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.

OBJECTIVE: To report the fidelity of the enhanced upper limb therapy programme within the Robot-Assisted Training for the Upper Limb after stroke (RATULS) randomized controlled trial, the types of goals selected and the proportion of goals achieved. DESIGN: Descriptive analysis of data on fidelity, goal selection and achievement from an intervention group within a randomized controlled trial. SETTING: Out-patient stroke rehabilitation within four UK NHS centres. SUBJECTS: 259 participants with moderate-severe upper limb activity limitation (Action Research Arm Test 0-39) between one week and five years post first stroke. INTERVENTION: The enhanced upper limb therapy programme aimed to provide 36 one-hour sessions, including 45 minutes of face-to-face therapy focusing on personal goals, over 12 weeks. RESULTS: 7877/9324 (84%) sessions were attended; a median of 34 [IQR 29-36] per participant. A median of 127 [IQR 70-190] repetitions were achieved per participant per session attended. Based upon the Canadian Occupational Performance Measure, goal categories were: self-care 1449/2664 (54%); productivity 374/2664 (14%); leisure 180/2664 (7%) and 'other' 661/2664 (25%). For the 2051/2664 goals for which data were available, 1287 (51%) were achieved, ranging between 27% by participants more than 12 months post stroke with baseline Action Research Arm Test scores 0-7, and 88% by those less than three months after stroke with scores 8-19. CONCLUSIONS: Intervention fidelity was high. Goals relating to self-care were most commonly selected. The proportion of goals achieved varied, depending on time post stroke and baseline arm activity limitation.

Robot assisted training for the upper limb after stroke (RATULS): a multicentre randomised controlled trial.

BACKGROUND: Loss of arm function is a common problem after stroke. Robot-assisted training might improve arm function and activities of daily living. We compared the clinical effectiveness of robot-assisted training using the MIT-Manus robotic gym with an enhanced upper limb therapy (EULT) programme based on repetitive functional task practice and with usual care. METHODS: RATULS was a pragmatic, multicentre, randomised controlled trial done at four UK centres. Stroke patients aged at least 18 years with moderate or severe upper limb functional limitation, between 1 week and 5 years after their first stroke, were randomly assigned (1:1:1) to receive robot-assisted training, EULT, or usual care. Robot-assisted training and EULT were provided for 45 min, three times per week for 12 weeks. Randomisation was internet-based using permuted block sequences. Treatment allocation was masked from outcome assessors but not from participants or therapists. The primary outcome was upper limb function success (defined using the Action Research Arm Test [ARAT]) at 3 months. Analyses were done on an intention-to-treat basis. This study is registered with the ISRCTN registry, number ISRCTN69371850. FINDINGS: Between April 14, 2014, and April 30, 2018, 770 participants were enrolled and randomly assigned to either robot-assisted training (n=257), EULT (n=259), or usual care (n=254). The primary outcome of ARAT success was achieved by 103 (44%) of 232 patients in the robot-assisted training group, 118 (50%) of 234 in the EULT group, and 85 (42%) of 203 in the usual care group. Compared with usual care, robot-assisted training (adjusted odds ratio [aOR] 1·17 [98·3% CI 0·70-1·96]) and EULT (aOR 1·51 [0·90-2·51]) did not improve upper limb function; the effects of robot-assisted training did not differ from EULT (aOR 0·78 [0·48-1·27]). More participants in the robot-assisted training group (39 [15%] of 257) and EULT group (33 [13%] of 259) had serious adverse events than in the usual care group (20 [8%] of 254), but none were attributable to the intervention. INTERPRETATION: Robot-assisted training and EULT did not improve upper limb function after stroke compared with usual care for patients with moderate or severe upper limb functional limitation. These results do not support the use of robot-assisted training as provided in this trial in routine clinical practice. FUNDING: National Institute for Health Research Health Technology Assessment Programme.

Task-specific ankle robotics gait training after stroke: a randomized pilot study.

