Robot-based assessment of motor and proprioceptive function identifies biomarkers for prediction of functional independence measures

Clinical, Neuroimaging and Robotic Measures Predict Long-Term Proprioceptive Impairments following Stroke

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.

A randomized clinical control study on the efficacy of three-dimensional upper limb robotic exoskeleton training in chronic stroke

Background

Although robotics assisted rehabilitation has proven to be effective in stroke rehabilitation, a limited functional improvements in Activities of Daily Life has been also observed after the administration of robotic training. To this aim in this study we compare the efficacy in terms of both clinical and functional outcomes of a robotic training performed with a multi-joint functional exoskeleton in goal-oriented exercises compared to a conventional physical therapy program, equally matched in terms of intensity and time. As a secondary goal of the study, it was assessed the capability of kinesiologic measurements—extracted by the exoskeleton robotic system—of predicting the rehabilitation outcomes using a set of robotic biomarkers collected at the baseline.

Methods

A parallel-group randomized clinical trial was conducted within a group of 26 chronic post-stroke patients. Patients were randomly assigned to two groups receiving robotic or manual therapy. The primary outcome was the change in score on the upper extremity section of the Fugl-Meyer Assessment (FMA) scale. As secondary outcome a specifically designed bimanual functional scale, Bimanual Activity Test (BAT), was used for upper limb functional evaluation. Two robotic performance indices were extracted with the purpose of monitoring the recovery process and investigating the interrelationship between pre-treatment robotic biomarkers and post-treatment clinical improvement in the robotic group.

Results

A significant clinical and functional improvements in both groups (p < 0.01) was reported. More in detail a significantly higher improvement of the robotic group was observed in the proximal portion of the FMA (p < 0.05) and in the reduction of time needed for accomplishing the tasks of the BAT (p < 0.01). The multilinear-regression analysis pointed out a significant correlation between robotic biomarkers at the baseline and change in FMA score (R² = 0.91, p < 0.05), suggesting their potential ability of predicting clinical outcomes.

Conclusion

Exoskeleton-based robotic upper limb treatment might lead to better functional outcomes, if compared to manual physical therapy. The extracted robotic performance could represent predictive indices of the recovery of the upper limb. These results are promising for their potential exploitation in implementing personalized robotic therapy.

Clinical Trial Registration clinicaltrials.gov, NCT03319992 Unique Protocol ID: RH-UL-LEXOS-10. Registered 20.10.2017, https://clinicaltrials.gov/ct2/show/NCT03319992

Assessing visually guided reaching in people with multiple sclerosis with and without self-reported upper limb impairment

The ability to accurately complete goal-directed actions, such as reaching for a glass of water, requires coordination between sensory, cognitive and motor systems. When these systems are impaired, like in people with multiple sclerosis (PwMS), deficits in movement arise. To date, the characterization of upper limb performance in PwMS has typically been limited to results attained from self-reported questionnaires or clinical tools. Our aim was to characterize visually guided reaching performance in PwMS. Thirty-six participants (12 PwMS who reported upper limb impairment (MS-R), 12 PwMS who reported not experiencing upper limb impairment (MS-NR), and 12 age- and sex-matched control participants without MS (CTL)) reached to 8 targets in a virtual environment while seeing a visual representation of their hand in the form of a cursor on the screen. Reaches were completed with both the dominant and non-dominant hands. All participants were able to complete the visually guided reaching task, such that their hand landed on the target. However, PwMS showed noticeably more atypical reaching profiles when compared to control participants. In accordance with these observations, analyses of reaching performance revealed that the MS-R group was more variable with respect to the time it took to initiate and complete their movements compared to the CTL group. While performance of the MS-NR group did not differ significantly from either the CTL or MS-R groups, individuals in the MS-NR group were less consistent in their performance compared to the CTL group. Together these findings suggest that PwMS with and without self-reported upper limb impairment have deficits in the planning and/or control of their movements. We further argue that deficits observed during movement in PwMS who report upper limb impairment may arise due to participants compensating for impaired movement planning processes.

Associations between Upper Extremity Motor Function and Aphasia after Stroke: A Multicenter Cross-Sectional Study

METHODS

Patients with stroke were compared and correlated from overall and three periods (1-3 months, 4-6 months, and >6 months). Fugl-Meyer assessment for the upper extremity (FMA-UE) and action research and arm test (ARAT) were used to compare the UE motor status between patients with PSA and without PSA through a cross-sectional study among 435 patients. Then, the correlations between the evaluation scale scores of UE motor status and language function of patients with PSA were analyzed in various dimensions, and the language subfunction most closely related to UE motor function was analyzed by multiple linear regression analysis.

