Posts Tagged Robot-assisted therapy

[REVIEW ARTICLE] Robot-Assisted Therapy in Upper Extremity Hemiparesis: Overview of an Evidence-Based Approach – Full Text

Robot-mediated therapy is an innovative form of rehabilitation that enables highly repetitive, intensive, adaptive, and quantifiable physical training. It has been increasingly used to restore loss of motor function, mainly in stroke survivors suffering from an upper limb paresis. Multiple studies collated in a growing number of review articles showed the positive effects on motor impairment, less clearly on functional limitations. After describing the current status of robotic therapy after upper limb paresis due to stroke, this overview addresses basic principles related to robotic therapy applied to upper limb paresis. We demonstrate how this innovation is an evidence-based approach in that it meets both the improved clinical and more fundamental knowledge-base about regaining effective motor function after stroke and the need of more objective, flexible and controlled therapeutic paradigms.

Introduction

Robot-mediated rehabilitation is an innovative exercise-based therapy using robotic devices that enable the implementation of highly repetitive, intensive, adaptive, and quantifiable physical training. Since the first clinical studies with the MIT-Manus robot (1), robotic applications have been increasingly used to restore loss of motor function, mainly in stroke survivors suffering from an upper limb paresis but also in cerebral palsy (2), multiple sclerosis (3), spinal cord injury (4), and other disease types. Thus, multiple studies suggested that robot-assisted training, integrated into a multidisciplinary program, resulted in an additional reduction of motor impairments in comparison to usual care alone in different stages of stroke recovery: namely, acute (57), subacute (18), and chronic phases after the stroke onset (911). Typically, patients engaged in the robotic therapy showed an impairment reduction of 5 points or more in the Fugl-Meyer assessment as compared to usual care. Of notice, rehabilitation studies conducted during the chronic stroke phase suggest that a 5-point differential represents the minimum clinically important difference (MCID), i.e., the magnitude of change that is necessary to produce real-world benefits for patients (12). These results were collated in multiple review articles and meta-analyses (1317). In contrast, the advantage of robotic training over usual care in terms of functional benefit is less clear, but there are recent results that suggest how best to organize training to achieve superior results in terms of both impairment and function (18). Indeed, the use of the robotic tool has allowed us the parse and study the ingredients that should form an efficacious and efficient rehabilitation program. The aim of this paper is to provide a general overview of the current state of robotic training in upper limb rehabilitation after stroke, to analyze the rationale behind its use, and to discuss our working model on how to more effectively employ robotics to promote motor recovery after stroke.

Upper Extremity Robotic Therapy: Current Status

Robotic systems used in the field of neurorehabilitation can be organized under two basic categories: exoskeleton and end-effector type robots. Exoskeleton robotic systems allow us to accurately determine the kinematic configuration of human joints, while end-effector type robots exert forces only in the most distal part of the affected limb. A growing number of commercial robotic devices have been developed employing either configuration. Examples of exoskeleton type include the Armeo®Spring, Armeo®Power, and Myomo® and of end-effector type include the InMotion™, Burt®, Kinarm™ and REAplan®. Both categories enable the implementation of intensive training and there are many other devices in different stages of development or commercialization (1920).

The last decade has seen an exponential growth in both the number of devices as well as clinical trials. The results coalesced in a set of systematic reviews, meta-analyses (1317) and guidelines such as those published by the American Heart Association and the Veterans Administration (AHA and VA) (21). There is a clear consensus that upper limb therapy using robotic devices over 30–60-min sessions, is safe despite the larger number of movement repetitions (14).

