Kinematic measures for upper limb robot-assisted therapy following stroke and correlations with clinical outcome measures: A review

A novel biomechanical index for quality assessment of the upper-extremity movements in post-stroke patients

BACKGROUND

While motor recovery is preferred to compensatory movements for stroke patients with mild to moderate motion impairment, current movement quality assessments rarely reflect the differences between a patient's pre- and post-stroke movement patterns. Such comparison can help therapists to identify the rate of the restoration of premorbid motion patterns and prescribe the most effective treatment.

METHODS

This paper attempted to present a new biomechanical metric for the quality of upper-limb movements which uses the subject's optimal movements as a reference to evaluate his/her UL movement quality. To this end, an inverse optimal control algorithm was applied to find an estimation of the patient's premorbid motion patterns. The new biomechanical index was then calculated as a measure of similarity between the optimal and actual movement trajectories. In the next part, various simulation and clinimetric investigations were performed to evaluate the responses of the new index to variations of the movement quality as well as its test-retest reliability and concurrent validity.

RESULTS

Simulation-based analyses demonstrated that the proposed index, in contrast to the previous popular biomechanical indices, can successfully detect a wide range of abnormalities in motion signals. In addition, it showed good test-retest reliability (ICC = 0.89) and moderate correlation with clinical indices, Fugl-Meyer Assessment (r = 0.66), Action Research Arm Test (r = 0.47), and ABILHAND (r = 0.27).

CONCLUSIONS

Although the proposed index has the same degree of clinimetric properties as the previous metrics, the ability to identify the level of movement restoration and also various types and severities of motor disabilities may lead to better design and management of motor rehabilitation.

Exoskeleton-assisted upper limb rehabilitation after stroke: a randomized controlled trial

OBJECTIVES

The upper-limb exoskeleton training program which is repetetive and task-specific therapy can improve motor functions in patients with stroke. To compare the effect of an upper-limb exoskeleton training program with Bobath concept on upper limb motor functions in individuals with chronic stroke.

METHODS

Participants were randomly assigned to exoskeleton group (EG, n = 12) or to Bobath group (BG, n = 12). Interventions were matched in terms of session duration and total number of sessions and performed 2 times per week for 6-weeks. Primary outcome was Fugl-Meyer-Upper Extremity (FMA-UE). Secondary outcomes were Modified Ashworth Scale (elbow and wrist flexor muscles), Motor Activity Log-30 which is consist of two parts as an amount of use (AOU) and quality of movement (QOM), and The Nottingham Extended Activities of Daily Living (NEADL) index.

RESULTS

After 12-sessions of training, the mean (SD) FMA-UE score increased by 5.7 (2.9) in the EG, and 1.9 (1.5) points in the BG (p < .05). In total, 40% of participants (5/12) demonstrated a clinically meaningful improvement (≥5.25 points) in the FM-UE, while none of the participants reached MCID score in the bobath group. Changes in the AOU, QOM, and NEADL were significantly larger in the EG compared to BG (p < .05). 7/12 (58.33%) of participants for AOU and 5/12 (42%) of participants for QOM in the EG showed that clinically meaningful change. 5/12 of participants (42%) in the EG demonstrated ≥4.9-point increase in NEADL score.

DISCUSSION

High-intensity repetitive arm and hand exercises with an exoskeleton device was safe and feasible. Exoskeleton-assisted training demonstrated significant benefits in improving upper limb functions and quality of life in individuals after stroke.

Metaverse in Mental Health: The Beginning of a Long History

PURPOSE OF REVIEW

We review the first pilot studies applying metaverse-related technologies in psychiatric patients and discuss the rationale for using this complex federation of technologies to treat mental diseases. Concerning previous virtual-reality applications in medical care, metaverse technologies provide the unique opportunity to define, control, and shape virtual scenarios shared by multi-users to exploit the "synchronized brains" potential exacerbated by social interactions.

RECENT FINDINGS

The application of an avatar-based sexual therapy program conducted on a metaverse platform has been demonstrated to be more effective concerning traditional sexual coaching for treating female orgasm disorders. Again, a metaverse-based social skills training program has been tested on children with autism spectrum disorders, demonstrating a significant impact on social interaction abilities. Metaverse-related technologies could enable us to develop new reliable approaches for treating diseases where behavioral symptoms can be addressed using socio-attentive tasks and social-interaction strategies.

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.

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.

A review of combined functional neuroimaging and motion capture for motor rehabilitation

BACKGROUND

Technological advancements in functional neuroimaging and motion capture have led to the development of novel methods that facilitate the diagnosis and rehabilitation of motor deficits. These advancements allow for the synchronous acquisition and analysis of complex signal streams of neurophysiological data (e.g., EEG, fNIRS) and behavioral data (e.g., motion capture). The fusion of those data streams has the potential to provide new insights into cortical mechanisms during movement, guide the development of rehabilitation practices, and become a tool for assessment and therapy in neurorehabilitation.

RESEARCH OBJECTIVE

This paper aims to review the existing literature on the combined use of motion capture and functional neuroimaging in motor rehabilitation. The objective is to understand the diversity and maturity of technological solutions employed and explore the clinical advantages of this multimodal approach.

METHODS

This paper reviews literature related to the combined use of functional neuroimaging and motion capture for motor rehabilitation following the PRISMA guidelines. Besides study and participant characteristics, technological aspects of the used systems, signal processing methods, and the nature of multimodal feature synchronization and fusion were extracted.

RESULTS

Out of 908 publications, 19 were included in the final review. Basic or translation studies were mainly represented and based predominantly on healthy participants or stroke patients. EEG and mechanical motion capture technologies were most used for biomechanical data acquisition, and their subsequent processing is based mainly on traditional methods. The system synchronization techniques at large were underreported. The fusion of multimodal features mainly supported the identification of movement-related cortical activity, and statistical methods were occasionally employed to examine cortico-kinematic relationships.

CONCLUSION

The fusion of motion capture and functional neuroimaging might offer advantages for motor rehabilitation in the future. Besides facilitating the assessment of cognitive processes in real-world settings, it could also improve rehabilitative devices' usability in clinical environments. Further, by better understanding cortico-peripheral coupling, new neuro-rehabilitation methods can be developed, such as personalized proprioceptive training. However, further research is needed to advance our knowledge of cortical-peripheral coupling, evaluate the validity and reliability of multimodal parameters, and enhance user-friendly technologies for clinical adaptation.

Research on multi-dimensional intelligent quantitative assessment of upper limb function based on kinematic parameters

BACKGROUND

Rehabilitation assessment is a critical component of rehabilitation treatment.

OBJECTIVE

This study focuses on a comprehensive analysis of patients' movement performance using the upper limb rehabilitation robot. It quantitatively assessed patients' motor control ability and constructed an intelligent grading model of functional impairments. These findings contribute to a deeper understanding of patients' motor ability and provide valuable insights for personalized rehabilitation interventions.

METHODS

Patients at different Brunnstrom stages underwent rehabilitation training using the upper limb rehabilitation robot, and data on the distal movement positions of the patients' upper limbs were collected. A total of 22 assessment metrics related to movement efficiency, smoothness, and accuracy were extracted. The performance of these assessment metrics was measured using the Mann-Whitney U test and Pearson correlation analysis. Due to the issue of imbalanced sample categories, data augmentation was performed using the Synthetic Minority Over-sampling Technique (SMOTE) algorithm based on weighted sampling, and an intelligent grading model of functional impairment based on the Extreme Gradient Boosting Tree (XGBoost) algorithm was constructed.

RESULTS

Sixteen assessment metrics were screened. These metrics were effectively normalized to their maximum values, enabling the derivation of quantitative assessment scores for motor control ability across the three dimensions through a weighted fusion approach. Notably, when applied to the data-enhanced dataset, the intelligent grading model exhibited remarkable improvement, achieving an accuracy rate exceeding 0.98. Moreover, significant enhancements were observed in terms of precision, recall, and f1-score.

