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Proprioceptive impairments occur in ~50% of stroke survivors, with 20–40% still impaired six months post-stroke. Early identification of those likely to have persistent impairments is key to personalizing rehabilitation strategies and reducing long-term proprioceptive impairments. In this study, clinical, neuroimaging and robotic measures were used to predict proprioceptive impairments at six months post-stroke on a robotic assessment of proprioception. Clinical assessments, neuroimaging, and a robotic arm position matching (APM) task were performed for 133 stroke participants two weeks post-stroke (12.4 ± 8.4 days). The APM task was also performed six months post-stroke (191.2 ± 18.0 days). Robotics allow more precise measurements of proprioception than clinical assessments. Consequently, an overall APM Task Score was used as ground truth to classify proprioceptive impairments at six months post-stroke. Other APM performance parameters from the two-week assessment were used as predictive features. Clinical assessments included the Thumb Localisation Test (TLT), Behavioural Inattention Test (BIT), Functional Independence Measure (FIM) and demographic information (age, sex and affected arm). Logistic regression classifiers were trained to predict proprioceptive impairments at six months post-stroke using data collected two weeks post-stroke. Models containing robotic features, either alone or in conjunction with clinical and neuroimaging features, had a greater area under the curve (AUC) and lower Akaike Information Criterion (AIC) than models which only contained clinical or neuroimaging features. All models performed similarly with regard to accuracy and F1-score (>70% accuracy). Robotic features were also among the most important when all features were combined into a single model. Predicting long-term proprioceptive impairments, using data collected as early as two weeks post-stroke, is feasible. Identifying those at risk of long-term impairments is an important step towards improving proprioceptive rehabilitation after a stroke.

Motor development in children and youth occurs non-linearly; improvements are rapid at younger ages and decrease as they reach adulthood. There is also evidence that performance variability changes as children and youth age. Accurate models of typical performance are necessary to identify deficits in motor performance and to track the efficacy of therapies. Robotic devices have been used previously to measure motor performance in children and youth, and produce models of typical performance; however, power analyses on these models have not been explored.An algorithm was created to generate normative models of typical motor performance. The accuracy and repeatability of the algorithm were tested using simulated data that changed the number of data points, and the curvature and variability of the data. Two-hundred and eighty-eight participants who are typically developing (ages 5-18) completed a robotic point-to-point reaching task with the Kinarm Exoskeleton. Exponential curves were fit to reaction time measured by the Kinarm to model typical performance. The results of the simulations were used to generate confidence intervals on the models of typical performance.The simulations showed that number of datapoints had the largest impact on accuracy and repeatability of the models, and that repeatability was age-dependent. The simulations with the uniform and non-uniform datasets generated different confidence intervals; however, these differences were minimal when the number of datapoints at each age were matched between the two datasets.To ensure identification of deficits is accurately determined, there is a need to account for differences in repeatability when developing models of typical motor performance in children and youth. The results of our simulations can be used to assess repeatability of non-linear models of motor performance based on dataset size in the future.

Background Cognitive and motor function must work together quickly and seamlessly to allow us to interact with a complex world, but their integration is difficult to assess directly. Interactive technology provides opportunities to assess motor actions requiring cognitive control. Objective To adapt a reverse reaching task to an interactive robotic platform to quantify impairments in cognitive-motor integration following stroke. Methods Participants with subacute stroke (N=59) performed two tasks using the Kinarm: Reverse Visually Guided Reaching (RVGR) and Visually Guided Reaching (VGR). Tasks required subjects move a cursor “quickly and accurately” to virtual targets. In RVGR, cursor motion was reversed compared to finger motion (i.e., hand moves left, cursor moves right). Task parameters and Task Scores were calculated based on models developed from healthy controls, and accounted for the influence of age, sex, and handedness. Results Many stroke participants (86%) were impaired in RVGR with their affected arm (Task Score > 95% of controls). The most common impairment was increased movement time. Seventy-three percent were also impaired with their less affected arm. The most common impairment was larger initial direction angles of reach. Impairments in RVGR improved over time, but 71% of participants tested longitudinally were still impaired with the affected arm ∼6 months post-stroke. Importantly, although 57% were impaired with the less affected arm at 6 months, these individuals were not impaired in VGR. Conclusions Individuals with stroke were impaired in a reverse reaching task but many did not show similar impairments in a standard reaching task, highlighting selective impairment in cognitive-motor integration.