This technic is feasible and showed a high rate of eligibility; in the VA ROBOTICS (911) study, nearly two thirds of interviewed stroke survivors were enrolled in the study. As a comparison the EXCITE cohort of constraint-induced movement therapy enrolled only 6% of the screened patients participated (22). On that issue, it is relevant to notice the admission criteria of both chronic stroke studies. ROBOTICS enrolled subjects with Fugl-Meyer assessment (FMA) of 38 or lower (out of 66) while EXCITE typically enrolled subjects with an FMA of 42 or higher. Duret and colleagues demonstrated that the target population, based on motor impairments, seems to be broader in the robotic intervention which includes patients with severe motor impairments, a group that typically has not seen much benefit from usual care (23). Indeed, Duret found that more severely impaired patients benefited more from robot-assisted training and that co-factors such as age, aphasia, and neglect had no impact on the amount of repetitive movements performed and were not contraindicated. Furthermore, all patients enrolled in robotic training were satisfied with the intervention. This result is consistent with the literature (24).

The main outcome result is that robotic therapy led to significantly more improvement in impairment as compared to conventional usual care, but only slightly more on motor function of the limb segments targeted by the robotic device (16). For example, Bertani et al. (15) and Zhang et al. (17) found that robotic training was more effective in reducing motor impairment than conventional usual care therapy in patients with chronic stroke, and further meta-analyses suggested that using robotic therapy as an adjunct to conventional usual care treatment is more effective than robotic training alone (1317). Other examples of disproven beliefs: many rehabilitation professionals mistakenly expected significant increase of muscle hyperactivity and shoulder pain due to the intensive training. Most studies showed just the opposite, i.e., that intensive robotic training was associated with tone reduction as compared to the usual care groups (92526). These results are shattering the resistance to the widespread adoption of robotic therapy as a therapeutic modality post-stroke.

That said, not all is rosy. Superior changes in functional outcomes were more controversial until the very last years as most studies and reviews concluded that robotic therapy did not improve activities of daily living beyond traditional care. One first step was reached in 2015 with Mehrholz et al. (14), who found that robotic therapy can provide more functional benefits when compared to other interventions however with a quality of evidence low to very low. 2018 may have seen a decisive step in favor of robotic as the latest meta-analysis conducted by Mehrholz et al. (27) concluded that robot-assisted arm training may improve activities of daily living in the acute phase after stroke with a high quality of evidence However, the results must be interpreted with caution because of the high variability in trial designs as evidenced by the multicenter study (28) in which robotic rehabilitation using the Armeo®Spring, a non-motorized device, was compared to self-management with negative results on motor impairments and potential functional benefits in the robotic group.

The Robot Assisted Training for the Upper Limb after Stroke (RATULS) study (29) might clarify things and put everyone in agreement on the topic. Of notice, RATULS goes beyond the Veterans Administration ROBOTICS with chronic stroke or the French REM_AVC study with subacute stroke. RATULS included 770 stroke patients and covered all stroke phases, from acute to chronic, and it included a positive meaningful control in addition to usual care.[…]

 

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[ARTICLE] Boosting robot-assisted rehabilitation of stroke hemiparesis by individualized selection of upper limb movements – a pilot study – Full Text

Abstract

Background

Intensive robot-assisted training of the upper limb after stroke can reduce motor impairment, even at the chronic stage. However, the effectiveness of practice for recovery depends on the selection of the practised movements. We hypothesized that rehabilitation can be optimized by selecting the movements to be practiced based on the trainee’s performance profile.

Methods

We present a novel principle (‘steepest gradients’) for performance-based selection of movements. The principle is based on mapping motor performance across a workspace and then selecting movements located at regions of the steepest transition between better and worse performance.

To assess the benefit of this principle we compared the effect of 15 sessions of robot-assisted reaching training on upper-limb motor impairment, between two groups of people who have moderate-to-severe chronic upper-limb hemiparesis due to stroke. The test group (N = 7) received steepest gradients-based training, iteratively selected according to the steepest gradients principle with weekly remapping, whereas the control group (N = 9) received a standard “centre-out” reaching training. Training intensity was identical.

Results

Both groups showed improvement in Fugl-Meyer upper-extremity scores (the primary outcome measure). Moreover, the test group showed significantly greater improvement (twofold) compared to control. The score remained elevated, on average, for at least 4 weeks although the additional benefit of the steepest-gradients -based training diminished relative to control.