CONCLUSION

The research findings demonstrate that this study enables the quantitative assessment of patients' motor control ability and intelligent grading of functional impairments, thereby contributing to the efficiency enhancement of clinical rehabilitation assessment. Moreover, this method resolves the issues associated with the subjectivity and prolonged periods of traditional rehabilitation assessment methods.

User Experience Evaluation of Upper Limb Rehabilitation Robots: Implications for Design Optimization: A Pilot Study

With the development of science and technology, people are trying to use robots to assist in stroke rehabilitation training. This study aims to analyze the result of the formative test to provide the orientation of upper limb rehabilitation robot design optimization. We invited 21 physical therapists (PTs) and eight occupational therapists (OTs) who had no experience operating any upper limb rehabilitation robots before, and 4 PTs and 1 OT who had experience operating upper limb rehabilitation robots. Data statistics use the Likert scale. The general group scored 3.5 for safety-related topics, while the experience group scored 4.5. In applicability-related questions, the main function score was 2.3 in the general group and 2.4 in the experience group; and the training trajectory score was 3.5 in the general group and 5.0 in the experience group. The overall ease of use score was 3.1 in the general group and 3.6 in the experience group. There was no statistical difference between the two groups. The methods to retouch the trajectory can be designed through the feedback collected in the formative test and gathering further detail in the next test. Further details about the smooth trajectory must be confirmed in the next test. The optimization of the recording process is also important to prevent users from making additional effort to know it well.

A Narrative Review on Multi-Domain Instrumental Approaches to Evaluate Neuromotor Function in Rehabilitation

In clinical scenarios, the use of biomedical sensors, devices and multi-parameter assessments is fundamental to provide a comprehensive portrait of patients' state, in order to adapt and personalize rehabilitation interventions and support clinical decision-making. However, there is a huge gap between the potential of the multidomain techniques available and the limited practical use that is made in the clinical scenario. This paper reviews the current state-of-the-art and provides insights into future directions of multi-domain instrumental approaches in the clinical assessment of patients involved in neuromotor rehabilitation. We also summarize the main achievements and challenges of using multi-domain approaches in the assessment of rehabilitation for various neurological disorders affecting motor functions. Our results showed that multi-domain approaches combine information and measurements from different tools and biological signals, such as kinematics, electromyography (EMG), electroencephalography (EEG), near-infrared spectroscopy (NIRS), and clinical scales, to provide a comprehensive and objective evaluation of patients' state and recovery. This multi-domain approach permits the progress of research in clinical and rehabilitative practice and the understanding of the pathophysiological changes occurring during and after rehabilitation. We discuss the potential benefits and limitations of multi-domain approaches for clinical decision-making, personalized therapy, and prognosis. We conclude by highlighting the need for more standardized methods, validation studies, and the integration of multi-domain approaches in clinical practice and research.

Exoskeleton-Assisted Anthropomorphic Movement Training for the Upper Limb After Stroke: The EAMT Randomized Trial

BACKGROUND

Robot-assisted arm training is generally delivered in the robot-like manner of planar or mechanical 3-dimensional movements. It remains unclear whether integrating upper extremity (UE) natural coordinated patterns into a robotic exoskeleton can improve outcomes. The study aimed to compare conventional therapist-mediated training to the practice of human-like gross movements derived from 5 typical UE functional activities managed with exoskeletal assistance as needed for patients after stroke.

METHODS

In this randomized, single-blind, noninferiority trial, patients with moderate-to-severe UE motor impairment due to subacute stroke were randomly assigned (1:1) to receive 20 sessions of 45-minute exoskeleton-assisted anthropomorphic movement training or conventional therapy. Treatment allocation was masked from independent assessors, but not from patients or investigators. The primary outcome was the change in the Fugl-Meyer Assessment for Upper Extremity from baseline to 4 weeks against a prespecified noninferiority margin of 4 points. Superiority would be tested if noninferiority was demonstrated. Post hoc subgroup analyses of baseline characteristics were performed for the primary outcome.

RESULTS

Between June 2020 and August 2021, totally 80 inpatients (67 [83.8%] males; age, 51.9±9.9 years; days since stroke onset, 54.6±38.0) were enrolled, randomly assigned to the intervention, and included in the intention-to-treat analysis. The mean Fugl-Meyer Assessment for Upper Extremity change in exoskeleton-assisted anthropomorphic movement training (14.73 points; [95% CI, 11.43-18.02]) was higher than that of conventional therapy (9.90 points; [95% CI, 8.15-11.65]) at 4 weeks (adjusted difference, 4.51 points [95% CI, 1.13-7.90]). Moreover, post hoc analysis favored the patient subgroup (Fugl-Meyer Assessment for Upper Extremity score, 23-38 points) with moderately severe motor impairment.

CONCLUSIONS

Exoskeleton-assisted anthropomorphic movement training appears to be effective for patients with subacute stroke through repetitive practice of human-like movements. Although the results indicate a positive sign for exoskeleton-assisted anthropomorphic movement training, further investigations into the long-term effects and paradigm optimization are warranted.

REGISTRATION

URL: https://www.chictr.org.cn; Unique identifier: ChiCTR2100044078.

Substituting some unassisted practice with robotic guidance: Assessing the feasibility of auditory-cued mixed practice for music-based interventions

BACKGROUND

There is equivocal evidence regarding the effectiveness of robotic guidance on the (re)learning of voluntary motor skills. Robotic guidance can improve the performance of continuous/ tracking skills, although being seldom more effective than unassisted practice alone. However, most of the previous studies employed robotic guidance on all intervention trials. Recently, we showed that mixing robotic guidance with unassisted practice (i.e., mixed practice) can significantly improve the learning of a golf putting task. Yet, these mixed practice studies involved self-paced movements in a standing posture, thus less applicable to rehabilitation contexts.

OBJECTIVE

The current study aimed to investigate the influence of mixed practice on the timing accuracy of an upper-limb, rhythmic, sequential task. The goal was to assess the feasibility of integrating mixed practice with music-based interventions.

METHODS

Two groups of participants performed circle-drawing sequences in synchrony with rhythmic auditory signals. They completed a pre-test and an acquisition phase, followed by immediate retention and transfer tests. One group received robotic guidance on 50% of the acquisition trials (i.e., mixed practice), whereas another group always practiced unassisted. The pre-test, retention, and transfer tests were performed unassisted.

RESULTS

Both groups significantly improved their timing accuracy and precision between the pre-test and the retention test.

CONCLUSION

This study provides further evidence that mixed practice can facilitate the (re)learning of voluntary actions, especially with the type of externally paced upper-limb movements employed in music-based interventions.

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.

Time course of recovery of different motor functions following a reproducible cortical infarction in non-human primates

A major challenge in human stroke research is interpatient variability in the extent of sensorimotor deficits and determining the time course of recovery following stroke. Although the relationship between the extent of the lesion and the degree of sensorimotor deficits is well established, the factors determining the speed of recovery remain uncertain. To test these experimentally, we created a cortical lesion over the motor cortex using a reproducible approach in four common marmosets, and characterized the time course of recovery by systematically applying several behavioral tests before and up to 8 weeks after creation of the lesion. Evaluation of in-cage behavior and reach-to-grasp movement revealed consistent motor impairments across the animals. In particular, performance in reaching and grasping movements continued to deteriorate until 4 weeks after creation of the lesion. We also found consistent time courses of recovery across animals for in-cage and grasping movements. For example, in all animals, the score for in-cage behaviors showed full recovery at 3 weeks after creation of the lesion, and the performance of grasping movement partially recovered from 4 to 8 weeks. In addition, we observed longer time courses of recovery for reaching movement, which may rely more on cortically initiated control in this species. These results suggest that different recovery speeds for each movement could be influenced by what extent the cortical control is required to properly execute each movement.

