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Background Robots can generate rich kinematic datasets that have the potential to provide far more insight into impairments than standard clinical ordinal scales. Determining how to define the presence or absence of impairment in individuals using kinematic data, however, can be challenging. Machine learning techniques offer a potential solution to this problem. In the present manuscript we examine proprioception in stroke survivors using a robotic arm position matching task. Proprioception is impaired in 50–60% of stroke survivors and has been associated with poorer motor recovery and longer lengths of hospital stay. We present a simple cut-off score technique for individual kinematic parameters and an overall task score to determine impairment. We then compare the ability of different machine learning (ML) techniques and the above-mentioned task score to correctly classify individuals with or without stroke based on kinematic data. Methods Participants performed an Arm Position Matching (APM) task in an exoskeleton robot. The task produced 12 kinematic parameters that quantify multiple attributes of position sense. We first quantified impairment in individual parameters and an overall task score by determining if participants with stroke fell outside of the 95% cut-off score of control (normative) values. Then, we applied five machine learning algorithms (i.e., Logistic Regression, Decision Tree, Random Forest, Random Forest with Hyperparameters Tuning, and Support Vector Machine), and a deep learning algorithm (i.e., Deep Neural Network) to classify individual participants as to whether or not they had a stroke based only on kinematic parameters using a tenfold cross-validation approach. Results We recruited 429 participants with neuroimaging-confirmed stroke (< 35 days post-stroke) and 465 healthy controls. Depending on the APM parameter, we observed that 10.9–48.4% of stroke participants were impaired, while 44% were impaired based on their overall task score. The mean performance metrics of machine learning and deep learning models were: accuracy 82.4%, precision 85.6%, recall 76.5%, and F1 score 80.6%. All machine learning and deep learning models displayed similar classification accuracy; however, the Random Forest model had the highest numerical accuracy (83%). Our models showed higher sensitivity and specificity (AUC = 0.89) in classifying individual participants than the overall task score (AUC = 0.85) based on their performance in the APM task. We also found that variability was the most important feature in classifying performance in the APM task. Conclusion Our ML models displayed similar classification performance. ML models were able to integrate more kinematic information and relationships between variables into decision making and displayed better classification performance than the overall task score. ML may help to provide insight into individual kinematic features that have previously been overlooked with respect to clinical importance.

IntroductionStroke causes different levels of impairment and the degree of recovery varies greatly between patients. The majority of recovery studies are biased towards patients with mild-to-moderate impairments, challenging a unified recovery process framework. Our aim was to develop a statistical framework to analyse recovery patterns in patients with severe and non-severe initial impairment and concurrently investigate whether they recovered differently.MethodsWe designed a Bayesian hierarchical model to estimate 3–6 months upper limb Fugl-Meyer (FM) scores after stroke. When focusing on the explanation of recovery patterns, we addressed confounds affecting previous recovery studies and considered patients with FM-initial scores <45 only. We systematically explored different FM-breakpoints between severe/non-severe patients (FM-initial=5–30). In model comparisons, we evaluated whether impairment-level-specific recovery patterns indeed existed. Finally, we estimated the out-of-sample prediction performance for patients across the entire initial impairment range.ResultsRecovery data was assembled from eight patient cohorts (n=489). Data were best modelled by incorporating two subgroups (breakpoint: FM-initial=10). Both subgroups recovered a comparable constant amount, but with different proportional components: severely affected patients recovered more the smaller their impairment, while non-severely affected patients recovered more the larger their initial impairment. Prediction of 3–6 months outcomes could be done with an R2=63.5% (95% CI=51.4% to 75.5%).ConclusionsOur work highlights the benefit of simultaneously modelling recovery of severely-to-non-severely impaired patients and demonstrates both shared and distinct recovery patterns. Our findings provide evidence that the severe/non-severe subdivision in recovery modelling is not an artefact of previous confounds. The presented out-of-sample prediction performance may serve as benchmark to evaluate promising biomarkers of stroke recovery.