Conclusions

This study provides a proof of concept for the superior benefit of performance-based selection of practiced movements in reducing upper-limb motor impairment due to stroke. This added benefit was most evident in the short term, suggesting that performance-based steepest-gradients training may be effective in increasing the rate of initial phase of practice-based recovery; we discuss how long-term retention may also be improved.

Background

Upper-limb (UL) motor impairment is a common outcome of stroke that can severely hamper basic daily living activities [123]. Training-based therapy can promote recovery with the outcome depending on the intensity and duration of the intervention [456]. Robot-assisted training allows intense practice without increasing the individual’s dependence on a therapist and can improve clinical scores of UL motor capacity [789]. However, the effects are usually small and provide limited improvement in motor function, especially in more severe hemiparesis [67101112]. Identifying training methods that can boost outcome is thus vital. Considering the extent of effort and sophistication invested in robot-assisted technology (e.g. [1314]) perhaps it is time to focus on how to optimise its utility (in terms of training principles). Recent attempts have focussed on creating training scenarios which are more engaging or which simulate daily living activities. However, the evidence for the added benefit of this approach is mixed [15]. Another approach is to individualize the difficulty of the practised task (e.g. [1617]). This is based on the idea that motor improvement depends on the ability to ‘make sense’ of information related to performance [18], and postulates that matching the challenge (difficulty) level of the training task to the current ability of the trainee would optimise motor learning [19]. Individualizing task difficulty is commonly achieved by adjusting the parameters controlling task demands (e.g. movement speed or distance; or amount of assistance) across a pre-selected standard set of movements, to match the ability of the individual. Yet, so far there is little evidence for the added benefit of this approach for UL motor recovery. Hence, individually adjusting the task difficulty level might –by itself – not suffice for boosting UL rehabilitation outcome.

We hypothesised instead that appropriate selection of the practiced movements – in terms of the muscle coordination patterns – is a key for improving motor recovery. UL hemiparesis can affect various aspects of control. Thus, different motor impairments may benefit from different training movements. For example, training with movements involving mainly patterns of intact muscle coordination is unlikely to contribute much to improve other impaired movement patterns, regardless of the task difficulty level. Similarly, training that focuses only on movements that involve severely impaired muscle control may contribute little, even if the task can be performed by compensatory movements. Hence, to be optimally effective, individualized training may need to be expressed, not only by individually adjusting the level of difficulty of the task, but also in selecting tasks which are relevant for recovery. Little has been done to explore this possibility (for some attempts see [2021]). Here we present a novel method for performance-based selection of the set of movement tasks for robot-assisted training. The method depends on the availability of a motor performance “map” that profiles performance across a workspace. Movements are selected within intermediate levels of performance, based on the variation of performance across the map. Specifically, we predicted that optimal reduction of UL hemiparesis would be achieved by training with movements located at points on the map of steep transition (steep gradient) from high to low performance (Fig. 1), thus promoting the cascade of generalisation of motor improvement. Improved performance of movements at these steep gradient locations on the performance map would steer improvement in neighbouring, but more impaired regions, and encourage recovery. Here, we present evidence supporting this hypothesis.

Fig. 1

Fig. 1Illustrative sketch of the principle of selection of trained movements, based on the steepest gradients in a hypothetical motor performance profile (e.g. reaching aiming; vertical axis) measured across some particular task parameter (e.g. movement direction; horizontal axis); for simplicity, we show here a single dimension. The selected movements (grey horizontal bars) correspond to the regions with the steepest performance gradients, indicated by dashed ellipses. This movement selection principle can be applied where movement tasks can be defined by one or more continuous parameters, i.e. in a 1D, 2D, or higher dimensional map as long as the derivative of performance can be calculated. In this study we applied this principle on two measures of reaching performance (ability to move and ability to aim) each measured across two dimensions of the task (target location and movement direction)

