattern of motor recovery compared to the shoulder-elbow, relevant to intensive intervention-related recovery of upper extremity function in chronic stroke. Trial registration http://www.clinicaltrials.gov. Actual study start date September 2012. First registered on 15 November 2012. Retrospectively registered. Unique identifiers: NCT01726673 and NCT03562663.

Background A detailed sensorimotor evaluation is essential in planning effective, individualized therapy post-stroke. Robotic kinematic assay may offer better accuracy and resolution to understand stroke recovery. Here we investigate the added value of distal wrist measurement to a proximal robotic kinematic assay to improve its correlation with clinical upper extremity measures in chronic stroke. Secondly, we compare linear and nonlinear regression models. Methods Data was sourced from a multicenter randomized controlled trial conducted from 2012 to 2016, investigating the combined effect of robotic therapy and transcranial direct current stimulation (tDCS). 24 kinematic metrics were derived from 4 shoulder-elbow tasks and 35 metrics from 3 wrist and forearm evaluation tasks. A correlation-based feature selection was performed, keeping only features substantially correlated with the target attribute (R > 0.5.) Nonlinear models took the form of a multilayer perceptron neural network: one hidden layer and one linear output. Results Shoulder-elbow metrics showed a significant correlation with the Fugl Meyer Assessment (upper extremity, FMA-UE), with a R = 0.82 (P P P P P P Conclusions Distal wrist kinematics were highly correlated to clinical outcomes, warranting future investigation to explore our nonlinear wrist model with acute or subacute stroke populations. Trial registration http://www.clinicaltrials.gov. Actual study start date September 2012. First registered on 15 November 2012. Retrospectively registered. Unique identifiers: NCT01726673 and NCT03562663.

Supplemental material, EULT_manuals_070220 for Evaluation of the enhanced upper limb therapy programme within the Robot-Assisted Training for the Upper Limb after Stroke trial: descriptive analysis of intervention fidelity, goal selection and goal achievement by Helen Bosomworth, Helen Rodgers, Lisa Shaw, Leanne Smith, Lydia Aird, Denise Howel, Nina Wilson, Natasha Alvarado, Sreeman Andole, David L Cohen, Jesse Dawson, Cristina Fernandez-Garcia, Tracy Finch, Gary A Ford, Richard Francis, Steven Hogg, Niall Hughes, Christopher I Price, Laura Ternent, Duncan L Turner, Luke Vale, Scott Wilkes, Hermano I Krebs and Frederike van Wijck in Clinical Rehabilitation

Robotic technology can provide innovative responses to the severe challenges of providing cost-effective care to restore sensory-motor function following neurological and biomechanical injury. It may be deployed at several points on a continuum of care, to provide precisely controlled sensory-motor therapy to ameliorate disability and promote recovery of function, or to provide assistance to compensate for functions that cannot be recovered, or to replace limbs lost irretrievably. This chapter reviews recent progress using robotic technology to capitalize on neural plasticity and promote recovery after neurological injury such as stroke (cerebral vascular accident), research on brain-computer interfaces as a source of control signals for assistive technologies, and research on high-performance multiple-degree-of-freedom upper-extremity prosthetic limbs.

Twelve moderately to severely involved chronic stroke survivors (12 mo) were randomized to one of two treatments: robotics and motor learning (ROB-ML) or functional neuromuscular stimulation and motor learning (FNS-ML). Treatment was 5 h/d, 5 d/wk for 12 wk. ROB-ML group had 1.5 h per session devoted to robotics shoulder and elbow (S/E) training. FNS-ML had 1.5 h per session devoted to functional neuromuscular stimulation (surface electrodes) for wrist and hand (W/H) flexors/extensors. The primary outcome measure was the functional measure Arm Motor Ability Test (AMAT). Secondary measures were AMAT-S/E and AMAT-W/H, Fugl-Meyer (FM) upper-limb coordination, and the motor control measures of target accuracy (TA) and smoothness of movement (SM). ROB-ML produced significant gains in AMAT, AMAT-S/E, FM upper-limb coordination, TA, and SM. FNS-ML produced significant gains in AMAT-W/H and FM upper-limb coordination.

This article reviews a decade of work aimed at developing effective interactive robotic tools to treat and understand motor impairment and disability. The success of an initial pilot study with acute-phase in-patients recovering from stroke prompted a larger study showing that these results could be replicated and a follow-up study showing that the benefits lasted. Studies of chronic-phase out-patients demonstrated that similar benefits could be obtained which also lasted and were accompanied by a concomitant reduction of pain. Exploration of the likely biology of recovery suggested an improvement of robotic treatment in the form of performance-based progressive therapy aimed at accelerating a process akin to motor learning postulated to underlie recovery. Initial studies of this method show a dramatic improvement over the previous successes. Kinematic studies of the recovery process show that, similar to the development of motor behavior in infants, it begins with stereotyped submovements and proceeds by progressively merging these to approach unimpaired motor performance.

We are applying robotics and information technology to assist, enhance, and quantify neuro-rehabilitation. Our goal is a new class of interactive, user-affectionate clinical devices designed not only for evaluating patients, but also for delivering meaningful therapy via engaging “video games.” Notably, the novel robot MIT-MANUS has been designed and programmed for clinical neurological applications, and has undergone extensive clinical trials for more than four years at Burke Rehabilitation Hospital – White Plains, NY. This paper will review the main result of the pilot clinical trial of the 20 patients focusing on the consequences of thalamic lesions.

We are studying if Parkinson disease patients (PD) have a learning deficit, particularly a procedural motor learning deficit. PD patients, age-matched and young adult controls were asked to execute point-to-point movements in a “virtual mechanical environment” generated by a portable robot. We compare the learning rate of subjects while they learned to manipulate the novel force field. Our preliminary results suggest that PD patients present a deficit in the rate of procedural motor learning and that we can estimate their learning specific “equivalent” age.