Elbow Joint Stiffness Functional Scales Based on Hill's Muscle Model and Genetic Optimization

The ultimate goal of rehabilitation engineering is to provide objective assessment tools for the level of injury and/or the degree of neurorehabilitation recovery based on a combination of different sensing technologies that enable the monitoring of relevant measurable variables, as well as the assessment of non-measurable variables (such as muscle effort/force and joint mechanical stiffness). This paper aims to present a feasibility study for a general assessment methodology for subject-specific non-measurable elbow model parameter prediction and elbow joint stiffness estimation. Ten participants without sensorimotor disorders performed a modified "Reach and retrieve" task of the Wolf Motor Function Test while electromyography (EMG) data of an antagonistic muscle pair (the triceps brachii long head and biceps brachii long head muscle) and elbow angle were simultaneously acquired. A complete list of the Hill's muscle model and passive joint structure model parameters was generated using a genetic algorithm (GA) on the acquired training dataset with a maximum deviation of 6.1% of the full elbow angle range values during the modified task 8 of the Wolf Motor Function Test, and it was also verified using two experimental test scenarios (a task tempo variation scenario and a load variation scenario with a maximum deviation of 8.1%). The recursive least square (RLS) algorithm was used to estimate elbow joint stiffness (Stiffness) based on the estimated joint torque and the estimated elbow angle. Finally, novel Stiffness scales (general patterns) for upper limb functional assessment in the two performed test scenarios were proposed. The stiffness scales showed an exponentially increasing trend with increasing movement tempo, as well as with increasing weights. The obtained general Stiffness patterns from the group of participants without sensorimotor disorders could significantly contribute to the further monitoring of motor recovery in patients with sensorimotor disorders.

Assistance level quantification-based human-robot interaction space reshaping for rehabilitation training

Stroke has become a major disease that seriously threatens human health due to its high incidence and disability rates. Most patients undergo upper limb motor dysfunction after stroke, which significantly impairs the ability of stroke survivors in their activities of daily living (ADL). Robots provide an optional solution for stroke rehabilitation by attending therapy in the hospital and the community, however, the rehabilitation robot still has difficulty in providing needed assistance interactively like human clinicians in conventional therapy. For safe and rehabilitation training, a human-robot interaction space reshaping method was proposed based on the recovery states of patients. According to different recovery states, we designed seven experimental protocols suitable for distinguishing rehabilitation training sessions. To achieve assist-as-needed (AAN) control, a PSO-SVM classification model and an LSTM-KF regression model were introduced to recognize the motor ability of patients with electromyography (EMG) and kinematic data, and a region controller for interaction space shaping was studied. Ten groups of offline and online experiments and corresponding data processing were conducted, and the machine learning and AAN control results were presented, which ensured the effective and the safe upper limb rehabilitation training. To discuss the human-robot interaction in different training stages and sessions, we defined a quantified assistance level index that characterizes the rehabilitation needs by considering the engagement of the patients and had the potential to apply in clinical upper limb rehabilitation training.

Interaction of network and rehabilitation therapy parameters in defining recovery after stroke in a Bilateral Neural Network

BACKGROUND

Restoring movement after hemiparesis caused by stroke is an ongoing challenge in the field of rehabilitation. With several therapies in use, there is no definitive prescription that optimally maps parameters of rehabilitation with patient condition. Recovery gets further complicated once patients enter chronic phase. In this paper, we propose a rehabilitation framework based on computational modeling, capable of mapping patient characteristics to parameters of rehabilitation therapy.

METHOD

To build such a system, we used a simple convolutional neural network capable of performing bilateral reaching movements in 3D space using stereovision. The network was designed to have bilateral symmetry to reflect the bilaterality of the cerebral hemispheres with the two halves joined by cross-connections. This network was then modified according to 3 chosen patient characteristics-lesion size, stage of recovery (acute or chronic) and structural integrity of cross-connections (analogous to Corpus Callosum). Similarly, 3 parameters were used to define rehabilitation paradigms-movement complexity (Exploratory vs Stereotypic), hand selection mode (move only affected arm, CIMT vs move both arms, BMT), and extent of plasticity (local vs global). For each stroke condition, performance under each setting of the rehabilitation parameters was measured and results were analyzed to find the corresponding optimal rehabilitation protocol.

RESULTS

Upon analysis, we found that regardless of patient characteristics network showed better recovery when high complexity movements were used and no significant difference was found between the two hand selection modes. Contrary to these two parameters, optimal extent of plasticity was influenced by patient characteristics. For acute stroke, global plasticity is preferred only for larger lesions. However, for chronic, plasticity varies with structural integrity of cross-connections. Under high integrity, chronic prefers global plasticity regardless of lesion size, but with low integrity local plasticity is preferred.

CONCLUSION

Clinically translating the results obtained, optimal recovery may be observed when paretic arm explores the available workspace irrespective of the hand selection mode adopted. However, the extent of plasticity to be used depends on characteristics of the patient mainly stage of stroke and structural integrity. By using systems as developed in this study and modifying rehabilitation paradigms accordingly it is expected post-stroke recovery can be maximized.

Identification of inverse kinematic parameters in redundant systems: Towards quantification of inter-joint coordination in the human upper extremity

Understanding and quantifying inter-joint coordination is valuable in several domains such as neurorehabilitation, robot-assisted therapy, robotic prosthetic arms, and control of supernumerary arms. Inter-joint coordination is often understood as a consistent spatiotemporal relation among kinematically redundant joints performing functional and goal-oriented movements. However, most approaches in the literature to investigate inter-joint coordination are limited to analysis of the end-point trajectory or correlation analysis of the joint rotations without considering the underlying task; e.g., creating a desirable hand movement toward a goal as in reaching motions. This work goes beyond this limitation by taking a model-based approach to quantifying inter-joint coordination. More specifically, we use the weighted pseudo-inverse of the Jacobian matrix and its associated null-space to explain the human kinematics in reaching tasks. We propose a novel algorithm to estimate such Inverse Kinematics weights from observed kinematic data. These estimated weights serve as a quantification for spatial inter-joint coordination; i.e., how costly a redundant joint is in its contribution to creating an end-effector velocity. We apply our estimation algorithm to datasets obtained from two different experiments. In the first experiment, the estimated Inverse Kinematics weights pinpoint how individuals change their Inverse Kinematics strategy when exposed to the viscous field wearing an exoskeleton. The second experiment shows how the resulting Inverse Kinematics weights can quantify a robotic prosthetic arm's contribution (or the level of assistance).

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.

Automated patient-robot assignment for a robotic rehabilitation gym: a simplified simulation model

Background

A robotic rehabilitation gym can be defined as multiple patients training with multiple robots or passive sensorized devices in a group setting. Recent work with such gyms has shown positive rehabilitation outcomes; furthermore, such gyms allow a single therapist to supervise more than one patient, increasing cost-effectiveness. To allow more effective multipatient supervision in future robotic rehabilitation gyms, we propose an automated system that could dynamically assign patients to different robots within a session in order to optimize rehabilitation outcome.

Methods

As a first step toward implementing a practical patient-robot assignment system, we present a simplified mathematical model of a robotic rehabilitation gym. Mixed-integer nonlinear programming algorithms are used to find effective assignment and training solutions for multiple evaluation scenarios involving different numbers of patients and robots (5 patients and 5 robots, 6 patients and 5 robots, 5 patients and 7 robots), different training durations (7 or 12 time steps) and different complexity levels (whether different patients have different skill acquisition curves, whether robots have exit times associated with them). In all cases, the goal is to maximize total skill gain across all patients and skills within a session.

