Kinematic robot-based evaluation scales and clinical counterparts to measure upper limb motor performance in patients with chronic stroke

Computer Vision for Gait Assessment in Cerebral Palsy: Metric Learning and Confidence Estimation

Assessing the motor impairments of individuals with neurological disorders holds significant importance in clinical practice. Currently, these clinical assessments are time-intensive and depend on qualitative scales administered by trained healthcare professionals at the clinic. These evaluations provide only coarse snapshots of a person's abilities, failing to track quantitatively the detail and minutiae of recovery over time. To overcome these limitations, we introduce a novel machine learning approach that can be administered anywhere including home. It leverages a spatial-temporal graph convolutional network (STGCN) to extract motion characteristics from pose data obtained from monocular video captured by portable devices like smartphones and tablets. We propose an end-to-end model, achieving an accuracy rate of approximately 76.6% in assessing children with Cerebral Palsy (CP) using the Gross Motor Function Classification System (GMFCS). This represents a 5% improvement in accuracy compared to the current state-of-the-art techniques and demonstrates strong agreement with professional assessments, as indicated by the weighted Cohen's Kappa ( κlw = 0.733 ). In addition, we introduce the use of metric learning through triplet loss and self-supervised training to better handle situations with a limited number of training samples and enable confidence estimation. Setting a confidence threshold at 0.95 , we attain an impressive estimation accuracy of 88% . Notably, our method can be efficiently implemented on a wide range of mobile devices, providing real-time or near real-time results.

Measurement properties of movement smoothness metrics for upper limb reaching movements in people with moderate to severe subacute stroke

BACKGROUND

Movement smoothness is a potential kinematic biomarker of upper extremity (UE) movement quality and recovery after stroke; however, the measurement properties of available smoothness metrics have been poorly assessed in this group. We aimed to measure the reliability, responsiveness and construct validity of several smoothness metrics.

METHODS

This ancillary study of the REM-AVC trial included 31 participants with hemiparesis in the subacute phase of stroke (median time since stroke: 38 days). Assessments performed at inclusion (Day 0, D0) and at the end of a rehabilitation program (Day 30, D30) included the UE Fugl Meyer Assessment (UE-FMA), the Action Research Arm Test (ARAT), and 3D motion analysis of the UE during three reach-to-point movements at a self-selected speed to a target located in front at shoulder height and at 90% of arm length. Four smoothness metrics were computed: a frequency domain smoothness metric, spectral arc length metric (SPARC); and three temporal domain smoothness metrics (TDSM): log dimensionless jerk (LDLJ); number of submovements (nSUB); and normalized average rectified jerk (NARJ).

RESULTS

At D30, large clinical and kinematic improvements were observed. Only SPARC and LDLJ had an excellent reliability (intra-class correlation > 0.9) and a low measurement error (coefficient of variation < 10%). SPARC was responsive to changes in movement straightness (rSpearman=0.64) and to a lesser extent to changes in movement duration (rSpearman=0.51) while TDSM were very responsive to changes in movement duration (rSpearman>0.8) and not to changes in movement straightness (non-significant correlations). Most construct validity hypotheses tested were verified except for TDSM with low correlations with clinical metrics at D0 (rSpearman<0.5), ensuing low predictive validity with clinical metrics at D30 (non-significant correlations).

CONCLUSIONS

Responsiveness and construct validity of TDSM were hindered by movement duration and/or noise-sensitivity. Based on the present results and concordant literature, we recommend using SPARC rather than TDSM in reaching movements of uncontrolled duration in individuals with spastic paresis after stroke.

TRIAL REGISTRATION

NCT01383512, https://clinicaltrials.gov/ , June 27, 2011.

Technological Advances in Stroke Rehabilitation: Robotics and Virtual Reality

Robotic technology and virtual reality (VR) have been widely studied technologies in stroke rehabilitation over the last few decades. Both technologies have typically been considered as ways to enhance recovery through promoting intensive, repetitive, and engaging therapies. In this review, we present the current evidence from interventional clinical trials that employ either robotics, VR, or a combination of both modalities to facilitate post-stroke recovery. Broadly speaking, both technologies have demonstrated some success in improving post-stroke outcomes and complementing conventional therapy. However, more high-quality, randomized, multicenter trials are required to confirm our current understanding of their role in precision stroke recovery.

Resistive versus active assisted robotic training for the upper limb after a stroke: A randomized controlled study

BACKGROUND

Selection of a suitable training modality according to the status of upper limb function can maximize the effects of robotic rehabilitation; therefore, it is necessary to identify the optimal training modality.

OBJECTIVES

This study aimed to compare robotic rehabilitation approaches incorporating either resistance training (RET) or active-assisted training (AAT) using the same rehabilitation robot in people with stroke and moderate impairment.

METHODS

In this randomized controlled trial, we randomly allocated 34 people with stroke who had moderate impairment to either the experimental group (RET, n = 18) or the control group (AAT, n = 16). Both groups performed robot-assisted therapy for 30 min, 5 days per week, for 4 weeks. The same rehabilitation robot provided resistance to the RET group and assistance to the AAT group. Body function and structure, activity, and participation outcomes were evaluated before, during, and after the intervention.

RESULTS

RET led to greater improvements than AAT in terms of smoothness (p = 0.006). The Fugl-Meyer Assessment (FMA)-upper extremity (p < 0.001), FMA-proximal (p < 0.001), Action Research Arm Test-gross movement (p = 0.011), and kinematic variables of joint independence (p = 0.017) and displacement (p = 0.011) also improved at the end of intervention more in the RET group.

CONCLUSIONS

Robotic RET was more effective than AAT in improving upper limb function, structure, and activity among participants with stroke who had moderate impairment.

Evaluation of finger strength and spasticity in hemiplegic patients using hand-finger robotic device: A validity and reliability study

We aimed to investigate the validity, reliability, and clinical relevance of Amadeo hand-finger robotic rehabilitation system measurements for evaluating spasticity and strength in hemiplegic patients. In total, 161 participants (107 hemiplegic patients and 54 sex- and age-matched healthy controls) were included in this study. Spasticity was evaluated using the Modified Ashworth Scale, hand motor functions were evaluated using the Fugl-Meyer Assessment Hand Subscale, and hand grip and pinch strength were evaluated using the Jamar hand grip and pinch dynamometer. The Amadeo (Tyromotion) hand-finger robotic rehabilitation system was used to evaluate finger spasticity and strength of the participants. A statistically significant difference was found between the median values of the Modified Ashworth Scale (both clinical and robotic evaluation results) and the mean values of hand flexor and extensor strength measured with the robotic device in patients compared to healthy subjects (P < .01). Statistically, excellent agreement was obtained between the clinical and robotic test-retest results of the scale (P < .01) (intra-class correlation coefficient, ICC = .98-.99; ICC = .98-.99, respectively). There was a statistically significant positive correlation between the clinical and robotic device results of the Modified Ashworth Scale (r = .72; P < .01). There was a statistically significant positive correlation between the hand strength values measured with the robotic device, Jamar grip, pinch, and Fugl-Meyer Assessment Hand Subscale scores (P < .01) in the patient group. Hand finger spasticity and strength measurements of the Amadeo hand-finger robotic rehabilitation system were valid, reliable, and clinically correlated in stroke patients.