RESULTS

We found that the scores of FMA-UE and ARAT in patients with PSA were 14 points ((CI) 10 to 18, p < 0.001) and 11 points lower ((CI) 8 to 13, p < 0.001), respectively, than those without PSA. Their FMA-UE (r = 0.70, p < 0.001) and ARAT (r = 0.62, p < 0.001) scores were positively correlated with language function. Regression analysis demonstrated that spontaneous speech ability may account for UE motor function (R 2 = 0.51, p < 0.001; R 2 = 0.42, p < 0.001). Consistent results were also obtained from the analyses within the three time subgroups.

CONCLUSION

Stroke patients with PSA have worse UE motor performance. UE motor status and language function showed positive correlations, in which spontaneous speech ability significantly accounts for the associations.

Assistive Sliding Mode Control of a Rehabilitation Robot with Automatic Weight Adjustment

There are approximately 13 million new stroke cases worldwide each year. Research has shown that robotics can provide practical and efficient solutions for expediting post-stroke patient recovery. This simulation study aimed to design a sliding mode controller (SMC) for an end-effector-based rehabilitation robot. A genetic algorithm (GA) was designed for automatic controller weight adjustment. The optimal weights were obtained by minimizing a cost function comprising the end-effector position error, robot input, robot input-rate, and patient input. To promote safe tuner optimization, a model of the human arm was incorporated to generate the human joint torque. A computed-torque proportional derivative controller (CTPD) was designed for the human arm to approximate the central nervous system (CNS) motor control. This controller was adjusted to simulate rehabilitation effects and patient adaptation. The tuner was optimized for a trajectory tracking task with an assistive high-level control scheme. The simulation results showed lower cost compared to seven manual weight settings. The optimal weights provided good tracking performance and suitable robot inputs. This research provides a framework to conduct various simulations before testing our controller on human subjects. The preliminary results of this study will be used as the starting point for online adaptive controller tuning, which will be examined in our future research.

A Data-Driven Investigation on Surface Electromyography Based Clinical Assessment in Chronic Stroke

Background: Surface electromyography (sEMG) based robot-assisted rehabilitation systems have been adopted for chronic stroke survivors to regain upper limb motor function. However, the evaluation of rehabilitation effects during robot-assisted intervention relies on traditional manual assessments. This study aimed to develop a novel sEMG data-driven model for automated assessment. Method: A data-driven model based on a three-layer backpropagation neural network (BPNN) was constructed to map sEMG data to two widely used clinical scales, i.e., the Fugl-Meyer Assessment (FMA) and the Modified Ashworth Scale (MAS). Twenty-nine stroke participants were recruited in a 20-session sEMG-driven robot-assisted upper limb rehabilitation, which consisted of hand reaching and withdrawing tasks. The sEMG signals from four muscles in the paretic upper limbs, i.e., biceps brachii (BIC), triceps brachii (TRI), flexor digitorum (FD), and extensor digitorum (ED), were recorded before and after the intervention. Meanwhile, the corresponding clinical scales of FMA and MAS were measured manually by a blinded assessor. The sEMG features including Mean Absolute Value (MAV), Zero Crossing (ZC), Slope Sign Change (SSC), Root Mean Square (RMS), and Wavelength (WL) were adopted as the inputs to the data-driven model. The mapped clinical scores from the data-driven model were compared with the manual scores by Pearson correlation. Results: The BPNN, with 15 nodes in the hidden layer and sEMG features, i.e., MAV, ZC, SSC, and RMS, as the inputs to the model, was established to achieve the best mapping performance with significant correlations (r > 0.9, P < 0.001), according to the FMA. Significant correlations were also obtained between the mapped and manual FMA subscores, i.e., FMA-wrist/hand and FMA-shoulder/elbow, before and after the intervention (r > 0.9, P < 0.001). Significant correlations (P < 0.001) between the mapped and manual scores of MASs were achieved, with the correlation coefficients r = 0.91 at the fingers, 0.88 at the wrist, and 0.91 at the elbow after the intervention. Conclusion: An sEMG data-driven BPNN model was successfully developed. It could evaluate upper limb motor functions in chronic stroke and have potential application in automated assessment in post-stroke rehabilitation, once validated with large sample sizes. Clinical Trial Registration: www.ClinicalTrials.gov, identifier: NCT02117089.