BACKGROUND: An unsettled question in the use of robotics for post-stroke gait rehabilitation is whether task-specific locomotor training is more effective than targeting individual joint impairments to improve walking function. The paretic ankle is implicated in gait instability and fall risk, but is difficult to therapeutically isolate and refractory to recovery. We hypothesize that in chronic stroke, treadmill-integrated ankle robotics training is more effective to improve gait function than robotics focused on paretic ankle impairments. FINDINGS: Participants with chronic hemiparetic gait were randomized to either six weeks of treadmill-integrated ankle robotics (n = 14) or dose-matched seated ankle robotics (n = 12) videogame training. Selected gait measures were collected at baseline, post-training, and six-week retention. Friedman, and Wilcoxon Sign Rank and Fisher's exact tests evaluated within and between group differences across time, respectively. Six weeks post-training, treadmill robotics proved more effective than seated robotics to increase walking velocity, paretic single support, paretic push-off impulse, and active dorsiflexion range of motion. Treadmill robotics durably improved gait dorsiflexion swing angle leading 6/7 initially requiring ankle braces to self-discarded them, while their unassisted paretic heel-first contacts increased from 44 % to 99.6 %, versus no change in assistive device usage (0/9) following seated robotics. CONCLUSIONS: Treadmill-integrated, but not seated ankle robotics training, durably improves gait biomechanics, reversing foot drop, restoring walking propulsion, and establishing safer foot landing in chronic stroke that may reduce reliance on assistive devices. These findings support a task-specific approach integrating adaptive ankle robotics with locomotor training to optimize mobility recovery. CLINICAL TRIAL IDENTIFIER: NCT01337960. https://clinicaltrials.gov/ct2/show/NCT01337960?term=NCT01337960&rank=1.

Spatiotemporal dynamics of online motor correction processing revealed by high-density electroencephalography.

The ability to control online motor corrections is key to dealing with unexpected changes arising in the environment with which we interact. How the CNS controls online motor corrections is poorly understood, but evidence has accumulated in favor of a submovement-based model in which apparently continuous movement is segmented into distinct submovements. Although most studies have focused on submovements' kinematic features, direct links with the underlying neural dynamics have not been extensively explored. This study sought to identify an electroencephalographic signature of submovements. We elicited kinematic submovements using a double-step displacement paradigm. Participants moved their wrist toward a target whose direction could shift mid-movement with a 50% probability. Movement kinematics and cortical activity were concurrently recorded with a low-friction robotic device and high-density electroencephalography. Analysis of spatiotemporal dynamics of brain activation and its correlation with movement kinematics showed that the production of each kinematic submovement was accompanied by (1) stereotyped topographic scalp maps and (2) frontoparietal ERPs time-locked to submovements. Positive ERP peaks from frontocentral areas contralateral to the moving wrist preceded kinematic submovement peaks by 220-250 msec and were followed by positive ERP peaks from contralateral parietal areas (140-250 msec latency, 0-80 msec before submovement peaks). Moreover, individual subject variability in the latency of frontoparietal ERP components following the target shift significantly predicted variability in the latency of the corrective submovement. Our results are in concordance with evidence for the intermittent nature of continuous movement and elucidate the timing and role of frontoparietal activations in the generation and control of corrective submovements.

Robotic measurement of arm movements after stroke establishes biomarkers of motor recovery.

BACKGROUND AND PURPOSE: Because robotic devices record the kinematics and kinetics of human movements with high resolution, we hypothesized that robotic measures collected longitudinally in patients after stroke would bear a significant relationship to standard clinical outcome measures and, therefore, might provide superior biomarkers. METHODS: In patients with moderate-to-severe acute ischemic stroke, we used clinical scales and robotic devices to measure arm movement 7, 14, 21, 30, and 90 days after the event at 2 clinical sites. The robots are interactive devices that measure speed, position, and force so that calculated kinematic and kinetic parameters could be compared with clinical assessments. RESULTS: Among 208 patients, robotic measures predicted well the clinical measures (cross-validated R(2) of modified Rankin scale=0.60; National Institutes of Health Stroke Scale=0.63; Fugl-Meyer=0.73; Motor Power=0.75). When suitably scaled and combined by an artificial neural network, the robotic measures demonstrated greater sensitivity in measuring the recovery of patients from day 7 to day 90 (increased standardized effect=1.47). CONCLUSIONS: These results demonstrate that robotic measures of motor performance will more than adequately capture outcome, and the altered effect size will reduce the required sample size. Reducing sample size will likely improve study efficiency.

Movement-generated afference paired with transcranial magnetic stimulation: an associative stimulation paradigm.