Utilizing the intelligence edge framework for robotic upper limb rehabilitation in home

Robotic devices are gaining popularity for the physical rehabilitation of stroke survivors. Transition of these robotic systems from research labs to the clinical setting has been successful, however, providing robot-assisted rehabilitation in home settings remains to be achieved. In addition to ensure safety to the users, other important issues that need to be addressed are the real time monitoring of the installed instruments, remote supervision by a therapist, optimal data transmission and processing. The goal of this paper is to advance the current state of robot-assisted in-home rehabilitation. A state-of-the-art approach to implement a novel paradigm for home-based training of stroke survivors in the context of an upper limb rehabilitation robot system is presented in this paper. First, a cost effective and easy-to-wear upper limb robotic orthosis for home settings is introduced. Then, a framework of the internet of robotics things (IoRT) is discussed together with its implementation. Experimental results are included from a proof-of-concept study demonstrating that the means of absolute errors in predicting wrist, elbow and shoulder angles are 0.8918 0 , 2.6753 0 and 8.0258 0 , respectively. These experimental results demonstrate the feasibility of a safe home-based training paradigm for stroke survivors. The proposed framework will help overcome the technological barriers, being relevant for IT experts in health-related domains and pave the way to setting up a telerehabilitation system increasing implementation of home-based robotic rehabilitation. The proposed novel framework includes:•A low-cost and easy to wear upper limb robotic orthosis which is suitable for use at home.•A paradigm of IoRT which is used in conjunction with the robotic orthosis for home-based rehabilitation.•A machine learning-based protocol which combines and analyse the data from robot sensors for efficient and quick decision making.

Uncertainty-aware automated assessment of the arm impedance with upper-limb exoskeletons

Providing high degree of personalization to a specific need of each patient is invaluable to improve the utility of robot-driven neurorehabilitation. For the desired customization of treatment strategies, precise and reliable estimation of the patient's state becomes important, as it can be used to continuously monitor the patient during training and to document the rehabilitation progress. Wearable robotics have emerged as a valuable tool for this quantitative assessment as the actuation and sensing are performed on the joint level. However, upper-limb exoskeletons introduce various sources of uncertainty, which primarily result from the complex interaction dynamics at the physical interface between the patient and the robotic device. These sources of uncertainty must be considered to ensure the correctness of estimation results when performing the clinical assessment of the patient state. In this work, we analyze these sources of uncertainty and quantify their influence on the estimation of the human arm impedance. We argue that this mitigates the risk of relying on overconfident estimates and promotes more precise computational approaches in robot-based neurorehabilitation.

Kinematic analysis of movement patterns during a reach-and-grasp task in stroke patients

Background

This study aimed to evaluate the kinematic movement patterns during a reach-and-grasp task in post-stroke patients according to the upper extremity impairment severity.

Methods

Subacute stroke patients (n = 46) and healthy controls (n = 20) were enrolled in this study. Spatiotemporal and kinematic data were obtained through 3D motion analysis during the reach-and-grasp task. Stroke patients were grouped using the Fugl-Meyer Assessment (FMA) scale, and a comparison of the groups was performed.

Results

The severe group showed a significantly longer movement time, lower peak velocity, and higher number of movement units than the mild group during the reach-and-grasp task (p < 0.05). Characteristic compensatory movement patterns, such as shoulder abduction, thoracic posterior tilting, and upward and external rotation were significantly greater during the forward transporting phase in the severe group than in the mild group (p < 0.05). The FMA score was significantly associated with the movement time during the forward transporting phase, number of movement units during the reaching phase, range of shoulder abduction-adduction and wrist flexion-extension movements during the reaching phase, and range of thoracic internal-external rotation during the backward transporting phase (p < 0.05).

Conclusion

Post-stroke patients have unique spatiotemporal and kinematic movement patterns during a reach-and grasp-task according to the impairment severity.

Spasticity evaluation with the Amadeo Tyromotion device in patients with hemispheric stroke

Objective

The objective of this study is to verify the reliability and the concurrent and discriminant validity of the measurements of spasticity offered by the robotic device, quantifying the (1) test-retest reliability, (2) correlation with the clinical evaluation using the Modified Ashworth Scale (MAS), (3) inter-rater reliability between the two physiotherapists, and (4) ability to discriminate between healthy and stroke patients.

Methods

A total of 20 stroke patients and 20 healthy volunteers participated in the study. Two physical therapists (PT1 and PT2) independently evaluated the hand spasticity of stroke subjects using the MAS. Spasticity was assessed, both in healthy and stroke patients, with the Amadeo device at three increasing velocities of passive movement for three consecutive repeated assessments, while raw data of force and position were collected through an external program.

Data analysis

The intraclass correlation coefficient (ICC) and the weighted kappa were computed to estimate the reliability of the Amadeo device measurements, the inter-rater reliability of MAS, and the correlation between the MAS and Amadeo device measurements. The discriminant ability of the Amadeo device was assessed by comparing the stroke and healthy subjects' spasticity measurements with the percentage of agreements with 0 in MAS for healthy subjects.

Results

The test-retest reliability of the Amadeo device was high with ICC at all three velocities (ICC = 0.908, 0.958, and 0.964, respectively) but lower if analyzed with weighted kappa correlation (0.584, 0.748, and 0.749, respectively) as mean values for each velocity. The correlation between Amadeo and the clinical scale for stroke patients with weighted kappa correlation was poor (0.280 ± 0.212 for PT1 and 0.290 ± 0.155 for PT2). The inter-rater reliability of the clinical MAS was high (ICC = 0.911).

Conclusion

Both MAS and Amadeo spasticity scores showed good reliability. The Amadeo scores did not show a strong clinical correlation with the MAS in stroke patients. Hitherto, Amadeo evaluation shows trends that are consistent with the characteristics of spasticity, such as an increase in spasticity as the speed of muscle stretching increases. The ability of the device to discriminate between stroke patients and healthy controls is low. Future studies adopting an instrumental gold standard for spasticity may provide further insight into the validity of these measurements.

Literature review of stroke assessment for upper-extremity physical function via EEG, EMG, kinematic, and kinetic measurements and their reliability

BACKGROUND

Significant clinician training is required to mitigate the subjective nature and achieve useful reliability between measurement occasions and therapists. Previous research supports that robotic instruments can improve quantitative biomechanical assessments of the upper limb, offering reliable and more sensitive measures. Furthermore, combining kinematic and kinetic measurements with electrophysiological measurements offers new insights to unlock targeted impairment-specific therapy. This review presents common methods for analyzing biomechanical and neuromuscular data by describing their validity and reporting their reliability measures.

METHODS

This paper reviews literature (2000-2021) on sensor-based measures and metrics for upper-limb biomechanical and electrophysiological (neurological) assessment, which have been shown to correlate with clinical test outcomes for motor assessment. The search terms targeted robotic and passive devices developed for movement therapy. Journal and conference papers on stroke assessment metrics were selected using PRISMA guidelines. Intra-class correlation values of some of the metrics are recorded, along with model, type of agreement, and confidence intervals, when reported.

RESULTS

A total of 60 articles are identified. The sensor-based metrics assess various aspects of movement performance, such as smoothness, spasticity, efficiency, planning, efficacy, accuracy, coordination, range of motion, and strength. Additional metrics assess abnormal activation patterns of cortical activity and interconnections between brain regions and muscle groups; aiming to characterize differences between the population who had a stroke and the healthy population.

CONCLUSION

Range of motion, mean speed, mean distance, normal path length, spectral arc length, number of peaks, and task time metrics have all demonstrated good to excellent reliability, as well as provide a finer resolution compared to discrete clinical assessment tests. EEG power features for multiple frequency bands of interest, specifically the bands relating to slow and fast frequencies comparing affected and non-affected hemispheres, demonstrate good to excellent reliability for populations at various stages of stroke recovery. Further investigation is needed to evaluate the metrics missing reliability information. In the few studies combining biomechanical measures with neuroelectric signals, the multi-domain approaches demonstrated agreement with clinical assessments and provide further information during the relearning phase. Combining the reliable sensor-based metrics in the clinical assessment process will provide a more objective approach, relying less on therapist expertise. This paper suggests future work on analyzing the reliability of metrics to prevent biasedness and selecting the appropriate analysis.