Results

Analyses of variance across different scenarios show that disjunctive and time-indexed optimization models significantly outperform two baseline schedules: staying on one robot throughout a session and switching robots halfway through a session. The disjunctive model results in higher skill gain than the time-indexed model in the given scenarios, and the optimization duration increases as the number of patients, robots and time steps increases. Additionally, we discuss how different model simplifications (e.g., perfectly known and predictable patient skill level) could be addressed in the future and how such software may eventually be used in practice.

Conclusions

Though it involves unrealistically simple scenarios, our study shows that intelligently moving patients between different rehabilitation robots can improve overall skill acquisition in a multi-patient multi-robot environment. While robotic rehabilitation gyms are not yet commonplace in clinical practice, prototypes of them already exist, and our study presents a way to use intelligent decision support to potentially enable more efficient delivery of technologically aided rehabilitation.

A unified scheme for the benchmarking of upper limb functions in neurological disorders

BACKGROUND

In neurorehabilitation, we are witnessing a growing awareness of the importance of standardized quantitative assessment of limb functions. Detailed assessments of the sensorimotor deficits following neurological disorders are crucial. So far, this assessment has relied mainly on clinical scales, which showed several drawbacks. Different technologies could provide more objective and repeatable measurements. However, the current literature lacks practical guidelines for this purpose. Nowadays, the integration of available metrics, protocols, and algorithms into one harmonized benchmarking ecosystem for clinical and research practice is necessary.

METHODS

This work presents a benchmarking framework for upper limb capacity. The scheme resulted from a multidisciplinary and iterative discussion among several partners with previous experience in benchmarking methodology, robotics, and clinical neurorehabilitation. We merged previous knowledge in benchmarking methodologies for human locomotion and direct clinical and engineering experience in upper limb rehabilitation. The scheme was designed to enable an instrumented evaluation of arm capacity and to assess the effectiveness of rehabilitative interventions with high reproducibility and resolution. It includes four elements: (1) a taxonomy for motor skills and abilities, (2) a list of performance indicators, (3) a list of required sensor modalities, and (4) a set of reproducible experimental protocols.

RESULTS

We proposed six motor primitives as building blocks of most upper-limb daily-life activities and combined them into a set of functional motor skills. We identified the main aspects to be considered during clinical evaluation, and grouped them into ten motor abilities categories. For each ability, we proposed a set of performance indicators to quantify the proposed ability on a quantitative and high-resolution scale. Finally, we defined the procedures to be followed to perform the benchmarking assessment in a reproducible and reliable way, including the definition of the kinematic models and the target muscles.

CONCLUSIONS

This work represents the first unified scheme for the benchmarking of upper limb capacity. To reach a consensus, this scheme should be validated with real experiments across clinical conditions and motor skills. This validation phase is expected to create a shared database of human performance, necessary to have realistic comparisons of treatments and drive the development of new personalized technologies.

Experiences of patients who had a stroke and rehabilitation professionals with upper limb rehabilitation robots: a qualitative systematic review protocol

INTRODUCTION

Emerging evidence suggests that robotic devices for upper limb rehabilitation after a stroke may improve upper limb function. For robotic upper limb rehabilitation in stroke to be successful, patients' experiences and those of the rehabilitation professionals must be considered. Therefore, this review aims to synthesise the available evidence on experiences of patients after a stroke with rehabilitation robots for upper limb rehabilitation and the experiences of rehabilitation professionals with rehabilitation robots for upper limb stroke rehabilitation.

METHODS AND ANALYSIS

Database search will include MEDLINE (Ovid), EMBASE (Elsevier), Cochrane CENTRAL, PsycINFO, Scopus, Web of Science, IEEE and CINAHL (EBSCOhost). Grey literature from Open Grey, PsyArXiv, bioRxiv, medRxiv and Google Scholar will also be searched. Qualitative studies or results from mixed-method studies that include adult patients after a stroke who use upper limb rehabilitation robots, either supervised by rehabilitation professionals or by patients themselves, at any stage of their rehabilitation and/or stroke professionals who use upper limb rehabilitation robots will be included. Robotic upper limb rehabilitation provided by students, healthcare assistants, technicians, non-professional caregivers, family caregivers, volunteer caregivers or other informal caregivers will be excluded. Articles published in English will be considered regardless of date of publication. Studies will be screened and critically appraised for methodological quality by two independent reviewers. A standardised tool from JBI System for the Unified Management, Assessment and Review of Information for data extraction, the meta-aggregation approach for data synthesis and the ConQual approach for confidence evaluation will be followed.

ETHICS AND DISSEMINATION

As this systematic review is based on previously published research, no informed consent or ethical approval is required. It is anticipated that this systematic review will highlight the experiences of patients after a stroke and perceived facilitators and barriers for rehabilitation professionals on this topic, which will be disseminated through peer-reviewed publications and national and international conferences.

PROSPERO REGISTRATION NUMBER

CRD42022321402.

The Impact of Cognitive Impairment on Robot-Based Upper-Limb Motor Assessment in Chronic Stroke

BACKGROUND

Chronic upper extremity motor deficits are present in up to 65% of stroke survivors, and cognitive impairment is prevalent in 46-61% of stroke survivors even 10 years after their stroke. Robot-assisted therapy programs tend to focus on motor recovery and do not include stroke patients with cognitive impairment.

OBJECTIVE

This study aims to investigate performance on the individual cognitive domains evaluated in the MoCA and their relation to upper-limb motor performance on a robotic system.

METHODS

Participants were recruited from the stroke population with a wide range of cognitive and motor levels to complete a trajectory tracking task using the Haptic TheraDrive rehabilitation robot system. Motor performance was evaluated against standard clinical cognitive and motor assessments. Our hypothesis is that the cognitive domains involved in the visuomotor tracking task are significant predictors of performance on the robot-based task and that impairment in these domains results in worse motor performance on the task compared to subjects with no cognitive impairment.

RESULTS

Our results support the hypothesis that visuospatial and executive function have a significant impact on motor performance, with differences emerging between different functional groups on the various robot-based metrics. We also show that the kinematic metrics from this task differentiate cognitive-motor functional groups differently.

CONCLUSION

This study demonstrates that performance on a motor-based robotic assessment task also involves a significant visuospatial and executive function component and highlights the need to account for cognitive impairment in the assessment of motor performance.

Assessment of Upper Extremity Function in People With Stroke Based on the Framework of the ICF: A Narrative Review

Although there are many assessment tools for upper extremity (UE) function, it is still difficult to select an appropriate outcome measurement for the rehabilitation process of individuals with stroke. This review aims to classify each tool within the International Classification of Functioning, Disability and Health (ICF) framework and provide an overview of UE assessments. Through a comprehensive understanding of assessments based on ICF, health care professionals will be able to choose suitable measurement tools for individuals, facilitating their rehabilitation.

Reliability and validity of digital health metrics for assessing arm and hand impairments in an ataxic disorder

OBJECTIVES

Autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS) is the second most frequent recessive ataxia and commonly features reduced upper limb coordination. Sensitive outcome measures of upper limb coordination are essential to track disease progression and the effect of interventions. However, available clinical assessments are insufficient to capture behavioral variability and detailed aspects of motor control. While digital health metrics extracted from technology-aided assessments promise more fine-grained outcome measures, these have not been validated in ARSACS. Thus, the aim was to document the metrological properties of metrics from a technology-aided assessment of arm and hand function in ARSACS.

METHODS

We relied on the Virtual Peg Insertion Test (VPIT) and used a previously established core set of 10 digital health metrics describing upper limb movement and grip force patterns during a pick-and-place task. We evaluated reliability, measurement error, and learning effects in 23 participants with ARSACS performing three repeated assessment sessions. In addition, we documented concurrent validity in 57 participants with ARSACS performing one session.