BACKGROUND: A peripheral nerve stimulus can enhance or suppress the evoked response to transcranial magnetic stimulation (TMS) depending on the latency of the preceding peripheral nerve stimulation (PNS) pulse. Similarly, somatosensory afference from the passively moving limb can transiently alter corticomotor excitability, in a phase-dependent manner. The repeated association of PNS with TMS is known to modulate corticomotor excitability; however, it is unknown whether repeated passive-movement associative stimulation (MAS) has similar effects. METHODS: In a proof-of-principal study, using a cross-over design, seven healthy subjects received in separate sessions: (1) TMS (120% of the resting motor threshold-RMT, optimal site for Flexor Carpi Radialis) with muscle at rest; (2) TMS paired with cyclic passive movement during extension cyclic passive movement (400 pairs, 1 Hz), with the intervention order randomly assigned. Normality was tested using the Kolmogorov-Smirnov test, then compared to pre-intervention baseline using repeated measures ANOVA with a Dunnet multiple comparisons test. RESULTS: MAS led to a progressive and significant decrease in the motor evoked potential (MEP) amplitude over the intervention (R(2) = 0.6665, P < 0.0001), which was not evident with TMS alone (R(2) = 0.0068, P = 0.641). Post-intervention excitability reduction, only present with MAS intervention, remained for 20 min (0-10 min = 68.2 ± 4.9%, P < 0.05; 10-20 min = 73.3 ± 9.7%, P < 0.05). CONCLUSION: The association of somatosensory afference from the moving limb with TMS over primary motor cortex in healthy subjects can be used to modulate corticomotor excitability, and may have therapeutic implications.

Robot-assisted therapy for long-term upper-limb impairment after stroke.

BACKGROUND: Effective rehabilitative therapies are needed for patients with long-term deficits after stroke. METHODS: In this multicenter, randomized, controlled trial involving 127 patients with moderate-to-severe upper-limb impairment 6 months or more after a stroke, we randomly assigned 49 patients to receive intensive robot-assisted therapy, 50 to receive intensive comparison therapy, and 28 to receive usual care. Therapy consisted of 36 1-hour sessions over a period of 12 weeks. The primary outcome was a change in motor function, as measured on the Fugl-Meyer Assessment of Sensorimotor Recovery after Stroke, at 12 weeks. Secondary outcomes were scores on the Wolf Motor Function Test and the Stroke Impact Scale. Secondary analyses assessed the treatment effect at 36 weeks. RESULTS: At 12 weeks, the mean Fugl-Meyer score for patients receiving robot-assisted therapy was better than that for patients receiving usual care (difference, 2.17 points; 95% confidence interval [CI], -0.23 to 4.58) and worse than that for patients receiving intensive comparison therapy (difference, -0.14 points; 95% CI, -2.94 to 2.65), but the differences were not significant. The results on the Stroke Impact Scale were significantly better for patients receiving robot-assisted therapy than for those receiving usual care (difference, 7.64 points; 95% CI, 2.03 to 13.24). No other treatment comparisons were significant at 12 weeks. Secondary analyses showed that at 36 weeks, robot-assisted therapy significantly improved the Fugl-Meyer score (difference, 2.88 points; 95% CI, 0.57 to 5.18) and the time on the Wolf Motor Function Test (difference, -8.10 seconds; 95% CI, -13.61 to -2.60) as compared with usual care but not with intensive therapy. No serious adverse events were reported. CONCLUSIONS: In patients with long-term upper-limb deficits after stroke, robot-assisted therapy did not significantly improve motor function at 12 weeks, as compared with usual care or intensive therapy. In secondary analyses, robot-assisted therapy improved outcomes over 36 weeks as compared with usual care but not with intensive therapy. (ClinicalTrials.gov number, NCT00372411.)

The passive stiffness of the wrist and forearm.