Automated and Continuous Fatigue Monitoring in Construction Workers Using Forearm EMG and IMU Wearable Sensors and Recurrent Neural Network

About 40% of the US construction workforce experiences high-level fatigue, which leads to poor judgment, increased risk of injuries, a decrease in productivity, and a lower quality of work. Therefore, it is essential to monitor fatigue to reduce its adverse effects and prevent long-term health problems. However, since fatigue demonstrates itself in several complex processes, there is no single standard measurement method for fatigue detection. This study aims to develop a system for continuous workers' fatigue monitoring by predicting the aerobic fatigue threshold (AFT) using forearm muscle activity and motion data. The proposed system consists of five modules: Data acquisition, activity recognition, oxygen uptake prediction, maximum aerobic capacity (MAC) estimation, and continuous AFT monitoring. The proposed system was evaluated on the participants performing fourteen scaffold-building activities. The results show that the AFT features have achieved a higher accuracy of 92.31% in assessing the workers' fatigue level compared to heart rate (51.28%) and percentage heart rate reserve (50.43%) features. Moreover, the overall performance of the proposed system on unseen data using average two-min AFT features was 76.74%. The study validates the feasibility of using forearm muscle activity and motion data to workers' fatigue levels continuously.

Baseline robot-measured kinematic metrics predict discharge rehabilitation outcomes in individuals with subacute stroke

Background: The literature on upper limb robot-assisted therapy showed that robot-measured metrics can simultaneously predict registered clinical outcomes. However, only a limited number of studies correlated pre-treatment kinematics with discharge motor recovery. Given the importance of predicting rehabilitation outcomes for optimizing physical therapy, a predictive model for motor recovery that incorporates multidirectional indicators of a patient's upper limb abilities is needed. Objective: The aim of this study was to develop a predictive model for rehabilitation outcome at discharge (i.e., muscle strength assessed by the Motricity Index of the affected upper limb) based on multidirectional 2D robot-measured kinematics. Methods: Re-analysis of data from 66 subjects with subacute stroke who underwent upper limb robot-assisted therapy with an end-effector robot was performed. Two least squares error multiple linear regression models for outcome prediction were developed and differ in terms of validation procedure: the Split Sample Validation (SSV) model and the Leave-One-Out Cross-Validation (LOOCV) model. In both models, the outputs were the discharge Motricity Index of the affected upper limb and its sub-items assessing elbow flexion and shoulder abduction, while the inputs were the admission robot-measured metrics. Results: The extracted robot-measured features explained the 54% and 71% of the variance in clinical scores at discharge in the SSV and LOOCV validation procedures respectively. Normalized errors ranged from 22% to 35% in the SSV models and from 20% to 24% in the LOOCV models. In all models, the movement path error of the trajectories characterized by elbow flexion and shoulder extension was the significant predictor, and all correlations were significant. Conclusion: This study highlights that motor patterns assessed with multidirectional 2D robot-measured metrics are able to predict clinical evalutation of upper limb muscle strength and may be useful for clinicians to assess, manage, and program a more specific and appropriate rehabilitation in subacute stroke patients.

Research and application advances in rehabilitation assessment of stroke

Stroke has a high incidence and disability rate, and rehabilitation is an effective means to reduce the disability rate of patients. To systematize rehabilitation assessment, which is the foundation for rehabilitation therapy, we summarize the assessment methods commonly used in research and clinical applications, including the various types of stroke rehabilitation scales and their applicability, and related biomedical detection technologies, including surface electromyography (sEMG), motion analysis systems, transcranial magnetic stimulation (TMS), magnetic resonance imaging (MRI), and combinations of different techniques. We also introduce some assessment techniques that are still in the experimental phase, such as the prospective application of artificial intelligence (AI) with optical correlation tomography (OCT) in stroke rehabilitation. This review provides a useful bibliography for the assessment of not only the severity of stroke injury, but also the therapeutic effects of stroke rehabilitation, and establishes a solid base for the future development of stroke rehabilitation skills.

Assessment of Neurological Impairment and Recovery Using Statistical Models of Neurologically Healthy Behavior

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.

Effectiveness of Platform-Based Robot-Assisted Rehabilitation for Musculoskeletal or Neurologic Injuries: A Systematic Review

During the last ten years the use of robotic-assisted rehabilitation has increased significantly. Compared with traditional care, robotic rehabilitation has several potential advantages. Platform-based robotic rehabilitation can help patients recover from musculoskeletal and neurological conditions. Evidence on how platform-based robotic technologies can positively impact on disability recovery is still lacking, and it is unclear which intervention is most effective in individual cases. This systematic review aims to evaluate the effectiveness of platform-based robotic rehabilitation for individuals with musculoskeletal or neurological injuries. Thirty-eight studies met the inclusion criteria and evaluated the efficacy of platform-based rehabilitation robots. Our findings showed that rehabilitation with platform-based robots produced some encouraging results. Among the platform-based robots studied, the VR-based Rutgers Ankle and the Hunova were found to be the most effective robots for the rehabilitation of patients with neurological conditions (stroke, spinal cord injury, Parkinson's disease) and various musculoskeletal ankle injuries. Our results were drawn mainly from studies with low-level evidence, and we think that our conclusions should be taken with caution to some extent and that further studies are needed to better evaluate the effectiveness of platform-based robotic rehabilitation devices.

Data-Driven Classification of Human Movements in Virtual Reality-Based Serious Games: Preclinical Rehabilitation Study in Citizen Science

BACKGROUND

Sustained engagement is essential for the success of telerehabilitation programs. However, patients' lack of motivation and adherence could undermine these goals. To overcome this challenge, physical exercises have often been gamified. Building on the advantages of serious games, we propose a citizen science-based approach in which patients perform scientific tasks by using interactive interfaces and help advance scientific causes of their choice. This approach capitalizes on human intellect and benevolence while promoting learning. To further enhance engagement, we propose performing citizen science activities in immersive media, such as virtual reality (VR).

OBJECTIVE

This study aims to present a novel methodology to facilitate the remote identification and classification of human movements for the automatic assessment of motor performance in telerehabilitation. The data-driven approach is presented in the context of a citizen science software dedicated to bimanual training in VR. Specifically, users interact with the interface and make contributions to an environmental citizen science project while moving both arms in concert.

METHODS

In all, 9 healthy individuals interacted with the citizen science software by using a commercial VR gaming device. The software included a calibration phase to evaluate the users' range of motion along the 3 anatomical planes of motion and to adapt the sensitivity of the software's response to their movements. During calibration, the time series of the users' movements were recorded by the sensors embedded in the device. We performed principal component analysis to identify salient features of movements and then applied a bagged trees ensemble classifier to classify the movements.

RESULTS

The classification achieved high performance, reaching 99.9% accuracy. Among the movements, elbow flexion was the most accurately classified movement (99.2%), and horizontal shoulder abduction to the right side of the body was the most misclassified movement (98.8%).

CONCLUSIONS

Coordinated bimanual movements in VR can be classified with high accuracy. Our findings lay the foundation for the development of motion analysis algorithms in VR-mediated telerehabilitation.