Visual and proprioceptive feedback both contribute to perceptual decisions, but it remains unknown how these feedback signals are integrated together or consider factors such as delays and variance during online control. We investigated this question by having participants reach to a target with randomly applied mechanical and/or visual disturbances. We observed that the presence of visual feedback during a mechanical disturbance did not increase the size of the muscle response significantly but did decrease variance, consistent with a dynamic Bayesian integration model. In a control experiment, we verified that vision had a potent influence when mechanical and visual disturbances were both present but opposite in sign. These results highlight a complex process for multisensory integration, where visual feedback has a relatively modest influence when the limb is mechanically disturbed, but a substantial influence when visual feedback becomes misaligned with the limb.

Visuospatial neglect is a disorder characterized by an impairment of attention, most commonly to the left side of space in individuals with stroke or injury to the right hemisphere. Clinical diagnosis is largely based on performance on pen and paper examinations that are unable to accurately measure the speed of processing environmental stimuli—important for interacting in our dynamic world. Numerous studies of impairment after visuospatial neglect demonstrate delayed reaction times when reaching to the left. However, little is known of the visuospatial impairment in other spatial directions and, further, the influence of the arm being assessed. In this study, we quantify the ability of a large cohort of 204 healthy control participants (females = 102) and 265 individuals with stroke (right hemisphere damage = 162, left hemisphere damage = 103; mean age 62) to generate goal-directed reaches. Participants used both their contralesional and ipsilesional arms to perform a centre-out visually guided reaching task in the horizontal plane. We found that the range of visuospatial impairment can vary dramatically across individuals with some individuals displaying reaction time impairments restricted to a relatively small portion of the workspace, whereas others displayed reaction time impairments in all spatial directions. Reaction time impairments were observed in individuals with right or left hemisphere lesions (48% and 30%, respectively). Directional impairments commonly rotated clockwise when reaching with the left versus the right arms. Impairment in all spatial directions was more prevalent in right than left hemisphere lesions (32% and 12%, respectively). Behavioral Inattention Test scores significantly correlated (r = −0.49, P < 0.005) with reaction time impairments but a large portion of individuals not identified as having visuospatial neglect on the Behavioral Inattention Test still displayed reaction time impairments (35%). MRI and CT scans identified distinct white matter and cortical regions of damage for individuals with directional (insula, inferior frontal–occipital fasciculus and inferior longitudinal fasciculus) and general (superior and middle temporal gyri) visuospatial impairment. This study highlights the prevalence and diversity of visuospatial impairments that can occur following stroke.

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 ( 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.

Our motor system allows us to generate an enormous breadth of voluntary actions, but it remains unclear whether and how much motor skill translates across tasks. For example, if an individual is good at gross motor control, are they also good at fine motor control? Previous research about the generalization across motor skills has been equivocal. Here, we compare human performance across five different motor skills. High correlation between task measures would suggest a certain level of underlying sensorimotor ability that dictates performance across all task types. Low correlation would suggest specificity in abilities across tasks. Performance on a reaching task, an object-hitting task, a bimanual coordination task, a rapid motion task and a target tracking task, was examined twice in a cohort of 25 healthy individuals. Across the cohort, we found relatively high correlations for different spatial and temporal parameters within a given task (16–53% of possible parameter pairs were significantly correlated, with significant r values ranging from 0.53 to 0.97) but relatively low correlations across different tasks (2.7–4.4% of possible parameter pairs were significantly correlated, with significant r values ranging from 0.53–0.71). We performed a cluster analysis across all individuals using 76 performance measures across all tasks for the two repeat testing sessions and demonstrated that repeat tests were commonly grouped together (16 of 25 pairs were grouped next to each other). These results highlight that individuals have different abilities across motor tasks, and that these patterns are consistent across time points.

While many areas of medicine have benefited from the development of objective assessment tools and biomarkers, there have been comparatively few improvements in techniques used to assess brain function and dysfunction. Brain functions such as perception, cognition, and motor control are commonly measured using criteria-based, ordinal scales which can be coarse, have floor/ceiling effects, and often lack the precision to detect change. There is growing recognition that kinematic and kinetic-based measures are needed to quantify impairments following neurological injury such as stroke, in particular for clinical research and clinical trials. This paper will first consider the challenges with using criteria-based ordinal scales to quantify impairment and recovery. We then describe how kinematic-based measures can overcome many of these challenges and highlight a statistical approach to quantify kinematic measures of behavior based on performance of neurologically healthy individuals. We illustrate this approach with a visually-guided reaching task to highlight measures of impairment for individuals following stroke. Finally, there has been considerable controversy about the calculation of motor recovery following stroke. Here, we highlight how our statistical-based approach can provide an effective estimate of impairment and recovery.