Because wrist rotation dynamics are dominated by stiffness (Charles SK, Hogan N. J Biomech 44: 614-621, 2011), understanding how humans plan and execute coordinated wrist rotations requires knowledge of the stiffness characteristics of the wrist joint. In the past, the passive stiffness of the wrist joint has been measured in 1 degree of freedom (DOF). Although these 1-DOF measurements inform us of the dynamics the neuromuscular system must overcome to rotate the wrist in pure flexion-extension (FE) or pure radial-ulnar deviation (RUD), the wrist rarely rotates in pure FE or RUD. Instead, understanding natural wrist rotations requires knowledge of wrist stiffness in combinations of FE and RUD. The purpose of this report is to present measurements of passive wrist stiffness throughout the space spanned by FE and RUD. Using a rehabilitation robot designed for the wrist and forearm, we measured the passive stiffness of the wrist joint in 10 subjects in FE, RUD, and combinations. For comparison, we measured the passive stiffness of the forearm (in pronation-supination), as well. Our measurements in pure FE and RUD agreed well with previous 1-DOF measurements. We have linearized the 2-DOF stiffness measurements and present them in the form of stiffness ellipses and as stiffness matrices useful for modeling wrist rotation dynamics. We found that passive wrist stiffness was anisotropic, with greater stiffness in RUD than in FE. We also found that passive wrist stiffness did not align with the anatomical axes of the wrist; the major and minor axes of the stiffness ellipse were rotated with respect to the FE and RUD axes by ∼20°. The direction of least stiffness was between ulnar flexion and radial extension, a direction used in many natural movements (known as the "dart-thrower's motion"), suggesting that the nervous system may take advantage of the direction of least stiffness for common wrist rotations.

An economic analysis of robot-assisted therapy for long-term upper-limb impairment after stroke.

BACKGROUND AND PURPOSE: Stroke is a leading cause of disability. Rehabilitation robotics have been developed to aid in recovery after a stroke. This study determined the additional cost of robot-assisted therapy and tested its cost-effectiveness. METHODS: We estimated the intervention costs and tracked participants' healthcare costs. We collected quality of life using the Stroke Impact Scale and the Health Utilities Index. We analyzed the cost data at 36 weeks postrandomization using multivariate regression models controlling for site, presence of a prior stroke, and Veterans Affairs costs in the year before randomization. RESULTS: A total of 127 participants were randomized to usual care plus robot therapy (n=49), usual care plus intensive comparison therapy (n=50), or usual care alone (n=28). The average cost of delivering robot therapy and intensive comparison therapy was $5152 and $7382, respectively (P<0.001), and both were significantly more expensive than usual care alone (no additional intervention costs). At 36 weeks postrandomization, the total costs were comparable for the 3 groups ($17 831 for robot therapy, $19 746 for intensive comparison therapy, and $19 098 for usual care). Changes in quality of life were modest and not statistically different. CONCLUSIONS: The added cost of delivering robot or intensive comparison therapy was recuperated by lower healthcare use costs compared with those in the usual care group. However, uncertainty remains about the cost-effectiveness of robotic-assisted rehabilitation compared with traditional rehabilitation. Clinical Trial Registration- URL: http://clinicaltrials.gov. Unique identifier: NCT00372411.

Measurement of passive ankle stiffness in subjects with chronic hemiparesis using a novel ankle robot.

Our objective in this study was to assess passive mechanical stiffness in the ankle of chronic hemiparetic stroke survivors and to compare it with those of healthy young and older (age-matched) individuals. Given the importance of the ankle during locomotion, an accurate estimate of passive ankle stiffness would be valuable for locomotor rehabilitation, potentially providing a measure of recovery and a quantitative basis to design treatment protocols. Using a novel ankle robot, we characterized passive ankle stiffness both in sagittal and in frontal planes by applying perturbations to the ankle joint over the entire range of motion with subjects in a relaxed state. We found that passive stiffness of the affected ankle joint was significantly higher in chronic stroke survivors than in healthy adults of a similar cohort, both in the sagittal as well as frontal plane of movement, in three out of four directions tested with indistinguishable stiffness values in plantarflexion direction. Our findings are comparable to the literature, thus indicating its plausibility, and, to our knowledge, report for the first time passive stiffness in the frontal plane for persons with chronic stroke and older healthy adults.

Effect of gravity on robot-assisted motor training after chronic stroke: a randomized trial.