Motor Ability Evaluation of the Upper Extremity with Point-To-Point Training Movement Based on End-Effector Robot-Assisted Training System

Assessment is critical during the procedure of stroke rehabilitation. However, traditional assessment methods are time-consuming, laborious, and dependent on the skillfulness of the therapist. Moreover, they cannot distinguish whether the improvement comes from the abnormal compensation or the improvement of upper extremity motor function. To make up for the shortcomings of the traditional methods, this study proposes a novel assessment system, which consisted of a rehabilitation robot and motion capture (MoCAP) system. A 9-degree-of-freedom (DOF) kinematic model is established, which consists of the shoulder girdle, shoulder, elbow, and wrist joints. And seven assessment indices are selected for this assessment system, including a range of motion (ROM), shoulder girdle compensation (SGC), trunk compensation (TC), aiming angle (AA), motion error (ME), motion length ratio (MLR), and useful force (UF). For AA, ME, and MLR, all describe the motor ability of the upper extremity, and a linear model was proposed to map these three indices into one index, called motor control ability (MCA). Then, this system can quantitatively evaluate human upper extremity motor function from joint space kinematics, Cartesian space kinematics, and dynamics. Three healthy participants were invited to verify the effectiveness of this system. The preliminary results show that all participants' handedness performs a little better than the nonhandedness. And the performance of the participants and the change of all the upper limb joints can be directly watched from the trajectory of the hand and joint angles' curve. Therefore, this assessment system can evaluate the human upper limb motor function well. Future studies are planned to recruit elderly volunteers or stroke patients to further verify the effectiveness of this system.

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 .

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s42234-021-00081-9.

Kinematic Evaluation via Inertial Measurement Unit Associated with Upper Extremity Motor Function in Subacute Stroke: A Cross-Sectional Study

Kinematic evaluation via portable sensor system has been increasingly applied in neurological sciences and clinical practice. However, conventional kinematic evaluation rarely extends the context beyond the motor impairment level. In addition, kinematic tasks with numerous items could be complex and time consuming that pose a burden to test applications and data processing. The study aimed to explore the correlation of finger-to-nose task (FNT) kinematics via Inertial Measurement Unit with upper limb motor function in subacute stroke. In this study, six FNT kinematic variables were used to measure movement time, smoothness, and velocity in 37 participants with subacute stroke. Upper limb motor function was evaluated with the Fugl-Meyer Assessment for Upper Extremity (FMA-UE), Action Research Arm Test (ARAT), and modified Barthel Index (MBI). As a result, mean velocity, peak velocity, and the number of movement units were associated with the clinical assessments. The multivariable linear regression models could estimate 55%, 51%, and 32% of variance in FMA-UE, ARAT, and MBI, respectively. In addition, age, gender, type of stroke, and paretic side had no significant effects on these associations. Results show that FNT kinematic variables measured via Inertial Measurement Unit are associated with upper extremity motor function in individuals with subacute stroke. The objective kinematic evaluation may be suitable for predicting clinical measures of motor impairment and capacity to understand upper extremity motor recovery and clinical decision making after stroke. This trial is registered with ChiCTR1900026656.

Quantitative Assessment of Motor Function by an End-Effector Upper Limb Rehabilitation Robot Based on Admittance Control

Various rehabilitation robots have been developed to assist the movement training of the upper limbs of stroke patients, among which some have been used to evaluate the motor recovery. However, how to understand the recovery of motor function from the quantitative assessment following robot-assisted rehabilitation training is still not clear. The objective of this study is to propose a quantitative assessment method of motor function based on the force and trajectory characteristics during robotic training to reflect motor functional recovery. To assist stroke patients who are not able to move voluntarily, an assistive training mode was developed for the robot-assisted rehabilitation system based on admittance control. Then, to validate the relationship between characteristic information and functional recovery, a clinical experiment was conducted, in which nine stroke patients and nine healthy subjects were recruited. The results showed a significant difference in movement range and movement smoothness during trajectory tracking tasks between stroke patients and healthy subjects. The two parameters above have a correlation with the Fugl-Meyer Assessment for Upper Extremity (FMU) of the involved patients. The multiple linear regression analysis showed FMU was positively correlated with parameters (R2=0.91,p<0.005). This finding indicated that the above-mentioned method can achieve quantitative assessment of motor function for stroke patients during robot-assisted rehabilitation training, which can contribute to promoting rehabilitation robots in clinical practice.

Robot-Assisted Therapy and Constraint-Induced Movement Therapy for Motor Recovery in Stroke: Results From a Randomized Clinical Trial

Background: Stroke is one of the leading causes of adult disability, and up to 80% of stroke survivors undergo upper extremity motor dysfunction. Constraint-Induced Movement Therapy (CIMT) and Robot-Assisted Therapy (RT) are used for upper limb stroke rehabilitation. Although CIMT and RT are different techniques, both are beneficial; however, their results must be compared. The objective is to establish the difference between RT and CIMT after a rehabilitation program for chronic stroke patients.

Method: This is a randomized clinical trial, registered at ClinicalTrials.gov (ID number NCT02700061), in which patients with stroke received sessions of RT or CIMT protocol, combined with a conventional rehabilitation program for 12 weeks. The primary outcome was measured by Wolf Motor Function Test (WMFT) and Fugl-Meyer Assessment—Upper Limb (FMA-UL). Activities of daily living were also assessed.

Results: Fifty one patients with mild to moderate upper limb impairment were enrolled in this trial, 25 women and 26 men, mean age of 60,02 years old (SD 14,48), with 6 to 36 months after stroke onset. Function significantly improved regardless of the treatment group. However, no statistical difference was found between both groups as p-values of the median change of function measured by WMFT and FMA were 0.293 and 0.187, respectively.

Conclusion: This study showed that Robotic Therapy (RT) was not different from Constraint-Induced Movement Therapy (CIMT) regardless of the analyzed variables. There was an overall upper limb function, motor recovery, functionality, and activities of daily living improvement regardless of the interventions. At last, the combination of both techniques should be considered in future studies.

Predicting Clinically Significant Improvement After Robot-Assisted Upper Limb Rehabilitation in Subacute and Chronic Stroke

Prior studies examining predictors of favorable clinical outcomes after upper limb robot-assisted therapy (RT) have many shortcomings. Therefore, the aim of this study was to identify meaningful predictors and a prediction model for clinically significant motor improvement in upper limb impairment after RT for each stroke phase. This retrospective, single-center study enrolled patients with stroke who received RT using InMotion2 along with conventional therapy (CT) from January 2015 to September 2019. Demographic characteristics, clinical measures, and robotic kinematic measures were evaluated. The primary outcome measure was the Fugl-Meyer Assessment-Upper Extremity (FMA-UE) and we classified patients with improvement more than the minimal clinically important difference as responders for each stroke phase. Univariable and multivariable logistic regression analyses were performed to assess the relationship between potential predictors and RT responders and determine meaningful predictors. Subsequently, meaningful predictors were included in the final prediction model. One hundred forty-four patients were enrolled. The Hand Movement Scale and time since onset were significant predictors of clinically significant improvement in upper limb impairment (P = 0.045 and 0.043, respectively), as represented by the FMA-UE score after RT along with CT, in patients with subacute stroke. These variables were also meaningful predictors with borderline statistical significance in patients with chronic stroke (P = 0.076 and 0.066, respectively). Better hand movement and a shorter time since onset can be used as realistic predictors of clinically significant motor improvement in upper limb impairment after RT with InMotion2 alongside CT in patients with subacute and chronic stroke. This information may help healthcare professionals discern optimal patients for RT and accurately inform patients and caregivers about outcomes of RT.

Myoelectric analysis of upper-extremity muscles during robot-assisted bilateral wrist flexion-extension in subjects with poststroke hemiplegia

BACKGROUND

Muscle co-contraction during the execution of motor tasks or training is common in poststroke subjects. EMG-derived muscular activation indexes have been used to evaluate muscle co-contractions during movements. In addition, robot-assisted bilateral arm training provides a repetitive and stable training method to improve arm movements. However, quantitative measures of muscle contractions during this training in poststroke subjects have not been described.