RESULTS

Eight metrics had excellent test-retest reliability (intraclass correlation coefficient 0.89 ± 0.08), five low measurement error (smallest real difference % 25.4 ± 5.7), and none strong learning effects (systematic change η -0.11 ± 2.5). Significant correlations (ρ 0.39 ± 0.13) with clinical scales describing gross and fine dexterity and lower limb coordination were observed.

INTERPRETATION

This establishes eight digital health metrics as valid and robust endpoints for cross-sectional studies and five metrics as potentially sensitive endpoints for longitudinal studies in ARSACS, thereby promising novel insights into upper limb sensorimotor control.

Characterization of Bimanual Cyclical Tasks from Single-trial EEG-fNIRS Measurements

Robot-assisted bimanual training is promising to improve motor function and cortical reorganization for hemiparetic stroke patients. Closing the rehabilitation training loop with neurofeedback can help refine training protocols in time for better engagements and outcomes. However, due to the low signal-to- noise ratio (SNR) and non-stationary properties of neural signals, reliable characterization of bimanual training-induced neural activities from single-trial measurement is challenging. In this study, ten human participants were recruited conducting robot-assisted bimanual cyclical tasks (in-phase, 90° out-of-phase, and anti-phase) when concurrent electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) were recorded. A unified EEG-fNIRS bimodal signal processing framework was proposed to characterize neural activities induced by three types of bimanual cyclical tasks. In this framework, novel artifact removal methods were used to improve the SNR and the task-related component analysis (TRCA) was introduced to increase the reproducibility of EEG-fNIRS bimodal features. The optimized features were transformed into low-dimensional indicators to reliably characterize bimanual training-induced neural activation. The SVM classification results of three bimanual cyclical tasks revealed a good discrimination ability of EEG-fNIRS bimodal indicators (90.1%), which was higher than that using EEG (74.8%) or fNIRS (82.2%) alone, supporting the proposed method as a feasible technique to characterize neural activities during robot-assisted bimanual training.

Retrospective Robot-Measured Upper Limb Kinematic Data From Stroke Patients Are Novel Biomarkers

Background: The efficacy of upper-limb Robot-assisted Therapy (ulRT) in stroke subjects is well-established. The robot-measured kinematic data can assess the biomechanical changes induced by ulRT and the progress of patient over time. However, literature on the analysis of pre-treatment kinematic parameters as predictive biomarkers of upper limb recovery is limited.

Objective: The aim of this study was to calculate pre-treatment kinematic parameters from point-to-point reaching movements in different directions and to identify biomarkers of upper-limb motor recovery in subacute stroke subjects after ulRT.

Methods: An observational retrospective study was conducted on 66 subacute stroke subjects who underwent ulRT with an end-effector robot. Kinematic parameters were calculated from the robot-measured trajectories during movements in different directions. A Generalized Linear Model (GLM) was applied considering the post-treatment Upper Limb Motricity Index and the kinematic parameters (from demanding directions of movement) as dependent variables, and the pre-treatment kinematic parameters as independent variables.

Results: A subset of kinematic parameters significantly predicted the motor impairment after ulRT: the accuracy in adduction and internal rotation movements of the shoulder was the major predictor of post-treatment Upper Limb Motricity Index. The post-treatment kinematic parameters of the most demanding directions of movement significantly depended on the ability to execute elbow flexion-extension and abduction and external rotation movements of the shoulder at baseline.

Conclusions: The multidirectional analysis of robot-measured kinematic data predicts motor recovery in subacute stroke survivors and paves the way in identifying subjects who may benefit more from ulRT.

Neuromechanical Biomarkers for Robotic Neurorehabilitation

One of the current challenges for translational rehabilitation research is to develop the strategies to deliver accurate evaluation, prediction, patient selection, and decision-making in the clinical practice. In this regard, the robot-assisted interventions have gained popularity as they can provide the objective and quantifiable assessment of the motor performance by taking the kinematics parameters into the account. Neurophysiological parameters have also been proposed for this purpose due to the novel advances in the non-invasive signal processing techniques. In addition, other parameters linked to the motor learning and brain plasticity occurring during the rehabilitation have been explored, looking for a more holistic rehabilitation approach. However, the majority of the research done in this area is still exploratory. These parameters have shown the capability to become the “biomarkers” that are defined as the quantifiable indicators of the physiological/pathological processes and the responses to the therapeutical interventions. In this view, they could be finally used for enhancing the robot-assisted treatments. While the research on the biomarkers has been growing in the last years, there is a current need for a better comprehension and quantification of the neuromechanical processes involved in the rehabilitation. In particular, there is a lack of operationalization of the potential neuromechanical biomarkers into the clinical algorithms. In this scenario, a new framework called the “Rehabilomics” has been proposed to account for the rehabilitation research that exploits the biomarkers in its design. This study provides an overview of the state-of-the-art of the biomarkers related to the robotic neurorehabilitation, focusing on the translational studies, and underlying the need to create the comprehensive approaches that have the potential to take the research on the biomarkers into the clinical practice. We then summarize some promising biomarkers that are being under investigation in the current literature and provide some examples of their current and/or potential applications in the neurorehabilitation. Finally, we outline the main challenges and future directions in the field, briefly discussing their potential evolution and prospective.

Kinematic Assessment to Measure Change in Impairment during Active and Active-Assisted Type of Robotic Rehabilitation for Patients with Stroke

Analysis of kinematic features related to clinical assessment scales may qualitatively improve the evaluation of upper extremity movements of stroke patients. We aimed to investigate kinematic features that could correlate the change in the Fugl-Meyer Assessment (FMA) score of stroke survivors through upper extremity robotic rehabilitation. We also analyzed whether changes in kinematic features by active and active-assisted robotic rehabilitation correlated differently with changes in FMA scores. Fifteen stroke patients participated in the upper extremity robotic rehabilitation program, and nine kinematic features were calculated from reach tasks for assessment. Simple and multiple linear regression analyses were used to characterize correlations. Features representing movement speed were associated with changes in FMA scores for the group that used an active rehabilitation robot. In contrast, in the group that used an active-assisted rehabilitation robot, features representing movement smoothness were associated with changes in the FMA score. These estimates can be an important basis for kinematic analysis to complement clinical scales.

Effect of Robotic Exoskeleton Motion Constraints on Upper Limb Muscle Synergies: A Case Study

Evidence exists that changes in composition, timing, and number of muscle synergies can be correlated to functional changes resulting from neurological injury. These changes can also serve as an indicator of level of motor impairment. As such, synergy analysis can be used as an assessment tool for robotic rehabilitation. However, it is unclear whether using a rehabilitation robot to isolate limb movements during training affects the subject's muscle synergies, which would affect synergy-based assessments. In this case study, electromyographic (EMG) data were collected to analyze muscle synergies generated during single degree-of-freedom (DoF) elbow and wrist movements performed by a single healthy subject in a four DoF robotic exoskeleton. For each trial, the subject was instructed to move a single DoF from a neutral position out to a target and back while the remaining DoFs were held in a neutral position by either the robot (constrained) or the subject (unconstrained). Four factorization methods were used to calculate muscle synergies for both types of trials: concatenation, averaging, single trials, and bootstrapping. The number of synergies was chosen to achieve 90% global variability accounted for. Our preliminary results indicate that muscle synergy composition and timing were highly similar for constrained and unconstrained trials, though some differences between the four factorization methods existed. These differences could be explained by higher trial-to-trial EMG variability for the unconstrained trials. These results suggest that using a robotic exoskeleton to constrain limb movements during robotic training may not alter a subject's muscle synergies, at least for healthy subjects.