OBJECTIVES: To determine the efficacy of 2 distinct 6-week robot-assisted reaching programs compared with an intensive conventional arm exercise program (ICAE) for chronic, stroke-related upper-extremity (UE) impairment. To examine whether the addition of robot-assisted training out of the horizontal plane leads to improved outcomes. DESIGN: Randomized controlled trial, single-blinded, with 12-week follow-up. SETTING: Research setting in a large medical center. PARTICIPANTS: Adults (N=62) with chronic, stroke-related arm weakness stratified by impairment severity using baseline UE motor assessments. INTERVENTIONS: Sixty minutes, 3 times a week for 6 weeks of robot-assisted planar reaching (gravity compensated), combined planar with vertical robot-assisted reaching, or intensive conventional arm exercise program. MAIN OUTCOME MEASURE: UE Fugl-Meyer Assessment (FMA) mean change from baseline to final training. RESULTS: All groups showed modest gains in the FMA from baseline to final with no significant between group differences. Most change occurred in the planar robot group (mean change ± SD, 2.94 ± 0.77; 95% confidence interval [CI], 1.40-4.47). Participants with greater motor impairment (n=41) demonstrated a larger difference in response (mean change ± SD, 2.29 ± 0.72; 95% CI, 0.85-3.72) for planar robot-assisted exercise compared with the intensive conventional arm exercise program (mean change ± SD, 0.43 ± 0.72; 95% CI, -1.00 to 1.86). CONCLUSIONS: Chronic UE deficits because of stroke are responsive to intensive motor task training. However, training outside the horizontal plane in a gravity present environment using a combination of vertical with planar robots was not superior to training with the planar robot alone.

Reversal of TMS-induced motor twitch by training is associated with a reduction in excitability of the antagonist muscle.

BACKGROUND: A single session of isolated repetitive movements of the thumb can alter the response to transcranial magnetic stimulation (TMS), such that the related muscle twitch measured post-training occurs in the trained direction. This response is attributed to transient excitability changes in primary motor cortex (M1) that form the early part of learning. We investigated; (1) whether this phenomenon might occur for movements at the wrist, and (2) how specific TMS activation patterns of opposing muscles underlie the practice-induced change in direction. METHODS: We used single-pulse suprathreshold TMS over the M1 forearm area, to evoke wrist movements in 20 healthy subjects. We measured the preferential direction of the TMS-induced twitch in both the sagittal and coronal plane using an optical goniometer fixed to the dorsum of the wrist, and recorded electromyographic (EMG) activity from the flexor carpi radialis (FCR) and extensor carpi radialis (ECR) muscles. Subjects performed gentle voluntary movements, in the direction opposite to the initial twitch for 5 minutes at 0.2 Hz. We collected motor evoked potentials (MEPs) elicited by TMS at baseline and for 10 minutes after training. RESULTS: Repetitive motor training was sufficient for TMS to evoke movements in the practiced direction opposite to the original twitch. For most subjects the effect of the newly-acquired direction was retained for at least 10 minutes before reverting to the original. Importantly, the direction change of the movement was associated with a significant decrease in MEP amplitude of the antagonist to the trained muscle, rather than an increase in MEP amplitude of the trained muscle. CONCLUSIONS: These results demonstrate for the first time that a TMS-twitch direction change following a simple practice paradigm may result from reduced corticospinal drive to muscles antagonizing the trained direction. Such findings may have implications for training paradigms in neurorehabilitation.

Robotic Kinematic measures of the arm in chronic Stroke: part 1 - Motor Recovery patterns from tDCS preceding intensive training.

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 .

Robotic Kinematic measures of the arm in chronic Stroke: part 2 - strong correlation with clinical outcome measures.

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 < 0.001) for the linear model and R = 0.88 (P < 0.001) for the nonlinear model. Similarly, a high correlation was found for wrist kinematics and the FMA-UE (R = 0.91 (P < 0.001) and R = 0.92 (P < 0.001) for the linear and nonlinear model respectively). The combined analysis produced a correlation of R = 0.91 (P < 0.001) for the linear model and R = 0.91 (P < 0.001) for the nonlinear model. 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 .

Accurate prediction of clinical stroke scales and improved biomarkers of motor impairment from robotic measurements.