METHODS

Seventeen subjects experiencing spastic hemiplegia after a stroke were recruited to perform robot-assisted bilateral wrist flexion and extension movements. The co-contraction index and two new indexes, temporal correlation and cross mutual information, which are derived from the EMGs of working muscles without the need for envelope normalization, are used to quantify intermuscular activation during wrist movements.

FINDINGS

Higher temporal correlation as well as higher co-contraction index was demonstrated in the affected muscles, implying the recruitment of muscle co-contractions to complete the movement task. On the other hand, a higher value of cross mutual information was exhibited in the unaffected muscles which was attributed to their distinct, rhythmic muscle contractions. The plot of temporal correlation versus cross mutual information further defined affected, unaffected synergistic, and unaffected agonist-antagonist muscular regions. Moreover, with the modified Ashworth scale, multiple regression models based on the co-contraction index and cross mutual information had the highest R-squared value of 0.733.

INTERPRETATION

EMG-derived intermuscular activation parameters demonstrated muscle co-contractions in the affected muscles and different types of intermuscular contractions during robot-assisted bilateral arm training. The modified Ashworth scale estimation based on multiple regression analysis of the activation indexes also demonstrated EMG-derived index a valuable method for assessing muscle spasticity in subjects with poststroke hemiplegia.

Robotic Assisted Upper Limb Training Post Stroke: A Randomized Control Trial Using Combinatory Approach Toward Reducing Workforce Demands

Post stroke upper limb rehabilitation is a challenging problem with poor outcomes as 40% of survivors have functionally useless upper limbs. Robot-aided therapy (RAT) is a potential method to alleviate the effort of intensive, task-specific, repetitive upper limb exercises for both patients and therapists. The present study aims to investigate how a time matched combinatory training scheme that incorporates conventional and RAT, using H-Man, compares with conventional training toward reducing workforce demands. In a randomized control trial (NCT02188628, www.clinicaltrials.gov), 44 subacute to chronic stroke survivors with first-ever clinical stroke and predominant arm motor function deficits were recruited and randomized into two groups of 22 subjects: Robotic Therapy (RT) and Conventional Therapy (CT). Both groups received 18 sessions of 90 min; three sessions per week over 6 weeks. In each session, participants of the CT group received 90 min of 1:1 therapist-supervised conventional therapy while participants of the RT group underwent combinatory training which consisted of 60 min of minimally-supervised H-Man therapy followed by 30 min of conventional therapy. The clinical outcomes [Fugl-Meyer (FMA), Action Research Arm Test and, Grip Strength] and the quantitative measures (smoothness, time efficiency, and task error, derived from two robotic assessment tasks) were independently evaluated prior to therapy intervention (week 0), at mid-training (week 3), at the end of training (week 6), and post therapy (week 12 and 24). Significant differences within group were observed at the end of training for all clinical scales compared with baseline [mean and standard deviation of FMA score changes between baseline and week 6; RT: Δ4.41 (3.46) and CT: Δ3.0 (4.0); p < 0.01]. FMA gains were retained 18 weeks post-training [week 24; RT: Δ5.38 (4.67) and week 24 CT: Δ4.50 (5.35); p < 0.01]. The RT group clinical scores improved similarly when compared to CT group with no significant inter-group at all time points although the conventional therapy time was reduced to one third in RT group. There were no training-related adverse side effects. In conclusion, time matched combinatory training incorporating H-Man RAT produced similar outcomes compared to conventional therapy alone. Hence, this study supports a combinatory approach to improve motor function in post-stroke arm paresis. Clinical Trial Registration: www.ClinicalTrials.gov, identifier: NCT02188628.

Association Between Finger-to-Nose Kinematics and Upper Extremity Motor Function in Subacute Stroke: A Principal Component Analysis

Background

Kinematic analysis facilitates interpreting the extent and mechanisms of motor restoration after stroke. This study was aimed to explore the kinematic components of finger-to-nose test obtained from principal component analysis (PCA) and the associations with upper extremity (UE) motor function in subacute stroke survivors.

Methods

Thirty-seven individuals with subacute stroke and twenty healthy adults participated in the study. Six kinematic metrics during finger-to-nose task (FNT) were utilized to perform PCA. Clinical assessments for stroke participants included the Fugl-Meyer Assessment for Upper Extremity (FMA-UE), Action Research Arm Test (ARAT), and Modified Barthel Index (MBI).

Results

Three principal components (PC) accounting for 91.3% variance were included in multivariable regression models. PC1 (48.8%) was dominated by mean velocity, peak velocity, number of movement units (NMU) and normalized integrated jerk (NIJ). PC2 (31.1%) described percentage of time to peak velocity and movement time. PC3 (11.4%) profiled percentage of time to peak velocity. The variance explained by principal component regression in FMA-UE (R² = 0.71) were higher than ARAT (R² = 0.59) and MBI (R² = 0.29) for stroke individuals.

Conclusion

Kinematic components during finger-to-nose test identified by PCA are associated with UE motor function in subacute stroke. PCA reveals the intrinsic association among kinematic metrics, which may add value to UE assessment and future intervention targeted for kinematic components for stroke individuals.

Clinical Trial Registration

Chinese Clinical Trial Registry (http://www.chictr.org.cn/) on 17 October 2019, identifier: ChiCTR1900026656.

Robot-Based Assessment of HIV-Related Motor and Cognitive Impairment for Neurorehabilitation

There is a pressing need for strategies to slow or treat the progression of functional decline in people living with HIV. This paper explores a novel rehabilitation robotics approach to measuring cognitive and motor impairment in adults living with HIV, including a subset with stroke. We conducted a cross-sectional study with 21 subjects exhibiting varying levels of cognitive and motor impairment. We tested three robot-based tasks – trajectory tracking, N-back, and spatial span – to assess if metrics derived from these tasks were sensitive to differences in subjects with varying levels of executive function and upper limb motor impairments. We also examined how well these metrics could estimate clinical cognitive and motor scores. The results showed that the average sequence length on the robot-based spatial span task was the most sensitive to differences between various cognitive and motor impairment levels. We observed strong correlations between robot-based measures and clinical cognitive and motor assessments relevant to the HIV population, such as the Color Trails 1 (rho = 0.83), Color Trails 2 (rho = 0.71), Digit Symbol – Coding (rho = 0.81), Montreal Cognitive Assessment – Executive Function subscore (rho = 0.70), and Box and Block Test (rho = 0.74). Importantly, our results highlight that gross motor impairment may be overlooked in the assessment of HIV-related disability. This study shows that rehabilitation robotics can be expanded to new populations beyond stroke, namely to people living with HIV and those with cognitive impairments.

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.

Novel Human-Centered Robotics: Towards an Automated Process for Neurorehabilitation

The global requirement of patient rehabilitation has surged with time due to the growing number of accidents, injuries, age-related issues, and other aspects. Parallelly, the cost of treatment and patient care also increased in a manifold. Moreover, constant monitoring and support for the patients having physical disabilities have become an ongoing challenge to the medical system. Robotics-based neurorehabilitation has reduced the human error while assisting such patients, precisely interpreting the signals, and communicating to the patient. Gradual precise application and improvement of the technology with time yielded a novel direction for patient care and support. The interdisciplinary contribution of many advanced technical branches allowed us to develop robotics-based assistance with high precision for the upper limb and the lower limb impairments. The present review summarizes the generation and background of robotic implementation for patient support, progress, present status, and future requirements.