To apply our method we first developed a novel principle of mapping of robot-assisted reaching performance across two dimensions of target location and movement direction [22], informing us about postural and movement-related aspects of motor control, respectively—key factors in the planning and execution of reaching movements [232425]. The performance maps then served to select movement sets for training, based on our “steepest gradients” principle. To test our hypothesis–namely, training based on that principle would lead to superior recovery–we compared the outcome of 15 sessions of robot-assisted training between two groups of people who have severe-to-moderate chronic UL hemiparesis due to stroke, differing only in the selection of trained movement. In one group the selection was based on the steepest performance gradients principle (updated weekly) whereas the other group was trained with a fixed set of centre-out reaching movements regardless of participant’s performance profile, as commonly used in robot-assisted UL therapy [26].[…]

 

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Fig. 2Experimental design. a. The sessions in each of the 3 participation phases are shown, with different colours indicating different session type. CA: clinical assessment; Map: mapping session. The first CA also served for screening. b. Schematic description of the experimental setting (top view; adapted from [32]). The participant held the robot handle, with grip ensured by a glove (Active Hands Co Ltd) and arm supported against gravity (SaeboMass, Saebo Inc.; not shown), which—at the beginning of each trial – was gently moved by the robot to a start position (white on-screen disc). Next, a target appeared on the horizontal display (blue on-screen disc; here shown black) and the participant tried to reach the target within the allotted time as accurately as possible, with the robot providing assisting and guiding forces as needed at each moment. Hand position was indicated on-screen by a red disc (not shown here). The horizontal display occluded the hand and the manipulandum from vision. Participants wore a harness to restrict trunk movement, keeping their forehead on a padded headrest attached to the workstation frame. The assistive force (Assist) promoted slower-than-allowed movements and also impeded very fast rebound-like movements characterising high elbow flexor muscle tone. The guiding force (Guide) impeded lateral deviation from a straight path towards the target. An animated ‘explosion’ was presented at the end of each trial with its final radius indicating reach accuracy (not shown). Also, during training sessions a 4-bar histogram summary, shown after each block (84 trials), informed the participant about his or her ability to initiate movements, move, aim and reach the target (adopted from [16]). c. The reaching workspace used for mapping performance. The locations of the 8 targets are indicated by small open circles and are specified by angular coordinates relative to the centre. An example of the hand located at the 90otarget is shown. Participants made 5 cm reaches to each target from 8 start locations (indicated, for the example target, by small black dots and arrows), which were also specified in angular coordinates relative to the particular target. Note that the start coordinates therefore correspond to intended movement direction. The dashed circle indicates the extent of the mapped workspace, centred 24 cm in front of the headrest

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[ARTICLE] Boosting robot-assisted rehabilitation of stroke hemiparesis by individualized selection of upper limb movements – a pilot study – Full Text

Abstract

Background

Intensive robot-assisted training of the upper limb after stroke can reduce motor impairment, even at the chronic stage. However, the effectiveness of practice for recovery depends on the selection of the practised movements. We hypothesized that rehabilitation can be optimized by selecting the movements to be practiced based on the trainee’s performance profile.

Methods

We present a novel principle (‘steepest gradients’) for performance-based selection of movements. The principle is based on mapping motor performance across a workspace and then selecting movements located at regions of the steepest transition between better and worse performance.

To assess the benefit of this principle we compared the effect of 15 sessions of robot-assisted reaching training on upper-limb motor impairment, between two groups of people who have moderate-to-severe chronic upper-limb hemiparesis due to stroke. The test group (N = 7) received steepest gradients-based training, iteratively selected according to the steepest gradients principle with weekly remapping, whereas the control group (N = 9) received a standard “centre-out” reaching training. Training intensity was identical.

Results

Both groups showed improvement in Fugl-Meyer upper-extremity scores (the primary outcome measure). Moreover, the test group showed significantly greater improvement (twofold) compared to control. The score remained elevated, on average, for at least 4 weeks although the additional benefit of the steepest-gradients -based training diminished relative to control.