OBJECTIVE: One of the greatest challenges in clinical trial design is dealing with the subjectivity and variability introduced by human raters when measuring clinical end-points. We hypothesized that robotic measures that capture the kinematics of human movements collected longitudinally in patients after stroke would bear a significant relationship to the ordinal clinical scales and potentially lead to the development of more sensitive motor biomarkers that could improve the efficiency and cost of clinical trials. MATERIALS AND METHODS: We used clinical scales and a robotic assay to measure arm movement in 208 patients 7, 14, 21, 30 and 90 days after acute ischemic stroke at two separate clinical sites. The robots are low impedance and low friction interactive devices that precisely measure speed, position and force, so that even a hemiparetic patient can generate a complete measurement profile. These profiles were used to develop predictive models of the clinical assessments employing a combination of artificial ant colonies and neural network ensembles. RESULTS: The resulting models replicated commonly used clinical scales to a cross-validated R2 of 0.73, 0.75, 0.63 and 0.60 for the Fugl-Meyer, Motor Power, NIH stroke and modified Rankin scales, respectively. Moreover, when suitably scaled and combined, the robotic measures demonstrated a significant increase in effect size from day 7 to 90 over historical data (1.47 versus 0.67). DISCUSSION AND CONCLUSION: These results suggest that it is possible to derive surrogate biomarkers that can significantly reduce the sample size required to power future stroke clinical trials.

Effects of unilateral robotic limb loading on gait characteristics in subjects with chronic stroke.

BACKGROUND: Hemiparesis after stroke often leads to impaired ankle motor control that impacts gait function. In recent studies, robotic devices have been developed to address this impairment. While capable of imparting forces to assist during training and gait, these devices add mass to the paretic leg which might encumber patients' gait pattern. The purpose of this study was to assess the effects of the added mass of one of these robots, the MIT's Anklebot, while unpowered, on gait of chronic stroke survivors during overground and treadmill walking. METHODS: Nine chronic stroke survivors walked overground and on a treadmill with and without the anklebot mounted on the paretic leg. Gait parameters, interlimb symmetry, and joint kinematics were collected for the four conditions. Repeated-measures analysis of variance (ANOVA) tests were conducted to examine for possible differences across four conditions for the paretic and nonparetic leg. RESULTS: The added inertia and friction of the unpowered anklebot had no statistically significant effect on spatio-temporal parameters of gait, including paretic and nonparetic step time and stance percentage, in both overground and treadmill conditions. Noteworthy, interlimb symmetry as characterized by relative stance duration was greater on the treadmill than overground regardless of loading conditions. The presence of the unpowered robot loading reduced the nonparetic knee peak flexion on the treadmill and paretic peak dorsiflexion overground (p < 0.05). CONCLUSIONS: Our results suggest that for these subjects the added inertia and friction of this backdriveable robot did not significantly alter their gait pattern.

Identification of Gait Events in Healthy Subjects and With Parkinson's Disease Using Inertial Sensors: An Adaptive Unsupervised Learning Approach.

Automatic identification of gait events is an essential component of the control scheme of assistive robotic devices. Many available techniques suffer limitations for real-time implementations and in guaranteeing high performances when identifying events in subjects with gait impairments. Machine learning algorithms offer a solution by enabling the training of different models to represent the gait patterns of different subjects. Here our aim is twofold: to remove the need for training stages using unsupervised learning, and to modify the parameters according to the changes within a walking trial using adaptive procedures. We developed two adaptive unsupervised algorithms for real-time detection of four gait events, using only signals from two single-IMU foot-mounted wearable devices. We evaluated the algorithms using data collected from five healthy adults and seven subjects with Parkinson's disease (PD) walking overground and on a treadmill. Both algorithms obtained high performance in terms of accuracy ( F1 -score ≥ 0.95 for both groups), and timing agreement using a force-sensitive resistors as reference (mean absolute differences of 66 ± 53 msec for the healthy group, and 58 ± 63 msec for the PD group). The proposed algorithms demonstrated the potential to learn optimal parameters for a particular participant and for detecting gait events without additional sensors, external labeling, or long training stages.

Robot-assisted training compared with an enhanced upper limb therapy programme and with usual care for upper limb functional limitation after stroke: the RATULS three-group RCT.