Background: The classical description of poststroke upper limb impairment follows a proximalto-distal impairment gradient. Previous studies are equivocal on whether the hand is more impaired than the arm.Objective: To compare impairment of the arm and hand during subacute stroke.Method: A total of 73 individuals were evaluated for impairment of the upper limb within 30 days (early subacute) and within 90–150 days (late subacute) of stroke. Impairments were quantified using the Chedoke-McMaster Stroke Assessment (CMSA) for the arm and hand, Purdue Pegboard task, and a robotic Visually Guided Reaching task.Results: In the early phase 42% of participants in the early phase and 59% in the late phase received the same CMSA score for the arm and hand, with 88% and 95% of participants in the early and late phases, respectively, receiving a 1-point difference. Strong correlations exist between the CMSA arm and hand scores (early r = 0.79, late r = 0.75), and moderate – strong correlations exist between CMSA arm and hand scores and Purdue Pegboard and Visually Guided Reaching performances (r = 0.66–0.81). No systematic differences were found between the arm and hand.Conclusion: Impairments in the arm and hand during subacute stroke are highly correlated and do not support the presence of a proximal-to-distal gradient. LAY ABSTRACTMotor impairments are a common occurrence after stroke, and are classically believed to present in a gradient from more impairment in the hand to less impairment in the arm. In this study, participants who had recently had a stroke underwent assessment with the Chedoke-McMaster Stroke Assessment, the Purdue Pegboard task, and a Visually Guided Reaching task to quantify impairment and performance of the arm and hand. Levels of impairment in the arm and hand, as measured with the Chedoke-McMaster Stroke Assessment, were found to be highly correlated. The study also showed strong correlations between quantitative measures of performance for both the arm and hand. Overall, our results do not support the presence of a proximal-to-distal gradient of impairment during subacute stroke.

A common problem in motor control concerns how to generate patterns of muscle activity when there are redundant solutions to attain a behavioral goal. Optimal feedback control is a theory that has guided many behavioral studies exploring how the motor system incorporates task redundancy. This theory predicts that kinematic errors that deviate the limb should not be corrected if one can still attain the behavioral goal. Studies in humans demonstrate that the motor system can flexibly integrate visual and proprioceptive feedback of the limb with goal redundancy within 90 ms and 70 ms, respectively. Here, we show monkeys (Macaca mulatta) demonstrate similar abilities to exploit goal redundancy. We trained four male monkeys to reach for a goal that was either a narrow square or a wide, spatially redundant rectangle. Monkeys exhibited greater trial-by-trial variability when reaching to the wide goal consistent with exploiting goal redundancy. On random trials we jumped the visual feedback of the hand and found monkeys corrected for the jump when reaching to the narrow goal and largely ignored the jump when reaching for the wide goal. In a separate set of experiments, we applied mechanical loads to the arm of the monkey and found similar corrective responses based on goal shape. Muscle activity reflecting these different corrective responses were detected for the visual and mechanical perturbations starting at ∼90 and ∼70 ms, respectively. Thus, rapid motor responses in macaques can exploit goal redundancy similar to humans, creating a paradigm to study the neural basis of goal-directed motor action and motor redundancy.SIGNIFICANCE STATEMENTMoving in the world requires selecting from an infinite set of possible motor commands. Theories predict that motor commands are selected that exploit redundancies. Corrective responses in humans to either visual or proprioceptive disturbances of the limb can rapidly exploit redundant trajectories to a goal in

Objective The objective of this study was to assess the feasibility of delivering Art skill-based Rehabilitation Training (ART), a novel upper limb motor training program, to patients with stroke as an adjunct to standard care in an inpatient setting. Design Feasibility study. Setting Inpatient stroke rehabilitation unit at a university hospital. Participants Thirty-eight patients admitted to a stroke rehabilitation unit with upper limb motor impairment were enrolled in the ART program facilitated by trained non-healthcare professionals between December 2017 and June 2021. Intervention The ART program included nine, one-hour sessions of supervised tracing and freehand drawing tasks completed with both hands. This program was intended to be delivered at a frequency of three times per week over a duration of 3 weeks or for the length of inpatient stay. Main outcome measures Feasibility outcomes included ART program adherence, acceptability, and safety. Results Thirty-two (84%) participants with subacute stroke completed the ART program and 30 (79%) were included in the study analysis. Participants completed 93–100% of the ART tasks in a median [IQR] of 8 [6–10] ART sessions over a median [IQR] duration of 15 [7–19] days. ART program facilitators effectively provided upper limb assistance to patients with more severe upper limb impairments. Adherence and acceptability were high and no study-related adverse events occurred. Conclusion The ART program was feasible to deliver and highly acceptable to patients with stroke. Further research is warranted to explore the impact of ART on upper limb sensorimotor function and use.

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.