Key components of mechanical work predict outcomes in robotic stroke therapy

Background

Clinical practice typically emphasizes active involvement during therapy. However, traditional approaches can offer only general guidance on the form of involvement that would be most helpful to recovery. Beyond assisting movement, robots allow comprehensive methods for measuring practice behaviors, including the energetic input of the learner. Using data from our previous study of robot-assisted therapy, we examined how separate components of mechanical work contribute to predicting training outcomes.

Methods

Stroke survivors (n = 11) completed six sessions in two-weeks of upper extremity motor exploration (self-directed movement practice) training with customized forces, while a control group (n = 11) trained without assistance. We employed multiple regression analysis to predict patient outcomes with computed mechanical work as independent variables, including separate features for elbow versus shoulder joints, positive (concentric) and negative (eccentric), flexion and extension.

Results

Our analysis showed that increases in total mechanical work during therapy were positively correlated with our final outcome metric, velocity range. Further analysis revealed that greater amounts of negative work at the shoulder and positive work at the elbow as the most important predictors of recovery (using cross-validated regression, R² = 52%). However, the work features were likely mutually correlated, suggesting a prediction model that first removed shared variance (using PCA, R² = 65–85%).

Conclusions

These results support robotic training for stroke survivors that increases energetic activity in eccentric shoulder and concentric elbow actions.

Trial registration

ClinicalTrials.gov, Identifier: NCT02570256. Registered 7 October 2015 – Retrospectively registered,

The sensorimotor effects of a lower limb proprioception training intervention in individuals with a spinal cord injury

Proprioception is critical for movement control. After a spinal cord injury (SCI), individuals not only experience paralysis but may also experience proprioceptive deficits, further confounding motor recovery. The objective of this study was to test the effects of a robotic-based proprioception training protocol on lower limb proprioceptive sense in people with incomplete SCI. A secondary objective was to assess whether the effects of training transferred to a precision stepping task in people with motor-incomplete SCI. Participants with chronic incomplete SCI and able-bodied controls underwent a 2-day proprioceptive training protocol using the Lokomat robotic exoskeleton. The training involved positioning the test leg to various positions and participants were asked to report whether they felt their heel position (end-point position) was higher or lower compared with a reference position. Feedback was provided after each trial to help participants learn strategies that could help them discern different positions of their foot. Changes in end-point position as well as knee joint position sense were assessed pre- and posttraining. We also assessed the effects of proprioception training on the performance of a precision stepping task in people with motor-incomplete SCI. Following training, there were significant improvements in end-point and knee joint position sense in both groups. The magnitude of improvement was related to pretraining (baseline) proprioceptive sense, indicating that those who initially had better lower limb position sense showed greater changes. Participants also showed improvements in performance of a precision stepping task.

NEW & NOTEWORTHY We show that it is possible to alter proprioceptive sense in people with incomplete SCI using a passive proprioception training protocol combined with feedback. Improvements in proprioceptive sense transferred from end-point to joint position sense and also to an untrained precision stepping task.

Differential Impact of Acute Lesions Versus White Matter Hyperintensities on Stroke Recovery

Background

Understanding how the size of acute lesions and white matter hyperintensities (WMH) impact stroke recovery can improve our ability to predict outcomes and tailor treatments. The aim of this exploratory study was to investigate the role of acute lesion volume and WMH volume on longitudinal recovery of specific sensory, motor, and cognitive impairments after stroke using robotic and clinical measures.

Methods and Results

Eighty‐two individuals were assessed at 1, 6, 12, and 26 weeks poststroke with robotic tasks and commonly used clinical measures. The volumes of acute lesions and WMH were measured on fluid‐attenuated inversion recovery images. Linear mixed models were used to investigate the role of acute lesions and WMH on parameters derived from the robotic tasks and clinical measures. Regression analysis determined the added value of acute lesion and WMH volumes along with measures of initial performance to predict outcomes at 6 months. Acute lesion volume has widespread effects on sensory, motor, and overall functional recovery poststroke. The impact of WMH was specific to cognitive impairments. Apart from the robotic position sense task, neither lesion volume nor WMH measure had significant ability to predict outcomes at 6 months over using initial impairment as measured by robotic assessments alone.

Conclusions

While acute lesion volume and WMH may impact different impairments poststroke, their clinical utility in predicting outcomes at 6 months poststroke is limited.