Robotic technologies have profoundly affected the identification of fundamental properties of brain function. This success is attributable to robots being able to control the position of or forces applied to limbs, and their inherent ability to easily, objectively, and reliably quantify sensorimotor behavior. Our general hypothesis is that these same attributes make robotic technologies ideal for clinically assessing sensory, motor, and cognitive impairments in stroke and other neurological disorders. Further, they provide opportunities for novel therapeutic strategies. The present opinionated review describes how robotic technologies combined with virtual/augmented reality systems can support a broad range of behavioral tasks to objectively quantify brain function. This information could potentially be used to provide more accurate diagnostic and prognostic information than is available from current clinical assessment techniques. The review also highlights the potential benefits of robots to provide upper-limb therapy. Although the capital cost of these technologies is substantial, it pales in comparison with the potential cost reductions to the overall healthcare system that improved assessment and therapeutic interventions offer.

Background Motor impairment after stroke interferes with performance of everyday activities. Upper limb spasticity may further disrupt the movement patterns that enable optimal function; however, the specific features of these altered movement patterns, which differentiate individuals with and without spasticity, have not been fully identified. This study aimed to characterize the kinematic and proprioceptive deficits of individuals with upper limb spasticity after stroke using the Kinarm robotic exoskeleton. Methods Upper limb function was characterized using two tasks: Visually Guided Reaching, in which participants moved the limb from a central target to 1 of 4 or 1 of 8 outer targets when cued (measuring reaching function) and Arm Position Matching, in which participants moved the less-affected arm to mirror match the position of the affected arm (measuring proprioception), which was passively moved to 1 of 4 or 1 of 9 different positions. Comparisons were made between individuals with ( n  = 35) and without (n = 35) upper limb post-stroke spasticity. Results Statistically significant differences in affected limb performance between groups were observed in reaching-specific measures characterizing movement time and movement speed, as well as an overall metric for the Visually Guided Reaching task. While both groups demonstrated deficits in proprioception compared to normative values, no differences were observed between groups. Modified Ashworth Scale score was significantly correlated with these same measures. Conclusions The findings indicate that individuals with spasticity experience greater deficits in temporal features of movement while reaching, but not in proprioception in comparison to individuals with post-stroke motor impairment without spasticity. Temporal features of movement can be potential targets for rehabilitation in individuals with upper limb spasticity after stroke.

Background Proprioceptive impairments are common after stroke and are associated with worse motor recovery and poor rehabilitation outcomes. Motor learning may also be an important factor in motor recovery, and some evidence in healthy adults suggests that reduced proprioceptive function is associated with reductions in motor learning. It is unclear how impairments in proprioception and motor learning relate after stroke. Here we used robotics and a traditional clinical assessment to examine the link between impairments in proprioception after stroke and a type of motor learning known as visuomotor adaptation. Methods We recruited participants with first-time unilateral stroke and controls matched for overall age and sex. Proprioceptive impairments in the more affected arm were assessed using robotic arm position- (APM) and movement-matching (AMM) tasks. We also assessed proprioceptive impairments using a clinical scale (Thumb Localization Test; TLT). Visuomotor adaptation was assessed using a task that systematically rotated hand cursor feedback during reaching movements (VMR). We quantified how much participants adapted to the disturbance and how many trials they took to adapt to the same levels as controls. Spearman’s rho was used to examine the relationship between proprioception, assessed using robotics and the TLT, and visuomotor adaptation. Data from healthy adults were used to identify participants with stroke who were impaired in proprioception and visuomotor adaptation. The independence of impairments in proprioception and adaptation were examined using Fisher’s exact tests. Results Impairments in proprioception (58.3%) and adaptation (52.1%) were common in participants with stroke (n = 48; 2.10% acute, 70.8% subacute, 27.1% chronic stroke). Performance on the APM task, AMM task, and TLT scores correlated weakly with measures of visuomotor adaptation. Fisher’s exact tests demonstrated that impairments in proprioception, assessed using robotics and the TLT, were independent from impairments in visuomotor adaptation in our sample. Conclusion Our results suggest impairments in proprioception may be independent from impairments in visuomotor adaptation after stroke. Further studies are needed to understand factors that influence the relationship between motor learning, proprioception and other rehabilitation outcomes throughout stroke recovery.