BACKGROUND: Loss of arm function is common after stroke. Robot-assisted training may improve arm outcomes. OBJECTIVE: The objectives were to determine the clinical effectiveness and cost-effectiveness of robot-assisted training, compared with an enhanced upper limb therapy programme and with usual care. DESIGN: This was a pragmatic, observer-blind, multicentre randomised controlled trial with embedded health economic and process evaluations. SETTING: The trial was set in four NHS trial centres. PARTICIPANTS: Patients with moderate or severe upper limb functional limitation, between 1 week and 5 years following first stroke, were recruited. INTERVENTIONS: Robot-assisted training using the Massachusetts Institute of Technology-Manus robotic gym system (InMotion commercial version, Interactive Motion Technologies, Inc., Watertown, MA, USA), an enhanced upper limb therapy programme comprising repetitive functional task practice, and usual care. MAIN OUTCOME MEASURES: The primary outcome was upper limb functional recovery 'success' (assessed using the Action Research Arm Test) at 3 months. Secondary outcomes at 3 and 6 months were the Action Research Arm Test results, upper limb impairment (measured using the Fugl-Meyer Assessment), activities of daily living (measured using the Barthel Activities of Daily Living Index), quality of life (measured using the Stroke Impact Scale), resource use costs and quality-adjusted life-years. RESULTS: A total of 770 participants were randomised (robot-assisted training, n = 257; enhanced upper limb therapy, n = 259; usual care, n = 254). Upper limb functional recovery 'success' was achieved in the robot-assisted training [103/232 (44%)], enhanced upper limb therapy [118/234 (50%)] and usual care groups [85/203 (42%)]. These differences were not statistically significant; the adjusted odds ratios were as follows: robot-assisted training versus usual care, 1.2 (98.33% confidence interval 0.7 to 2.0); enhanced upper limb therapy versus usual care, 1.5 (98.33% confidence interval 0.9 to 2.5); and robot-assisted training versus enhanced upper limb therapy, 0.8 (98.33% confidence interval 0.5 to 1.3). The robot-assisted training group had less upper limb impairment (as measured by the Fugl-Meyer Assessment motor subscale) than the usual care group at 3 and 6 months. The enhanced upper limb therapy group had less upper limb impairment (as measured by the Fugl-Meyer Assessment motor subscale), better mobility (as measured by the Stroke Impact Scale mobility domain) and better performance in activities of daily living (as measured by the Stroke Impact Scale activities of daily living domain) than the usual care group, at 3 months. The robot-assisted training group performed less well in activities of daily living (as measured by the Stroke Impact Scale activities of daily living domain) than the enhanced upper limb therapy group at 3 months. No other differences were clinically important and statistically significant. Participants found the robot-assisted training and the enhanced upper limb therapy group programmes acceptable. Neither intervention, as provided in this trial, was cost-effective at current National Institute for Health and Care Excellence willingness-to-pay thresholds for a quality-adjusted life-year. CONCLUSIONS: Robot-assisted training did not improve upper limb function compared with usual care. Although robot-assisted training improved upper limb impairment, this did not translate into improvements in other outcomes. Enhanced upper limb therapy resulted in potentially important improvements on upper limb impairment, in performance of activities of daily living, and in mobility. Neither intervention was cost-effective. FUTURE WORK: Further research is needed to find ways to translate the improvements in upper limb impairment seen with robot-assisted training into improvements in upper limb function and activities of daily living. Innovations to make rehabilitation programmes more cost-effective are required. LIMITATIONS: Pragmatic inclusion criteria led to the recruitment of some participants with little prospect of recovery. The attrition rate was higher in the usual care group than in the robot-assisted training or enhanced upper limb therapy groups, and differential attrition is a potential source of bias. Obtaining accurate information about the usual care that participants were receiving was a challenge. TRIAL REGISTRATION: Current Controlled Trials ISRCTN69371850. FUNDING: This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 24, No. 54. See the NIHR Journals Library website for further project information.