Improving motor function after chronic stroke by interactive gaming with a redesigned MR-compatible hand training device

New rehabilitation strategies enabled by technological developments are challenging the prevailing concept of there being a limited window for functional recovery after stroke. In this study, we examined the utility of a robot-assisted therapy used in combination with a serious game as a rehabilitation and motor assessment tool in patients with chronic stroke. We evaluated 928 game rounds from 386 training sessions of 8 patients who had suffered an ischemic stroke affecting middle cerebral artery territory that incurred at least 6 months prior. Motor function was assessed with clinical motor scales, including the Fugl-Meyer upper extremity (FM UE) scale, Action Research Arm Test, Modified Ashworth scale and the Box and Blocks test. Robotic device output measures (mean force, force-position correlation) and serious game score elements (collisions, rewards and total score) were calculated. A total of 2 patients exhibited a marginal improvement after a 10-week training protocol according to the FM UE scale and an additional patient exhibited a significant improvement according to Box and Blocks test. Motor scales showed strong associations of robotic device parameters and game metrics with clinical motor scale scores, with the strongest correlations observed for the mean force (0.677<Ρ<0.869), followed by the number of collisions (-0.670<Ρ<-0.585). Linear regression analysis showed that these indices were independent predictors of motor scale scores. In conclusion, a robotic device linked to a serious game can be used by patients with chronic stroke and induce at least some clinical improvements in motor performance. Robotic device output parameters and game score elements associate strongly with clinical motor scales and have the potential to be used as predictors in models of rehabilitation progress.

Upper limb robotic assessment: Pilot study comparing velocity dependent resistance in individuals with acquired brain injury to healthy controls

Introduction

Assessment of velocity dependent resistance (VDR) can provide insights into spasticity in individuals with upper motor neuron syndrome. This study investigates the relationship between Modified Ashworth scores and a biomechanical based representation of VDR using a rehabilitation robot. Comparisons in VDR are made for the upper limb (UL) between individuals with acquired brain injury and healthy controls for the para-sagittal plane.

Methods

The system manipulates the individual’s limb through five flexion and extension motions at increasing speeds to obtain force profiles at different velocities. An approximation of VDR is calculated and analyzed statistically against clinical scales and tested for interactions.

Results

All individuals (aged 18–65), including healthy controls exhibited VDR greater than 0 (P < 0.05). MAS scores were found to be related to VDR (P < 0.05) with an interaction found between MAS Bicep and Tricep scores (P < 0.01). Considering this interaction, evidence of differences in VDR were found between several neighboring assessment score combinations.

Conclusion

The robot can detect and quantify VDR that captures information relevant to UL spasticity. Results suggests a better categorization of VDR is possible and supports further development of rehabilitation robotics for assisting spasticity assessment.

Evaluating Rehabilitation Progress Using Motion Features Identified by Machine Learning

Evaluating progress throughout a patient's rehabilitation episode is critical for determining the effectiveness of the selected treatments and is an essential ingredient in personalised and evidence-based rehabilitation practice. The evaluation process is complex due to the inherently large human variations in motor recovery and the limitations of commonly used clinical measurement tools. Information recorded during a robot-assisted rehabilitation process can provide an effective means to continuously quantitatively assess movement performance and rehabilitation progress. However, selecting appropriate motion features for rehabilitation evaluation has always been challenging. This paper exploits unsupervised feature learning techniques to reduce the complexity of building the evaluation model of patients' progress. A new feature learning technique is developed to select the most significant features from a large amount of kinematic features measured from robotics, providing clinically useful information to health practitioners with reduction of modeling complexity. A novel indicator that uses monotonicity and trendability is proposed to evaluate kinematic features. The data used to develop the feature selection technique consist of kinematic data from robot-aided rehabilitation for a population of stroke patients. The selected kinematic features allow for human variations across a population of patients as well as over the sequence of rehabilitation sessions. The study is based on data records pertaining to 41 stroke patients using three different robot assisted exercises for upper limb rehabilitation. Consistent with the literature, the results indicate that features based on movement smoothness are the best measures among 17 kinematic features suitable to evaluate rehabilitation progress.

Robot-based assessment of HIV-related motor and cognitive impairment for neurorehabilitation

There is a pressing need for strategies to slow or treat the progression of functional decline in people living with HIV. This paper explores a novel rehabilitation robotics approach to measuring cognitive and motor impairment in adults living with HIV, including a subset with stroke. We conducted a cross-sectional study with 21 subjects exhibiting varying levels of cognitive and motor impairment. We developed three robot-based tasks – trajectory tracking, N-back, and spatial span – to assess if metrics derived from these tasks were sensitive to differences in subjects with varying levels of executive function and upper limb motor impairments. We also examined if these metrics could estimate clinical cognitive and motor scores. The results showed that the average sequence length on the robot-based spatial span task was the most sensitive to differences between subjects’ cognitive and motor impairment levels. We observed strong correlations between robot-based measures and clinical cognitive and motor assessments relevant to the HIV population, such as the Color Trails 1 (rho = 0.83), Color Trails 2 (rho = 0.71), Digit Symbol – Coding (rho = 0.81), Montreal Cognitive Assessment – Executive Function subscore (rho = 0.70), and Box and Block Test (rho = 0.74). Importantly, our results highlight that gross motor impairment may be overlooked in the assessment of HIV-related disability. This study shows that rehabilitation robotics can be expanded to new populations beyond stroke, namely to people living with HIV and those with cognitive impairments.

Robotic Rehabilitation and Multimodal Instrumented Assessment of Post-stroke Elbow Motor Functions-A Randomized Controlled Trial Protocol

Background: The reliable assessment, attribution, and alleviation of upper-limb joint stiffness are essential clinical objectives in the early rehabilitation from stroke and other neurological disorders, to prevent the progression of neuromuscular pathology and enable proactive physiotherapy toward functional recovery. However, the current clinical evaluation and treatment of this stiffness (and underlying muscle spasticity) are severely limited by their dependence on subjective evaluation and manual limb mobilization, thus rendering the evaluation imprecise and the treatment insufficiently tailored to the specific pathologies and residual capabilities of individual patients.

Methods: To address these needs, the proposed clinical trial will employ the NEUROExos Elbow Module (NEEM), an active robotic exoskeleton, for the passive mobilization and active training of elbow flexion and extension in 60 sub-acute and chronic stroke patients with motor impairments (hemiparesis and/or spasticity) of the right arm. The study protocol is a randomized controlled trial consisting of a 4-week functional rehabilitation program, with both clinical and robotically instrumented assessments to be conducted at baseline and post-treatment. The primary outcome measures will be a set of standard clinical scales for upper limb spasticity and motor function assessment, including the Modified Ashworth Scale and Fugl-Meyer Index, to confirm the safety and evaluate the efficacy of robotic rehabilitation in reducing elbow stiffness and improving function. Secondary outcomes will include biomechanical, muscular activity, and motor performance parameters extracted from instrumented assessments using the NEEM along with synchronous EMG recordings.

Conclusions: This randomized controlled trial aims to validate an innovative instrumented methodology for clinical spasticity assessment and functional rehabilitation, relying on the precision and accuracy of an elbow exoskeleton combined with EMG recordings and the expertise of a physiotherapist, thus complementing and maximizing the benefits of both practices.

Clinical Trial Registration:www.ClinicalTrials.gov, identifier NCT04484571.

The feasibility of assessing cognitive and motor function in multiple sclerosis patients using robotics

Background

Multiple sclerosis (MS) causes pervasive motor, sensory and cognitive dysfunction. The Expanded Disability Status Scale (EDSS) is the gold standard for assessing MS disability. The EDSS is biased towards mobility and may not accurately measure MS-related disabilities in the upper limb or in cognitive functions (e.g. executive function).

Objective

Our objectives were to determine the feasibility of using the Kinarm robotic system to quantify neurological deficits related to arm function and cognition in MS patients, and examine relationships between traditional clinical assessments and Kinarm variables.

Methods

Individuals with MS performed 8 robotic tasks assessing motor, cognitive, and sensory ability. We additionally collected traditional clinical assessments and compared these to the results of the robotic assessment.