Exoskeleton-Assisted Anthropomorphic Movement Training (EAMT) for Poststroke Upper Limb Rehabilitation: A Pilot Randomized Controlled Trial

OBJECTIVE

To investigate the feasibility of exoskeleton-assisted anthropomorphic movement training (EAMT) and its effects on upper extremity motor impairment, function, and kinematics after stroke.

DESIGN

A single-blind pilot randomized controlled trial.

SETTING

Stroke rehabilitation inpatient unit.

PARTICIPANTS

Participants with a hemiplegia (N=20) due to a first-ever, unilateral, subacute stroke who had a score of 8-47 on the Fugl-Meyer Assessment for Upper Extremity (FMA-UE).

INTERVENTIONS

The exoskeleton group received EAMT therapy that provided task-specific training under anthropomorphic trajectories and postures. The control group received conventional upper limb therapy. For both groups, therapy was delivered at the same intensity, frequency, and duration: 45 minutes daily, 5 days per week, for 4 weeks.

MAIN OUTCOME MEASURES

Primary outcome: feasibility analysis.

SECONDARY OUTCOMES

FMA-UE, Action Research Arm Test (ARAT), modified Barthel Index (MBI), and kinematic metrics during exoskeleton therapy.

RESULTS

Twenty participants with subacute stroke were recruited and completed all therapy sessions. EAMT therapy was feasible and acceptable for the participants. The recruitment rate, retention rate, and number of therapists required for EAMT therapy were acceptable compared with other robotic trials. EAMT was determined to be safe, as no adverse event occurred except tolerable muscle fatigue in 2 participants. There were significant between-group differences in the change scores of FMA-UE (difference, 4.30 points; P=.04) and MBI (difference, 8.70 points; P=.03) in favor of EAMT therapy. No significant between-group difference was demonstrated for the change scores of ARAT (P=.18). Participants receiving EAMT showed significant improvements in kinematic metrics after treatment (P<.01).

CONCLUSIONS

Our results indicate that EAMT is a feasible approach and may improve upper extremity motor impairment, activities of daily living, and kinematics after stroke. However, fully powered randomized controlled trials are warranted to confirm the results of this pilot study and explore the underlying mechanisms by which EAMT therapy might work.

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.

Clinical validation of kinematic assessments of post-stroke upper limb movements with a multi-joint arm exoskeleton

Background

The clinical evaluation of the upper limb of severely impaired stroke patient is challenging. Sensor-based assessments may allow for an objective evaluation of this patient population. This study investigated the validity of a device-assisted approach in comparison to the clinical outcome that it is supposed to reflect.

Methods

In nineteen severely impaired chronic stroke patients, we applied a gravity-compensating, multi-joint arm exoskeleton (Armeo Spring) and compared this sensor-based assessment with the clinical outcome measure Upper Extremity Fugl-Meyer Assessment (UE-FMA) scale. Specifically, we assessed separately and subsequently the range of motion in joint space for four single joints (i.e., wrist, elbow and shoulder flexion/extension (FE), and shoulder internal/external rotation (IER)), and the closing and opening of the hand with a pressure sensor placed in the handle.

Results

Within the kinematic parameters, a strong correlation was observed between wrist and elbow FE (r > 0.7, p < 0.003; Bonferroni corrected). The UE-FMA was significantly predicted by a multiple regression model (F (5, 13) = 12.22, p < 0.0005, adj. R² = 0.83). Both shoulder IER and grip pressure added significantly (p < 0.05) to the prediction with the standardized coefficients β of 0.55 and 0.38, respectively.

Conclusions

By applying an exoskeleton-based self-contained evaluation of single-joint movements, a clinically valid assessment of the upper limb range of motion in severely impaired stroke patients is feasible. Shoulder IER contributed most relevantly to the prediction of the clinical status. These findings need to be confirmed in a large, independent patient cohort.

Relative independence of upper limb position sense and reaching in children with hemiparetic perinatal stroke

BACKGROUND

Studies using clinical measures have suggested that proprioceptive dysfunction is related to motor impairment of the upper extremity following adult stroke. We used robotic technology and clinical measures to assess the relationship between position sense and reaching with the hemiparetic upper limb in children with perinatal stroke.

METHODS

Prospective term-born children with magnetic resonance imaging-confirmed perinatal ischemic stroke and upper extremity deficits were recruited from a population-based cohort. Neurotypical controls were recruited from the community. Participants completed two tasks in the Kinarm robot: arm position-matching (three parameters: variability [Var_xy], contraction/expansion [Area_xy], systematic spatial shift [Shift_xy]) and visually guided reaching (five parameters: posture speed [PS], reaction time [RT], initial direction error [IDE], speed maxima count [SMC], movement time [MT]). Additional clinical assessments of sensory (thumb localization test) and motor impairment (Assisting Hand Assessment, Chedoke-McMaster Stroke Assessment) were completed and compared to robotic measures.

RESULTS

Forty-eight children with stroke (26 arterial, 22 venous, mean age: 12.0 ± 4.0 years) and 145 controls (mean age: 12.8 ± 3.9 years) completed both tasks. Position-matching performance in children with stroke did not correlate with performance on the visually guided reaching task. Robotic sensory and motor measures correlated with only some clinical tests. For example, AHA scores correlated with reaction time (R = - 0.61, p < 0.001), initial direction error (R = - 0.64, p < 0.001), and movement time (R = - 0.62, p < 0.001).

CONCLUSIONS

Robotic technology can quantify complex, discrete aspects of upper limb sensory and motor function in hemiparetic children. Robot-measured deficits in position sense and reaching with the contralesional limb appear to be relatively independent of each other and correlations for both with clinical measures are modest. Knowledge of the relationship between sensory and motor impairment may inform future rehabilitation strategies and improve outcomes for children with hemiparetic cerebral palsy.

Kinematic of the Position and Orientation Synchronization of the Posture of a n DoF Upper-Limb Exoskeleton with a Virtual Object in an Immersive Virtual Reality Environment

Exoskeletons are an external structural mechanism with joints and links that work in tandem with the user, which increases, reinforces, or restores human performance. Virtual Reality can be used to produce environments, in which the intensity of practice and feedback on performance can be manipulated to provide tailored motor training. Will it be possible to combine both technologies and have them synchronized to reach better performance? This paper consists of the kinematics analysis for the position and orientation synchronization between an n DoF upper-limb exoskeleton pose and a projected object in an immersive virtual reality environment using a VR headset. To achieve this goal, the exoskeletal mechanism is analyzed using Euler angles and the Pieper technique to obtain the equations that lead to its orientation, forward, and inverse kinematic models. This paper extends the author’s previous work by using an early stage upper-limb exoskeleton prototype for the synchronization process.

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.

Sequentially applied myoelectrically controlled FES in a task-oriented approach and robotic therapy for the recovery of upper limb in post-stroke patients: A randomized controlled pilot study

BACKGROUND

Functional recovery of the plegic upper limb in post-stroke patients may be enhanced by sequentially applying a myoelectrically controlled FES (MeCFES), which allows the patient to voluntarily control the muscle contraction during a functional movement, and robotic therapy which allows many repetitions of movements.

OBJECTIVE

Evaluate the efficacy of MeCFES followed by robotic therapy compared to standard care arm rehabilitation for post-stroke patients.

METHODS

Eighteen stroke subjects (onset ⩾ 3 months, age 60.1 ± 15.5) were recruited and randomized to receive an experimental combination of MeCFES during task-oriented reaching followed by robot therapy (MRG) or same intensity conventional rehabilitation care (CG) aimed at the recovery of the upper limb (20 sessions/45 minutes). Change was evaluated through Fugl-Meyer upper extremity (FMA-UE), Reaching Performance Scale and Box and Block Test.