Many people who have arm weakness following a stroke feel that insufficient attention is paid by rehabilitation services to recovery of their arm. Unfortunately, it is currently unclear how best to provide rehabilitation to optimise recovery, but robot-assisted training and therapy programmes that focus on practising functional tasks are promising and require further evaluation. The Robot-Assisted Training for the Upper Limb after Stroke (RATULS) trial evaluated three approaches to rehabilitation for people with moderate or severe difficulty using their arm. These approaches were robot-assisted training using the Massachusetts Institute of Technology-Manus robotic gym system (InMotion commercial version, Interactive Motion Technologies, Inc., Watertown, MA, USA), an enhanced upper limb therapy programme based on repetitive practice of functional tasks and usual care. Robot-assisted training and the enhanced upper limb therapy programme were provided in an outpatient setting for 45 minutes per session, three times per week, for 12 weeks, in addition to usual care. The Massachusetts Institute of Technology-Manus robotic gym system was selected as it was felt to be the best available technology. The participant sits at a table, places their affected arm onto the Massachusetts Institute of Technology-Manus arm support and attempts to move their arm to play a game on the computer screen. Movements are assisted by the Massachusetts Institute of Technology-Manus if the patient cannot perform the movements themselves. The results of the RATULS trial show that robot-assisted training did not result in additional improvement in stroke survivors' arm use when compared with the enhanced upper limb therapy programme or usual care. Stroke survivors who received enhanced upper limb therapy experienced meaningful improvements in undertaking activities of daily living, when compared with those participants who received either robot-assisted training or usual care. Participants who received enhanced upper limb therapy also experienced benefits in their mobility, compared with usual care participants. Participants and therapists found both therapies acceptable, and described various benefits. A health economic analysis found that neither robot-assisted training nor the enhanced upper limb therapy programme was a cost-effective treatment for the NHS.

Multicenter randomized trial of robot-assisted rehabilitation for chronic stroke: methods and entry characteristics for VA ROBOTICS.

BACKGROUND: Chronic upper extremity impairment due to stroke has significant medical, psychosocial, and financial consequences, but few studies have examined the effectiveness of rehabilitation therapy during the chronic stroke period. OBJECTIVE: . To test the safety and efficacy of the MIT-Manus robotic device for chronic upper extremity impairment following stroke. METHODS: . The VA Cooperative Studies Program initiated a multicenter, randomized, controlled trial in November 2006 (VA ROBOTICS). Participants with upper extremity impairment >/=6 months poststroke were randomized to robot-assisted therapy (RT), intensive comparison therapy (ICT), or usual care (UC). RT and ICT consisted of three 1-hour treatment sessions per week for 12 weeks. The primary outcome was change in the Fugl-Meyer Assessment upper extremity motor function score at 12 weeks relative to baseline. Secondary outcomes included the Wolf Motor Function Test and the Stroke Impact Scale. RESULTS: . A total of 127 participants were randomized: 49 to RT, 50 to ICT, and 28 to UC. The majority of participants were male (96%), with a mean age of 65 years. The primary stroke type was ischemic (85%), and 58% of strokes occurred in the anterior circulation. Twenty percent of the participants reported a stroke in addition to their index stroke. The average time from the index stroke to enrollment was 56 months (range, 6 months to 24 years). The mean Fugl-Meyer score at entry was 18.9. CONCLUSIONS: . VA ROBOTICS demonstrates the feasibility of conducting multicenter clinical trials to rigorously test new rehabilitative devices before their introduction to clinical practice. The results are expected in early 2010.

Submovement changes characterize generalization of motor recovery after stroke.

Submovements are hypothesized to be discrete building blocks of human movement. Changes in their parameters appear to account for features observed in processes of motor learning and motor recovery from stroke. Our previous studies analyzed submovement changes in subjects recovering from stroke. Subjects were trained on point-to-point movements with the assistance of a rehabilitation robot as part of a stroke treatment protocol. Results suggested that recovery starts first by regaining the ability to generate submovements and then, over a longer time period, by reacquiring the means to combine submovements. Over recovery submovements became fewer, longer, and faster and such changes contributed to changes in movement smoothness. Taken together these results lent support to the theory that movement is produced via centrally generated submovements and that changes in submovements characterize recovery. More recently, we investigated generalization of training. We found that stroke subjects trained on point-to-point movements became progressively better able to draw circles, a task on which they had received no training. The goal of this paper was to further investigate the changes that occur in untrained movements during motor recovery from stroke. Specifically we wanted to test whether changes in smoothness and submovements also characterize untrained movements. We analyzed circle drawing movements performed by 47 chronic stroke subjects who underwent training on point-to-point movements over an 18-session robot-assisted therapy program. We found that during recovery the shapes drawn by subjects became not only closer to circles (a task not trained during therapy) but also smoother. Concurrently, submovements grew fewer, longer, and faster. These results are consistent with the theory that movement is produced via submovements and suggest that changes in smoothness and submovements might characterize and describe the process of motor recovery from stroke. Also, they are consistent with the idea that motor recovery after a stroke shares similar traits with motor learning.