Results

Forty-three people with MS were assessed. Most participants could complete the robotic assessment. Twenty-six (60%) were impaired on at least one cognitive task and twenty-six (60%) were impaired on at least one upper-limb motor task. Cognitive domain task performance correlated most strongly with the EDSS.

Conclusions

Kinarm robotic assessment of people with MS is feasible, can identify a broad range of upper-limb motor and sensory, as well as cognitive, impairments, and complements current clinical rating scales in the assessment of MS-related disability.

Kinematic parameters obtained with the ArmeoSpring for upper-limb assessment after stroke: a reliability and learning effect study for guiding parameter use

BACKGROUND

After stroke, kinematic measures obtained with non-robotic and robotic devices are highly recommended to precisely quantify the sensorimotor impairments of the upper-extremity and select the most relevant therapeutic strategies. Although the ArmeoSpring exoskeleton has demonstrated its effectiveness in stroke motor rehabilitation, its interest as an assessment tool has not been sufficiently documented. The aim of this study was to investigate the psychometric properties of selected kinematic parameters obtained with the ArmeoSpring in post-stroke patients.

METHODS

This study involved 30 post-stroke patients (mean age = 54.5 ± 16.4 years; time post-stroke = 14.7 ± 26.7 weeks; Upper-Extremity Fugl-Meyer Score (UE-FMS) = 40.7 ± 14.5/66) who participated in 3 assessment sessions, each consisting of 10 repetitions of the 'horizontal catch' exercise. Five kinematic parameters (task and movement time, hand path ratio, peak velocity, number of peak velocity) and a global Score were computed from raw ArmeoSpring' data. Learning effect and retention were analyzed using a 2-way repeated-measures ANOVA, and reliability was investigated using the intra-class correlation coefficient (ICC) and minimal detectable change (MDC).

RESULTS

We observed significant inter- and intra-session learning effects for most parameters except peak velocity. The measures performed in sessions 2 and 3 were significantly different from those of session 1. No additional significant difference was observed after the first 6 trials of each session and successful retention was also highlighted for all the parameters. Relative reliability was moderate to excellent for all the parameters, and MDC values expressed in percentage ranged from 42.6 to 102.8%.

CONCLUSIONS

After a familiarization session, the ArmeoSpring can be used to reliably and sensitively assess motor impairment and intervention effects on motor learning processes after a stroke. Trial registration The study was approved by the local hospital ethics committee in September 2016 and was registered under number 05-0916.

Technology-aided assessment of functionally relevant sensorimotor impairments in arm and hand of post-stroke individuals

BACKGROUND

Assessing arm and hand sensorimotor impairments that are functionally relevant is essential to optimize the impact of neurorehabilitation interventions. Technology-aided assessments should provide a sensitive and objective characterization of upper limb impairments, but often provide arm weight support and neglect the importance of the hand, thereby questioning their functional relevance. The Virtual Peg Insertion Test (VPIT) addresses these limitations by quantifying arm and hand movements as well as grip forces during a goal-directed manipulation task requiring active lifting of the upper limb against gravity. The aim of this work was to evaluate the ability of the VPIT metrics to characterize arm and hand sensorimotor impairments that are relevant for performing functional tasks.

METHODS

Arm and hand sensorimotor impairments were systematically characterized in 30 chronic stroke patients using conventional clinical scales and the VPIT. For the latter, ten previously established kinematic and kinetic core metrics were extracted. The validity and robustness of these metrics was investigated by analyzing their clinimetric properties (test-retest reliability, measurement error, learning effects, concurrent validity).

RESULTS

Twenty-three of the participants, the ones with mild to moderate sensorimotor impairments and without strong cognitive deficits, were able to successfully complete the VPIT protocol (duration 16.6 min). The VPIT metrics detected impairments in arm and hand in 90.0% of the participants, and were sensitive to increased muscle tone and pathological joint coupling. Most importantly, significant moderate to high correlations between conventional scales of activity limitations and the VPIT metrics were found, thereby indicating their functional relevance when grasping and transporting objects, and when performing dexterous finger manipulations. Lastly, the robustness of three out of the ten VPIT core metrics in post-stroke individuals was confirmed.

CONCLUSIONS

This work provides evidence that technology-aided assessments requiring goal-directed manipulations without arm weight support can provide an objective, robust, and clinically feasible way to assess functionally relevant sensorimotor impairments in arm and hand in chronic post-stroke individuals with mild to moderate deficits. This allows for a better identification of impairments with high functional relevance and can contribute to optimizing the functional benefits of neurorehabilitation interventions.

Deficits underlying handgrip performance in mildly affected chronic stroke persons

BACKGROUND

Knowledge of the deficits underlying handgrip performance is fundamental for the development of targeted interventions.

OBJECTIVES

The purpose of this study was to evaluate maximal handgrip strength, fatigue resistance, grip work, and muscle fatigue in mildly affected stroke persons.

METHODS

We conducted a prospective observational study. A total of 20 individuals after a first unilateral ischemic/hemorrhagic chronic stroke (months poststroke: mean 33.64 ± 19.60), mildly affected according to functional score (FIM: 112.71 ± 16.14) and with arm motor impairment (upper-extremity Fugl-Meyer score: mean 57.07 ± 7.82 on the contralesional side); and 20 sex and age-matched controls were included. The outcomes assessed were maximal handgrip strength evaluated through maximal voluntary contraction, fatigue resistance defined as the seconds during which grip strength dropped to 50% of its maximum and gripwork, which was calculated using the equation grip work = maximal grip strength * 0.75 * fatigue resistance. Muscle fatigue was assessed using surface electromyography during a sustained contraction over 50% of maximal voluntary contraction.

RESULTS

Persons with stroke demonstrated significantly reduced handgrip performance regarding maximal handgrip strength, resistance to fatigue, grip work, and muscle fatigue for the contralesional hand. In addition, a reduced grip resistance and muscle fatigue was shown for the ipsilesional hand compared with controls. We found no effect of the hemispheric side of the lesion on the grip performance measures assessed.

CONCLUSIONS

Our findings provide evidence that handgrip performance remain impaired after 6 months after stroke, and may serve as a target for interventions to improve these abilities after stroke.

Changes in arm kinematics of chronic stroke individuals following "Assist-As-Asked" robot-assisted training in virtual and physical environments: A proof-of-concept study

Introduction

In this proof-of-concept study, we introduce a custom-developed robot-assisted training protocol, named “Assist-As-Asked”, aiming at improving arm function of chronic stroke subjects with moderate-to-severe upper extremity motor impairment. The study goals were to investigate the feasibility and potential adverse effects of this training protocol in both physical and virtual environments.

Methods

A sample of convenience of four chronic stroke subjects participated in 10 half-hour sessions. The task was to practice reaching six targets in both virtual and physical environments. The robotic arm used the Assist-As-Asked paradigm in which it helped subjects to complete movements when asked by them. Changes in the kinematics of the reaching movements and the participants’ perception of the reaching practice in both environments were the outcome measures of interest.

Results

Subjects improved their reaching performance and none of them reported any adverse events. There were no differences between the two environments in terms of kinematic measures even though subjects had different opinions about the environment preference.

Conclusions

Using the Assist-As-Asked protocol in moderate-to-severe chronic stroke survivors is feasible and it can be used with both physical and virtual environments with no evidence of one of them to be superior to the other based on users’ perspectives and movement kinematics.