Much of our understanding of motor control deficits in children with developmental coordination disorder (DCD) comes from upper limb assessments focusing on the dominant limb. Here, using two robotic behavioural tasks, we investigated motor control in both the dominant and non-dominant limbs of children with DCD. Twenty-six children with diagnosed DCD (20 males; mean age 10.6 years ± 1.3 years) and 155 controls were included in this cross-sectional study. Participants completed a visually guided reaching task with their dominant and non-dominant limbs and a bimanual object hitting task. Motor performance was quantified across nine parameters. We determined the number of children with DCD who fell outside of the typical performance range of the controls on these parameters and compared the DCD and control groups using ANCOVAs, accounting for age. Children with DCD demonstrated impairments in six out of nine parameters; deficits were more commonly noted in the non-dominant limb. Interestingly, when looking at individual performance, several children with DCD performed in the range of controls. These findings indicate that children with DCD display deficits in motor control in both the dominant and non-dominant limb and highlight the importance of including detailed assessments of both limbs when investigating children with DCD. They also demonstrate the variability in motor control performance evidenced by children with DCD.

Background A key motor skill is the ability to rapidly interact with our dynamic environment. Humans can generate goal-directed motor actions in response to sensory stimulus within ~ 60-200ms. This ability can be impaired after stroke, but most clinical tools lack any measures of rapid feedback processing. Reaching tasks have been used as a framework to quantify impairments in generating motor corrections for individuals with stroke. However, reaching may be inadequate as an assessment tool as repeated reaching can be fatiguing for individuals with stroke. Further, reaching requires many trials to be completed including trials with and without disturbances, and thus, exacerbate fatigue. Here, we describe a novel robotic task to quantify rapid feedback processing in healthy controls and compare this performance with individuals with stroke to (more) efficiently identify impairments in rapid feedback processing. Methods We assessed a cohort of healthy controls ( n  = 135) and individuals with stroke ( n  = 40; Mean 41 days from stroke) in the Fast Feedback Interception Task (FFIT) using the Kinarm Exoskeleton robot. Participants were instructed to intercept a circular white target moving towards them with their hand represented as a virtual paddle. On some trials, the arm could be physically perturbed, the target or paddle could abruptly change location, or the target could change colour requiring the individual to now avoid the target. Results Most participants with stroke were impaired in reaction time (85%) and end-point accuracy (83%) in at least one of the task conditions, most commonly with target or paddle shifts. Of note, this impairment was also evident in most individuals with stroke when performing the task using their unaffected arm (75%). Comparison with upper limb clinical measures identified moderate correlations with the FFIT. Conclusion The FFIT was able to identify a high proportion of individuals with stroke as impaired in rapid feedback processing using either the affected or unaffected arms. The task allows many different types of feedback responses to be efficiently assessed in a short amount of time.

Rehabilitation is important for regaining mobility poststroke. Clinical practice guidelines suggest a high number of repetitive stepping activities to optimize subacute recovery especially when undertaken at intensities that challenge cardiovascular fitness. However, adherence to these guidelines is unclear. The objective of this study was to quantify aerobic minutes and step number in usual care inpatient stroke rehabilitation unit physical therapy sessions across Canada and identify characteristics of participants who met guideline aerobic intensity minutes at a session midpoint in their rehabilitation. To gain insight into usual care, we analyzed cross-sectional data from the usual care arm of the Walk 'n Watch implementation trial; trial sites included Canadian rehabilitation units that were not typically involved in research studies. To be included, medically stable patients were admitted for inpatient stroke rehabilitation, and able to take > 5 steps with a maximum of one person assisting. We assessed a midpoint physical therapy session with a wrist-based heart monitor (aerobic minutes) and ankle-based step counter (step number). Means, histograms, and correlations between aerobic minutes (> 40% heart rate reserve) and steps were calculated. There were 166 participants (69 females, age 69 standard deviation (SD)12 years) with stroke (138 Ischemic/ 27 Hemorrhagic) included. Participants had a mean of 10(SD11) aerobic minutes and 985(SD579) steps. The relationship between step number and aerobic minutes was negligible (R2 = 0.003). More participants with ≥20 aerobic minutes in a session were male, with lower 6 Minute Walk Test distance, and have a subcortical stroke location. The number of steps has increased, but aerobic minutes has not changed and remains extremely low compared to published reports in the past several years. Given that increasing activity levels are critical for stroke recovery, further investigation into the potential barriers to achieving targets set by guidelines is recommended. ClinicalTrials.gov NCT04238260.