RESULTS

The experimental treatment resulted in higher improvement on the FMA-UE compared with CG (P= 0.04), with a 10-point increase following intervention. Effect sizes were moderate in favor of the MRG group on FMA-UE, FMA-UE proximal and RPS (0.37-0.56).

CONCLUSIONS

Preliminary findings indicate that a combination of MeCFES and robotic treatment may be more effective than standard care for recovery of the plegic arm in persons > 3 months after stroke. The mix of motor learning techniques may be important for successful rehabilitation of arm function.

The design of Lil'Flo, a socially assistive robot for upper extremity motor assessment and rehabilitation in the community via telepresence

INTRODUCTION

We present Lil'Flo, a socially assistive robotic telerehabilitation system for deployment in the community. As shortages in rehabilitation professionals increase, especially in rural areas, there is a growing need to deliver care in the communities where patients live, work, learn, and play. Traditional telepresence, while useful, fails to deliver the rich interactions and data needed for motor rehabilitation and assessment.

METHODS

We designed Lil'Flo, targeted towards pediatric patients with cerebral palsy and brachial plexus injuries using results from prior usability studies. The system combines traditional telepresence and computer vision with a humanoid, who can play games with patients and guide them in a present and engaging way under the supervision of a remote clinician. We surveyed 13 rehabilitation clinicians in a virtual usability test to evaluate the system.

RESULTS

The system is more portable, extensible, and cheaper than our prior iteration, with an expressive humanoid. The virtual usability testing shows that clinicians believe Lil'Flo could be deployed in rural and elder care facilities and is more capable of remote stretching, strength building, and motor assessments than traditional video only telepresence.

CONCLUSIONS

Lil'Flo represents a novel approach to delivering rehabilitation care in the community while maintaining the clinician-patient connection.

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.

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.

Test-Retest Reliability of Kinematic Assessments for Upper Limb Robotic Rehabilitation

Robot-measured kinematic variables are increasingly used in neurorehabilitation to characterize motor recovery following stroke. However, few studies have evaluated the reliability of these kinematic variables. This study aimed at evaluating the test-retest reliability of typically-used robot-measured kinematic variables in healthy subjects (HS) and patients with stroke (SP). Sixty-one participants (40 HS, 21 SP) carried out a planar robot-pointing task on two consecutive days. Nine robot-measured kinematic variables were computed: movement time (T), mean velocity (mV), maximal velocity (MV), smoothness error (SE), number of velocity peaks (nP), mean arrest period ratio (MAPR), normalized path length (NPL), root mean square error (RMS) from a straight line and the orthogonal projection of the last point of movement (LP). Intraclass Correlation Coefficients (ICC), percentage of the Standard Error of Measurement (SEM measured as a percentage of the mean value of the variable (%SEM)) and percentage of the Minimum Detectable Difference (MDD measured as a percentage of the mean value of the variable (%MDD)) were used to analyze the test-retest reliability of the kinematic variables. ICC scores for all kinematic variables were above 0.75 in both groups. %SEM values were below 10% except for MAPR (13.4%) in HS and nP, MAPR and RMS in SP (13.0%, 11.7% and 15.2% respectively). %MDD values were higher for RMS in SP (42.1%) and MAPR in HS (37.1%) and lower for LP (1.6% in HS and 8.1% in SP). The nine robot-measured kinematic variables all demonstrated good reliability, with high ICC values (>0.75) and an acceptable level of measurement error (%SEM< 16%). However, 3/9 robot-measured kinematic variables, did not appear to be sufficiently sensitive to change (%MDD>30%) to be considered useful in patients with stroke.

A data-driven framework for selecting and validating digital health metrics: use-case in neurological sensorimotor impairments

Digital health metrics promise to advance the understanding of impaired body functions, for example in neurological disorders. However, their clinical integration is challenged by an insufficient validation of the many existing and often abstract metrics. Here, we propose a data-driven framework to select and validate a clinically relevant core set of digital health metrics extracted from a technology-aided assessment. As an exemplary use-case, the framework is applied to the Virtual Peg Insertion Test (VPIT), a technology-aided assessment of upper limb sensorimotor impairments. The framework builds on a use-case-specific pathophysiological motivation of metrics, models demographic confounds, and evaluates the most important clinimetric properties (discriminant validity, structural validity, reliability, measurement error, learning effects). Applied to 77 metrics of the VPIT collected from 120 neurologically intact and 89 affected individuals, the framework allowed selecting 10 clinically relevant core metrics. These assessed the severity of multiple sensorimotor impairments in a valid, reliable, and informative manner. These metrics provided added clinical value by detecting impairments in neurological subjects that did not show any deficits according to conventional scales, and by covering sensorimotor impairments of the arm and hand with a single assessment. The proposed framework provides a transparent, step-by-step selection procedure based on clinically relevant evidence. This creates an interesting alternative to established selection algorithms that optimize mathematical loss functions and are not always intuitive to retrace. This could help addressing the insufficient clinical integration of digital health metrics. For the VPIT, it allowed establishing validated core metrics, paving the way for their integration into neurorehabilitation trials.

Low-cost equipment for the evaluation of reach and grasp in post-stroke individuals: a pilot study

Background

Reach–grasp movements are motor components commonly affected after stroke and directly related to the independence of these individuals. Evaluations of these activities can be performed using clinical instruments and assessed by detailed and costly kinematic analyses. The aim of this study was to develop an analysis of reach–grasp movements in post-stroke patients using a simple, inexpensive, and manageable instrument.

Results

A Mann–Whitney test was used to compare paretic and non-paretic limb motor performance. A statistically significant difference was found between the variables of total time (p = 0.02) and speed to reach target 3 (p = 0.04) for task 1, while in task 2 significance was found only in the aspect of speed to reach target 2 (p = 0.04). The correlation between clinical tests and variables of tasks was then performed using Spearman’s rank correlation coefficient. At task 1, when compared with the REACH instrument, the close target sub-item; there was a high positive correlation between the parameters of total time (p = 0.028), target velocity 3 (p = 0.028), and target acceleration 3 (p = 0.028). Another instrument that showed a high positive correlation with the target time 3 (p = 0.01) and target acceleration 3 (p = 0.028) variables was the Box and Block Test. When correlated, the data between the task 2 variables and clinical instruments did not present statistically significant data.

Conclusion

Our instrument—the Temporal Data Acquisition Instrument—TDAI—fulfilled the expected objectives and can be used as an option to evaluate the movements of reach and grasp of upper limb post-stroke, using an easy and fast application, without the need for calibration.

Trial registration Trial Registration: Research Ethics Committee of the Trairi School of Health Sciences—Number 2.625.609, approved on April 13, 2018; Brazilian Registry of Clinical Trials—RBR-4995cr approved on July 4, 2019 retrospectively registered (http://www.ensaiosclinicos.gov.br/rg/RBR-4995cr/)

The assessment of trunk recovery in stroke patients using 3D kinematic measures

The kinematic analysis of trunk recovery in patients with stroke has sparsely been investigated. This study is aimed at evaluating the validity of a kinematic system for measuring trunk movements. Forty-five right-handed stroke patients in the post-acute phase were assessed in a within-subject design, before and after intensive conventional neurorehabilitation treatment. An eight-camera system was used to analyze the three-dimensional (3D) kinematics of trunk during typical displacements (anterior/posterior and right/left lateral). Kinematic trunk measurements and clinical evaluations were performed immediately in a blind fashion before and after rehabilitation treatments. Of the 9 kinematic variables, 4 showed a significant relationship with the clinical measure of the trunk: Trunk Control Test (TCT). Among these, only the kinematic evaluation of the lateral pelvic was the best predictor (R²= 0.2; p-level< 0.006; beta= 0.41) of clinical recovery measured with TCT. Here, we present a 3D kinematic system for assessing trunk impairments in stroke patients. We found that different kinematic variables reflect motor recovery as assessed by conventional clinical scale. Further evaluations, including reliability analysis and application on patients with gait impairments, are required.