Conclusions

This study provides a proof of concept for the superior benefit of performance-based selection of practiced movements in reducing upper-limb motor impairment due to stroke. This added benefit was most evident in the short term, suggesting that performance-based steepest-gradients training may be effective in increasing the rate of initial phase of practice-based recovery; we discuss how long-term retention may also be improved.

Background

Upper-limb (UL) motor impairment is a common outcome of stroke that can severely hamper basic daily living activities []. Training-based therapy can promote recovery with the outcome depending on the intensity and duration of the intervention []. Robot-assisted training allows intense practice without increasing the individual’s dependence on a therapist and can improve clinical scores of UL motor capacity []. However, the effects are usually small and provide limited improvement in motor function, especially in more severe hemiparesis []. Identifying training methods that can boost outcome is thus vital. Considering the extent of effort and sophistication invested in robot-assisted technology (e.g. []) perhaps it is time to focus on how to optimise its utility (in terms of training principles). Recent attempts have focussed on creating training scenarios which are more engaging or which simulate daily living activities. However, the evidence for the added benefit of this approach is mixed []. Another approach is to individualize the difficulty of the practised task (e.g. []). This is based on the idea that motor improvement depends on the ability to ‘make sense’ of information related to performance [], and postulates that matching the challenge (difficulty) level of the training task to the current ability of the trainee would optimise motor learning []. Individualizing task difficulty is commonly achieved by adjusting the parameters controlling task demands (e.g. movement speed or distance; or amount of assistance) across a pre-selected standard set of movements, to match the ability of the individual. Yet, so far there is little evidence for the added benefit of this approach for UL motor recovery. Hence, individually adjusting the task difficulty level might –by itself – not suffice for boosting UL rehabilitation outcome.

We hypothesised instead that appropriate selection of the practiced movements – in terms of the muscle coordination patterns – is a key for improving motor recovery. UL hemiparesis can affect various aspects of control. Thus, different motor impairments may benefit from different training movements. For example, training with movements involving mainly patterns of intact muscle coordination is unlikely to contribute much to improve other impaired movement patterns, regardless of the task difficulty level. Similarly, training that focuses only on movements that involve severely impaired muscle control may contribute little, even if the task can be performed by compensatory movements. Hence, to be optimally effective, individualized training may need to be expressed, not only by individually adjusting the level of difficulty of the task, but also in selecting tasks which are relevant for recovery. Little has been done to explore this possibility (for some attempts see []). Here we present a novel method for performance-based selection of the set of movement tasks for robot-assisted training. The method depends on the availability of a motor performance “map” that profiles performance across a workspace. Movements are selected within intermediate levels of performance, based on the variation of performance across the map. Specifically, we predicted that optimal reduction of UL hemiparesis would be achieved by training with movements located at points on the map of steep transition (steep gradient) from high to low performance (Fig. 1), thus promoting the cascade of generalisation of motor improvement. Improved performance of movements at these steep gradient locations on the performance map would steer improvement in neighbouring, but more impaired regions, and encourage recovery. Here, we present evidence supporting this hypothesis.

Fig. 1Illustrative sketch of the principle of selection of trained movements, based on the steepest gradients in a hypothetical motor performance profile (e.g. reaching aiming; vertical axis) measured across some particular task parameter (e.g. movement direction; horizontal axis); for simplicity, we show here a single dimension. The selected movements (grey horizontal bars) correspond to the regions with the steepest performance gradients, indicated by dashed ellipses. This movement selection principle can be applied where movement tasks can be defined by one or more continuous parameters, i.e. in a 1D, 2D, or higher dimensional map as long as the derivative of performance can be calculated. In this study we applied this principle on two measures of reaching performance (ability to move and ability to aim) each measured across two dimensions of the task (target location and movement direction)

[…]

Continue —> Boosting robot-assisted rehabilitation of stroke hemiparesis by individualized selection of upper limb movements – a pilot study | SpringerLink