Regression techniques employing feature selection to predict clinical outcomes in stroke

It is not fully clear which measurable factors can reliably predict chronic stroke patients’ recovery of motor ability. In this analysis, we investigate the impact of patient demographic characteristics, movement features, and a three-week upper-extremity intervention on the post-treatment change in two widely used clinical outcomes—the Upper Extremity portion of the Fugl-Meyer and the Wolf Motor Function Test. Models based on LASSO, which in validation tests account for 65% and 86% of the variability in Fugl-Meyer and Wolf, respectively, were used to identify the set of salient demographic and movement features. We found that age, affected limb, and several measures describing the patient’s ability to efficiently direct motions with a single burst of speed were the most consequential in predicting clinical recovery. On the other hand, the upper-extremity intervention was not a significant predictor of recovery. Beyond a simple prognostic tool, these results suggest that focusing therapy on the more important features is likely to improve recovery. Such validation-intensive methods are a novel approach to determining the relative importance of patient-specific metrics and may help guide the design of customized therapy.

Smallest real differences for robotic measures of upper extremity function after stroke: Implications for tracking recovery

Introduction

Measurements from upper limb rehabilitation robots could guide therapy progression, if a robotic assessment’s measurement error was small enough to detect changes occurring on a time scale of a few days. To guide this determination, this study evaluated the smallest real differences of robotic measures, and of clinical outcome assessments predicted from these measures.

Methods

A total of nine older chronic stroke survivors took part in 12-week study with an upper-limb end-effector robot. Fourteen robotic measures were extracted, and used to predict Fugl-Meyer Assessment-Upper Extremity (FMA-UE) and Action Research Arm Test (ARAT) scores using multilinear regression. Smallest real differences and intraclass correlation coefficients were computed for the robotic measures and predicted clinical outcomes, using data from seven baseline sessions.

Results

Smallest real differences of robotic measures ranged from 8.8% to 26.9% of the available range. Smallest real differences of predicted clinical assessments varied widely depending on the regression model (1.3 to 36.2 for FMA-UE, 1.8 to 59.7 for ARAT), and were not strongly related to a model’s predictive performance or to the smallest real differences of the model inputs. Models with acceptable predictive performance as well as low smallest real differences were identified.

Conclusions

Smallest real difference evaluations suggest that using robotic assessments to guide therapy progression is feasible.

Analysis and Quantification of Repetitive Motion in Long-Term Rehabilitation

Objective assessment in long-term rehabilitation under real-life recording conditions is a challenging task. We propose a data-driven method to evaluate changes in motor function under uncontrolled, long-term conditions with the low-cost Microsoft Kinect sensor. Instead of using human ratings as ground truth data, we propose kinematic features of hand motion, healthy reference trajectories derived by principal component regression, and methods taken from machine learning to analyze the progression of motor function. We demonstrate the capability of this approach on datasets with repetitive unrestrained bi-manual drumming movements in three-dimensional space of stroke survivors, patients suffering of Parkinson's disease, and a healthy control group. We present processing steps to eliminate the influence of varying recording setups under real-life conditions and offer visualization methods to support clinicians in the evaluation of treatment effects.

Energetics during robot-assisted training predicts recovery in stroke

Clinical investigators have asserted patients should be active participants in the therapy process in stroke rehabilitation. While robotics introduces new tools for measurement and treatment of motor impairments, it also presents challenges for evaluating how much a patient contributes to observed movements during training. Our approach employs established methods of inverse dynamics combined with measurements of human motion and interaction forces between the human and robot. Here, we investigated whether measures of patient active involvement predict the level of upper limb recovery due to robot-assisted therapy. Stroke survivors (n=11) completed "exploration" training with customizable forces that increased their velocities (i.e., negative damping). While our results showed a mild trend between mechanical work during training and expanded velocity capability (Pearson r = 0.57), we found significant correlations with the amount of positive work (i.e., propulsion; r = 0.77), but not negative work (i.e., braking; r = 0.41). This work supports robotic tools that encourage more positive work.