An interactive motion-tracking system for home-based assessing and training reach-to-target tasks in stroke survivors-a preliminary study

Quantitative evaluation and training of the reach-to-target ability in stroke patients are needed for postdischarge rehabilitation, which can be achieved using a motion-tracking system. However, most of these systems are either costly, involve sophisticated parameter interpretation, or are not designed for rehabilitation. We developed an interactive reach-to-target assessment and training system (IRTATS) based on a camera and three marker straps to detect tracking signals. IRTATS supports audiovisual feedback, personal goal setting, and use in a small clinic or home without the internet. This study aims to evaluate the reliability, validity of IRTATS, and its measurement accuracy of the range of motion (ROM). Ninety-nine stroke patients and 20 healthy adults were recruited for the study. Kinematic variables and active joint ROM (AROM) were assessed using IRTATS. The AROM was measured by a universal goniometer, and scores from multiple clinical scales concerning motor and activity capability were calculated. Although the AROMs measured by IRTATS and the goniometer did not agree, IRTATS has clinically acceptable reliability and validity. Three variables in IRTATS could discriminate the motor performance of patients and healthy subjects. IRTATS may provide a new supplement to conventional physiotherapy in the assessment of the reach-to-target ability in stroke patients. Graphical abstract System configuration • The system is based on an infrared camera and the adjustable marker straps as a sensor module. • It is portable and compact, and has clinically acceptable reliability and validity. • It supports audiovisual feedback, personal goal setting, and use in regions without the internet. • It can be used as an adjunct to conventional physiotherapy in the assessment of the reach-to-target ability.

Motor improvement estimation and task adaptation for personalized robot-aided therapy: a feasibility study

BACKGROUND

In the past years, robotic systems have become increasingly popular in upper limb rehabilitation. Nevertheless, clinical studies have so far not been able to confirm superior efficacy of robotic therapy over conventional methods. The personalization of robot-aided therapy according to the patients' individual motor deficits has been suggested as a pivotal step to improve the clinical outcome of such approaches.

METHODS

Here, we present a model-based approach to personalize robot-aided rehabilitation therapy within training sessions. The proposed method combines the information from different motor performance measures recorded from the robot to continuously estimate patients' motor improvement for a series of point-to-point reaching movements in different directions. Additionally, it comprises a personalization routine to automatically adapt the rehabilitation training. We engineered our approach using an upper-limb exoskeleton. The implementation was tested with 17 healthy subjects, who underwent a motor-adaptation paradigm, and two subacute stroke patients, exhibiting different degrees of motor impairment, who participated in a pilot test undergoing rehabilitative motor training.

RESULTS

The results of the exploratory study with healthy subjects showed that the participants divided into fast and slow adapters. The model was able to correctly estimate distinct motor improvement progressions between the two groups of participants while proposing individual training protocols. For the two pilot patients, an analysis of the selected motor performance measures showed that both patients were able to retain the improvements gained during training when reaching movements were reintroduced at a later stage. These results suggest that the automated training adaptation was appropriately timed and specifically tailored to the abilities of each individual.

CONCLUSIONS

The results of our exploratory study demonstrated the feasibility of the proposed model-based approach for the personalization of robot-aided rehabilitation therapy. The pilot test with two subacute stroke patients further supported our approach, while providing encouraging results for the applicability in clinical settings. Trial registration This study is registered in ClinicalTrials.gov (NCT02770300, registered 30 March 2016, https://clinicaltrials.gov/ct2/show/NCT02770300).

Resting-state EEG topographies: Reliable and sensitive signatures of unilateral spatial neglect

Theoretical advances in the neurosciences are leading to the development of an increasing number of proposed interventions for the enhancement of functional recovery after brain damage. Integration of these novel approaches in clinical practice depends on the availability of reliable, simple, and sensitive biomarkers of impairment level and extent of recovery, to enable an informed clinical-decision process. However, the neuropsychological tests currently in use do not tap into the complex neural re-organization process that occurs after brain insult and its modulation by treatment. Here we show that topographical analysis of resting-state electroencephalography (rsEEG) patterns using singular value decomposition (SVD) could be used to capture these processes. In two groups of subacute stroke patients, we show reliable detection of deviant neurophysiological patterns over repeated measurement sessions on separate days. These patterns generalized across patients groups. Additionally, they maintained a significant association with ipsilesional attention bias, discriminating patients with spatial neglect of different severity levels. The sensitivity and reliability of these rsEEG topographical analyses support their use as a tool for monitoring natural and treatment-induced recovery in the rehabilitation process.

Effects of robot therapy on upper body kinematics and arm function in persons post stroke: a pilot randomized controlled trial

BACKGROUND

Robot-based rehabilitation for persons post-stroke may improve arm function and daily-life activities as measured by clinical scales, but its effects on motor strategies during functional tasks are still poorly investigated. This study aimed at assessing the effects of robot-therapy versus arm-specific physiotherapy in persons post-stroke on motor strategies derived from upper body instrumented kinematic analysis, and on arm function measured by clinical scales.

METHODS

Forty persons in the sub-acute and chronic stage post-stroke were recruited. This sample included all those subjects, enrolled in a larger bi-center study, who underwent instrumented kinematic analysis and who were randomized in Center 2 into Robot (R_Group) and Control Group (C_Group). R_Group received robot-assisted training. C_Group received arm-specific treatment delivered by a physiotherapist. Pre- and post-training assessment included clinical scales and instrumented kinematic analysis of arm and trunk during a virtual untrained task simulating the transport of an object onto a shelf. Instrumented outcomes included shoulder/elbow coordination, elbow extension and trunk sagittal compensation. Clinical outcomes included Fugl-Meyer Motor Assessment of Upper Extremity (FM-UE), modified Ashworth Scale (MAS) and Functional Independence Measure (FIM).

RESULTS

R_Group showed larger post-training improvements of shoulder/elbow coordination (Cohen's d = - 0.81, p = 0.019), elbow extension (Cohen's d = - 0.71, p = 0.038), and trunk movement (Cohen's d = - 1.12, p = 0.002). Both groups showed comparable improvements in clinical scales, except proximal muscles MAS that decreased more in R_Group (Cohen's d = - 0.83, p = 0.018). Ancillary analyses on chronic subjects confirmed these results and revealed larger improvements after robot-therapy in the proximal portion of FM-UE (Cohen's d = 1.16, p = 0.019).

CONCLUSIONS

Robot-assisted rehabilitation was as effective as arm-specific physiotherapy in reducing arm impairment (FM-UE) in persons post-stroke, but it was more effective in improving motor control strategies adopted during an untrained task involving vertical movements not practiced during training. Specifically, robot therapy induced larger improvements of shoulder/elbow coordination and greater reduction of abnormal trunk sagittal movements. The beneficial effects of robot therapy seemed more pronounced in chronic subjects. Future studies on a larger sample should be performed to corroborate present findings.

TRIAL REGISTRATION

www.ClinicalTrials.gov NCT03530358. Registered 21 May 2018. Retrospectively registered.

Work with me, not for me: Relationship between robotic assistance and performance in subacute and chronic stroke patients

Introduction

Studies in robotic therapy which applied the performance enhancement approach report improvements in motor performance during training, though these improvements do not always transfer to motor learning.

Objectives

We postulate that there exists an assistance threshold for which performance saturates. Above this threshold, the robot’s input outweighs the patient’s input and likely learning is not fostered. This study investigated the relationship between assistance and performance changes in stroke patients to find the assistance threshold for performance saturation.

Methods

Twelve subacute and chronic stroke patients engaged in five sessions (over two weeks, each 60 min) in which they performed a reaching task with the rehabilitation robot H-Man in presence of varying levels of haptic assistance (50 N/m to 290 N/m, randomized order). In two additional sessions, a therapist manually tuned the assistance to promote maximal motor learning.

Results

Higher levels of assistance resulted in smoother and faster performance that saturated at assistance levels with K ≥ 110 N/m. Also, the therapist selected assistance levels of K = 175 N/m or below.

Conclusion

The findings of the study indicate that low levels of assistance (K ≤ 175 N/m) can sufficiently induce a significant change in performance.