Background Robotic rehabilitation after stroke provides the potential to increase and carefully control dosage of therapy. Only a small number of studies, however, have examined robotic therapy in the first few weeks post-stroke. In this study we designed robotic upper extremity therapy tasks for the bilateral Kinarm Exoskeleton Lab and piloted them in individuals with subacute stroke. Pilot testing was focused mainly on the feasibility of implementing these new tasks, although we recorded a number of standardized outcome measures before and after training. Methods Our team developed 9 robotic therapy tasks to incorporate feedback, intensity, challenge, and subject engagement as well as addressing both unimanual and bimanual arm activities. Subacute stroke participants were assigned to a robotic therapy (N = 9) or control group (N = 10) in a matched-group manner. The robotic therapy group completed 1-h of robotic therapy per day for 10 days in addition to standard therapy. The control group participated only in standard of care therapy. Clinical and robotic assessments were completed prior to and following the intervention. Clinical assessments included the Fugl-Meyer Assessment of Upper Extremity (FMA UE), Action Research Arm Test (ARAT) and Functional Independence Measure (FIM). Robotic assessments of upper limb sensorimotor function included a Visually Guided Reaching task and an Arm Position Matching task, among others. Paired sample t-tests were used to compare initial and final robotic therapy scores as well as pre- and post-clinical and robotic assessments. Results Participants with subacute stroke (39.8 days post-stroke) completed the pilot study. Minimal adverse events occurred during the intervention and adding 1 h of robotic therapy was feasible. Clinical and robotic scores did not significantly differ between groups at baseline. Scores on the FMA UE, ARAT, FIM, and Visually Guided Reaching improved significantly in the robotic therapy group following completion of the robotic intervention. However, only FIM and Arm Position Match improved over the same time in the control group. Conclusions The Kinarm therapy tasks have the potential to improve outcomes in subacute stroke. Future studies are necessary to quantify the benefits of this robot-based therapy in a larger cohort. Trial registration: ClinicalTrials.gov, NCT04201613, Registered 17 December 2019—Retrospectively Registered, https://clinicaltrials.gov/ct2/show/NCT04201613 .

Background An individual’s rapid motor skills allow them to perform many daily activities and are a hallmark of physical health. Although age and sex are both known to affect motor performance, standardized methods for assessing their impact on upper limb function are limited. Methods Here we perform a cross-sectional study of 643 healthy human participants in two interactive motor tasks developed to quantify sensorimotor abilities, Object-Hit (OH) and Object-Hit-and-Avoid (OHA). The tasks required participants to hit virtual objects with and without the presence of distractor objects. Velocities and positions of hands and objects were recorded by a robotic exoskeleton, allowing a variety of parameters to be calculated for each trial. We verified that these tasks are viable for measuring performance in healthy humans and we examined whether any of our recorded parameters were related to age or sex. Results Our analysis shows that both OH and OHA can assess rapid motor behaviours in healthy human participants. It also shows that while some parameters in these tasks decline with age, those most associated with the motor system do not. Three parameters show significant sex-related effects in OH, but these effects disappear in OHA. Conclusions This study suggests that the underlying effect of aging on rapid motor behaviours is not on the capabilities of the motor system, but on the brain’s capacity for processing inputs into motor actions. Additionally, this study provides a baseline description of healthy human performance in OH and OHA when using these tasks to investigate age-related declines in sensorimotor ability.