Studies of ongoing, rapid motor behaviors have often focused on the decision-making implicit in the task. Here, we instead study how decision-making integrates with the perceptual and motor systems and propose a framework of limited-capacity, pipelined processing with flexible resources to understand rapid motor behaviors. Results from three experiments show that human performance is consistent with our framework: participants perform objectively worse as task difficulty increases, and, surprisingly, this drop in performance is largest for the most skilled performers. As well, our analysis shows that the worst-performing participants can perform equally well under increased task demands, which is consistent with flexible neural resources being allocated to reduce bottleneck effects and improve overall performance. We conclude that capacity limits lead to information bottlenecks and that processes like attention help reduce the effects that these bottlenecks have on maximal performance.

General motor and executive functions are integral for tasks of daily living and are typically assessed when quantifying impairment of an individual. Robotic tasks offer highly repeatable and objective measures of motor and cognitive function. Additionally, robotic tasks and measures have been used successfully to quantify impairment of children with cerebral palsy (CP). Many robotic tasks include multiple performance parameters, so interpretation of results and identification of impairment can be difficult, especially when multiple tasks are completed. This study used exploratory factor analysis to investigate a potential set of quantitative models of motor and cognitive function in children, and compare performance of participants with CP to these models. The three calculated factors achieved strong differentiation between participants with mild CP and the typically developing population. This demonstrates the feasibility of these factors to quantify impairment and track improvements related to therapies.Clinical Relevance- This establishes a method to differentiate atypical motor performance related to CP using a robotic reversed visually guided reaching task.

BackgroundKinarm Standard Tests (KSTs) is a suite of upper limb tasks to assess sensory, motor, and cognitive functions, which produces granular performance data that reflect spatial and temporal aspects of behavior (>100 variables per individual). We have previously used principal component analysis (PCA) to reduce the dimensionality of multivariate data using the Kinarm End-Point Lab (EP). Here, we performed PCA using data from the Kinarm Exoskeleton Lab (EXO), and determined agreement of PCA results across EP and EXO platforms in healthy participants. We additionally examined whether further dimensionality reduction was possible by using PCA across behavioral tasks.MethodsHealthy participants were assessed using the Kinarm EXO (N = 469) and EP (N = 170–200). Four behavioral tasks (six assessments in total) were performed that quantified arm sensory and motor function, including position sense [Arm Position Matching (APM)] and three motor tasks [Visually Guided Reaching (VGR), Object Hit (OH), and Object Hit and Avoid (OHA)]. The number of components to include per task was determined from scree plots and parallel analysis, and rotation type (orthogonal vs. oblique) was decided on a per-task basis. To assess agreement, we compared principal components (PCs) across platforms using distance correlation. We additionally considered inter-task interactions in EXO data by performing PCA across all six behavioral assessments.ResultsBy applying PCA on a per task basis to data collected using the EXO, the number of behavioral parameters were substantially reduced by 58–75% while accounting for 76–87% of the variance. These results compared well to the EP analysis, and we found good-to-excellent agreement values (0.75–0.99) between PCs from the EXO and those from the EP. Finally, we were able to reduce the dimensionality of the EXO data across tasks down to 16 components out of a total of 76 behavioral parameters, which represents a reduction of 79% while accounting for 73% of the total variance.ConclusionPCA of Kinarm robotic assessment appears to capture similar relationships between kinematic features in healthy individuals and is agnostic to the robotic platform used for collection. Further work is needed to investigate the use of PCA-based data reduction for the characterization of neurological deficits in clinical populations.

Stroke is a leading cause of death and disability worldwide and survivors are frequently left with long-term disabilities that diminish their autonomy and result in the need for chronic care. There is an urgent need for the development of therapies that improve stroke recovery, as well as accurate and quantitative tools to measure function. Nonhuman primates closely resemble humans in neuroanatomy and upper limb function and may be crucial in randomized pre-clinical trials for testing the efficacy of stroke therapies. To test the feasibility of robotic assessment of motor function in a NHP model of stroke, two cynomolgus macaques were trained to perform a visually guided reaching task and were also assessed in a passive stretch task using the Kinarm robot. Strokes were then induced in these animals by transiently occluding the middle cerebral artery, and their motor performance on the same tasks was assessed after recovery. Relative to pre-stroke performance, post-stroke hand movements of the affected limb became slower and less accurate. Regression analyses revealed both recovered and compensatory movements to complete movements in different spatial directions. Lastly, we noted decreased range of motion in the elbow joint of the affected limb post-stroke associated with spasticity during passive stretch. Taken together, these studies highlight that sensorimotor deficits in reaching movements following stroke in cynomolgus macaques resemble those in human patients and validate the use of robotic assessment tools in a nonhuman primate model of stroke for identifying and characterizing such deficits.