Systematic Review on Kinematic Assessments of Upper Limb Movements After Stroke

Background and Purpose- Assessing upper limb movements poststroke is crucial to monitor and understand sensorimotor recovery. Kinematic assessments are expected to enable a sensitive quantification of movement quality and distinguish between restitution and compensation. The nature and practice of these assessments are highly variable and used without knowledge of their clinimetric properties. This presents a challenge when interpreting and comparing results. The purpose of this review was to summarize the state of the art regarding kinematic upper limb assessments poststroke with respect to the assessment task, measurement system, and performance metrics with their clinimetric properties. Subsequently, we aimed to provide evidence-based recommendations for future applications of upper limb kinematics in stroke recovery research. Methods- A systematic search was conducted in PubMed, Embase, CINAHL, and IEEE Xplore. Studies investigating clinimetric properties of applied metrics were assessed for risk of bias using the Consensus-Based Standards for the Selection of Health Measurement Instruments checklist. The quality of evidence for metrics was determined according to the Grading of Recommendations Assessment, Development, and Evaluation approach. Results- A total of 225 studies (N=6197) using 151 different kinematic metrics were identified and allocated to 5 task and 3 measurement system groups. Thirty studies investigated clinimetrics of 62 metrics: reliability (n=8), measurement error (n=5), convergent validity (n=22), and responsiveness (n=2). The metrics task/movement time, number of movement onsets, number of movement ends, path length ratio, peak velocity, number of velocity peaks, trunk displacement, and shoulder flexion/extension received a sufficient evaluation for one clinimetric property. Conclusions- Studies on kinematic assessments of upper limb sensorimotor function are poorly standardized and rarely investigate clinimetrics in an unbiased manner. Based on the available evidence, recommendations on the assessment task, measurement system, and performance metrics were made with the goal to increase standardization. Further high-quality studies evaluating clinimetric properties are needed to validate kinematic assessments, with the long-term goal to elucidate upper limb sensorimotor recovery poststroke. Clinical Trial Registration- URL: https://www.crd.york.ac.uk/prospero/ . Unique identifier: CRD42017064279.

Effects of virtual reality-based planar motion exercises on upper extremity function, range of motion, and health-related quality of life: a multicenter, single-blinded, randomized, controlled pilot study

Background

Virtual reality (VR)-based rehabilitation is considered a beneficial therapeutic option for stroke rehabilitation. This pilot study assessed the clinical feasibility of a newly developed VR-based planar motion exercise apparatus (Rapael Smart Board™ [SB]; Neofect Inc., Yong-in, Korea) for the upper extremities as an intervention and assessment tool.

Methods

This single-blinded, randomized, controlled trial included 26 stroke survivors. Patients were randomized to the intervention group (SB group) or control (CON) group. During one session, patients in the SB group completed 30 min of intervention using the SB and an additional 30 min of standard occupational therapy; however, those in the CON group completed the same amount of conventional occupational therapy. The primary outcome was the change in the Fugl–Meyer assessment (FMA) score, and the secondary outcomes were changes in the Wolf motor function test (WMFT) score, active range of motion (AROM) of the proximal upper extremities, modified Barthel index (MBI), and Stroke Impact Scale (SIS) score. A within-group analysis was performed using the Wilcoxon signed-rank test, and a between-group analysis was performed using a repeated measures analysis of covariance. Additionally, correlations between SB assessment data and clinical scale scores were analyzed by repeated measures correlation. Assessments were performed three times (baseline, immediately after intervention, and 1 month after intervention).

Results

All functional outcome measures (FMA, WMFT, and MBI) showed significant improvements (p < 0.05) in the SB and CON groups. AROM showed greater improvements in the SB group, especially regarding shoulder abduction and internal rotation. There was a significant effect of time × group interactions for the SIS overall score (p = 0.038). Some parameters of the SB assessment, such as the explored area ratio, mean reaching distance, and smoothness, were significantly associated with clinical upper limb functional measurements with moderate correlation coefficients.

Conclusions

The SB was available for improving upper limb function and health-related quality of life and useful for assessing upper limb ability in stroke survivors.

Trial registration

The study was registered with the clinical research information service (CRIS) (KCT0003783, registered 15 April 2019; retrospectively registered).

Kinematic Parameters for Tracking Patient Progress during Upper Limb Robot-Assisted Rehabilitation: An Observational Study on Subacute Stroke Subjects

BACKGROUND

Upper limb robot-assisted therapy (RT) provides intensive, repetitive, and task-specific treatment, and its efficacy for stroke survivors is well established in literature. Biomechanical data from robotic devices has been widely employed for patient's assessment, but rarely it has been analysed for tracking patient progress during RT. The goal of this retrospective study is to analyse built-in kinematic data registered by a planar end-effector robot for assessing the time course of motor recovery and patient's workspace exploration skills. A comparison of subjects having mild and severe motor impairment has been also conducted. For that purpose, kinematic data recorded by a planar end-effector robot have been processed for investigating how motor performance in executing point-to-point trajectories with different directions changes during RT.

METHODS

Observational retrospective study of 68 subacute stroke patients who conducted 20 daily sessions of upper limb RT with the InMotion 2.0 (Bionik Laboratories, USA): planar point-to-point reaching tasks with an "assist as needed" strategy. The following kinematic parameters (KPs) were computed for each subject and for each point-to-point trajectory executed during RT: movement accuracy, movement speed, number of peak speed, and task completion time. The Wilcoxon signed-rank tests were used with clinical outcomes. the Friedman test and post hoc Conover's test (Bonferroni's correction) were applied to KPs. A secondary data analysis has been conducted by comparing patients having different severities of motor impairment. The level of significance was set at p value < 0.05.

RESULTS

At the RT onset, the movements were less accurate and smoothed, and showed higher times of execution than those executed at the end of treatment. The analysis of the time course of KPs highlighted that RT seems to improve the motor function mainly in the first sessions of treatment: most KPs show significant intersession differences during the first 5/10 sessions. Afterwards, no further significant variations occurred. The ability to perform movements away from the body and from the hemiparetic side remains more challenging. The results obtained from the data stratification show significant differences between subjects with mild and severe motor impairment.

CONCLUSION

Significant improvements in motor performance were registered during the time course of upper limb RT in subacute stroke patients. The outcomes depend on movement direction and motor impairment and pave the way to optimize healthcare resources and to design patient-tailored rehabilitative protocols.

Validation of computerized square-drawing based evaluation of motor function in patients with stroke

Human-administered clinical scales are commonly used for quantifying motor performance and determining the course of therapy in post-stroke individuals. Computerized methods aim to improve consistency, resolution and duration of patients' evaluation. The objective of this study was to test the validity of computerized square-drawing test (DT) for assessment of shoulder and elbow function by using novel set of DT-based kinematic measures and explore their relation with Wolf Motor Function Test (WMFT) scoring. Forty-seven stroke survivors were tested before and after the rehabilitation program. DT involved drawing a square in horizontal plane using a mechanical manipulandum and a digitizing board. Depending on the initial classification of patients into low or high performance groups, the two different outcome metrics were derived from DT kinematic data for evaluation of each group. Linear regression models applied to map DT outcome values to WMFT scores for both groups resulted with high correlation coefficients and low mean absolute prediction error. In conclusion, we have identified a set of kinematic measures suitable for fast and objective motor function evaluation and functional classification, strongly correlating with WMFT score in post-stroke individuals. The results support validation of square-drawing motor function assessment, encouraging its use in clinical settings.