Technological Approaches for Neurorehabilitation: From Robotic Devices to Brain Stimulation and Beyond

Neurological diseases causing motor/cognitive impairments are among the most common causes of adult-onset disability. More than one billion of people are affected worldwide, and this number is expected to increase in upcoming years, because of the rapidly aging population. The frequent lack of complete recovery makes it desirable to develop novel neurorehabilitative treatments, suited to the patients, and better targeting the specific disability. To date, rehabilitation therapy can be aided by the technological support of robotic-based therapy, non-invasive brain stimulation, and neural interfaces. In this perspective, we will review the above methods by referring to the most recent advances in each field. Then, we propose and discuss current and future approaches based on the combination of the above. As pointed out in the recent literature, by combining traditional rehabilitation techniques with neuromodulation, biofeedback recordings and/or novel robotic and wearable assistive devices, several studies have proven it is possible to sensibly improve the amount of recovery with respect to traditional treatments. We will then discuss the possible applied research directions to maximize the outcome of a neurorehabilitation therapy, which should include the personalization of the therapy based on patient and clinician needs and preferences.

Robotic exoskeleton assessment of transient ischemic attack

We used a robotic exoskeleton to quantify specific patterns of abnormal upper limb motor behaviour in people who have had transient ischemic attack (TIA). A cohort of people with TIA was recruited within two weeks of symptom onset. All individuals completed a robotic-based assessment of 8 behavioural tasks related to upper limb motor and proprioceptive function, as well as cognitive function. Robotic task performance was compared to a large cohort of controls without neurological impairments corrected for the influence of age. Impairment in people with TIA was defined as performance below the 5^th percentile of controls. Participants with TIA were also assessed with the National Institutes of Health Stroke Scale (NIHSS) score, Chedoke-McMaster Stroke Assessment (CMSA) of the arm, the Behavioural Inattention Test (BIT), the Purdue pegboard test (PPB), and the Montreal Cognitive Assessment (MoCA). Age-related white matter change (ARWMC), prior infarction and cella-media index (CMI) were assessed from baseline CT scan that was performed within 24 hours of TIA. Acute infarction was assessed from diffusion-weighted imaging in a subset of people with TIA. Twenty-two people with TIA were assessed. Robotic assessment showed impaired upper limb motor function in 7/22 people with TIA patients and upper limb sensory impairment in 4/22 individuals. Cognitive tasks involving robotic assessment of the upper limb were completed in 13 participants, of whom 8 (61.5%) showed significant impairment. Abnormal performance in the CMSA arm inventory was present in 12/22 (54.5%) participants. ARWMC was 11.8 ± 6.4 and CMI was 5.4 ± 1.5. DWI was positive in 0 participants. Quantitative robotic assessment showed that people who have had a TIA display a spectrum of upper limb motor and sensory performance deficits as well as cognitive function deficits despite resolution of symptoms and no evidence of tissue infarction.

Low-Cost Robotic Assessment of Visuo-Motor Deficits in Alzheimer's Disease

A low-cost robotic interface was used to assess the visuo-motor performance of patients with Alzheimer's disease (AD). Twenty AD patients and twenty age-matched controls participated in this work. The battery of tests included simple reaction times, position tracking, and stabilization tasks performed with both hands. The regularity, velocity, visual and haptic feedback were manipulated to vary movement complexity. Reaction times and movement tracking error were analyzed. Results show a marked group effect on a subset of conditions, in particular when the patients could not rely on the visual feedback of hand movement. The visuo-motor performance correlated with the measures of global cognitive functioning and with different memory-related abilities. Our results support the hypothesis that the ability to recall and use visuo-spatial associations might underlie the impairment in complex motor behavior that has been reported in AD patients. Importantly, the patients had preserved learning effects across sessions, which might relate to visuo-motor deficits being less evident in every-day life and clinical assessments. This robotic assessment, lasting less than 1 h, provides detailed information about the integrity of visuo-motor abilities. The data can aid the understanding of the complex pattern of deficits that characterizes this pervasive disease.

Reduction of Assessment Time for Stroke-Related Impairments Using Robotic Evaluation

Robotic technologies can provide objective, reliable tools for assessing a broad range of sensory, motor and cognitive functions. However, as additional tasks are developed on these platforms, the time necessary to assess a patient increases. In this paper, we present a hierarchical task selection strategy for five tasks that form part of the battery of standard tests performed on the KINARM robotic system. The strategy is built using dependencies derived through three types of analyses: 1) non-linear hierarchical ordering theory is applied to determine the ordering of five tasks; 2) the parameters of all tasks are also ranked using non-linear hierarchical ordering theory; and 3) a modeling technique, fast orthogonal search, is applied to assess the predictive power of each robotic task for the estimation of other task parameters. The inferred hierarchical task selection strategy can lead to a reduction of up to 91% of the time required to assess a patient.