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[Abstract] Safety, feasibility, and efficacy of transcutaneous vagus nerve stimulation combined with upper-limb robotic rehabilitation after stroke

Abstract

The efficacy of standard rehabilitation for improving upper limb functionality after stroke is limited; thus, alternative strategies are needed. Vagus nerve stimulation (VNS) combined with rehabilitation is a promising approach, but the invasiveness of this technique reduces its clinical application. Recently, a non-invasive technique for stimulating vagus nerve has been developed. Aim of this study is to evaluate safety, feasibility, and efficacy of noninvasive VNS combined with robotic rehabilitation for improving upper limb functionality in chronic stroke. We designed a proof-of-principle, double-blind, semi-randomized, sham-controlled trial. Fourteen patients with either ischemic or haemorrhagic chronic stroke were randomized to robot-assisted therapy associated with real or sham VNS, delivered for 10 consecutive working days. Efficacy was evaluated by change in upper extremity Fugl-Meyer score. After intervention, there were no adverse events and Fugl-Meyer scores were significantly better in the real group compared to the sham group. Our pilot study confirms that VNS is feasible in chronic stroke patients and can produce a slight clinical improvement in association to robotic rehabilitation. Compared to traditional stimulation, noninvasive VNS seems to be safer and more tolerable. Further studies are needed to confirm the efficacy of this innovative approach.

via Safety, feasibility, and efficacy of transcutaneous vagus nerve stimulation combined with upper-limb robotic rehabilitation after stroke – ScienceDirect

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[Abstract] Comparative hybrid effects of combining botulinum toxin A injection with bilateral robot-assisted, mirror or task-oriented therapy for upper extremity spasticity in patients with chronic stroke

Introduction/Background

Spasticity, a common impairment after stroke, has profound negative impact on outcomes in patients with stroke. Botulinum toxin type A (BoNT-A) injection combined with rehabilitation training is suggested for spasticity treatment. However, there is no recommendation about what kind of rehabilitation training is more appropriate than others following BoNT-A injection. The purpose of this study was to compare the effects of combining BoNT-A injection with bilateral robot-assisted (RT) or mirror (MT) or task-oriented (TT) therapy for upper extremity (UE) spasticity in patients with chronic stroke.

Material and method

Participants were randomly assigned to RT, or MT, or TT group after BoNT-A injection. The participants received 45 minutes of intervention per day, 3 days/week, for 8 weeks according the allocated results. In addition, all participants received 30 minutes of functional practice training. At pre-intervention, post-intervention and 3-month follow-up a blinded research assistant did outcome measures, including body function and structures by Fugl-Meyer Assessment (FMA), and Modified Ashworth Scale (MAS); activity and participation measures by Motor Activity Log (MAL), and Nottingham Extended Activities of Daily Living Scale (EADLS).

Results

Thirty-seven subjects met the inclusion criteria and underwent randomization, 13 were assigned to the RT; 12 to MT; and 12 to TT group. The 3 groups were well matched with regard to baseline characteristics and functional status. All groups had significant improvement in FMA, MAS and MAL post-intervention. There were no group differences in FMA, MAS, EADLs either post-intervention or at follow-up. There was a trend that TT group had higher quality of movement (QOM) in MAL post intervention than the other 2 groups (P = 0.07), at follow-up TT group had significantly higher QOM in MAL than the other 2 groups (P = 0.03).

Conclusion

Combining BoNT-A injection with TT resulted in better quality of UE movement in patients with spastic stroke than with RT or MT.

 

via Comparative hybrid effects of combining botulinum toxin A injection with bilateral robot-assisted, mirror or task-oriented therapy for upper extremity spasticity in patients with chronic stroke – ScienceDirect 

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[ARTICLE] Mapping upper-limb motor performance after stroke – a novel method with utility for individualized motor training – Full Text

Abstract

Background

Chronic upper limb motor impairment is a common outcome of stroke. Therapeutic training can reduce motor impairment. Recently, a growing interest in evaluating motor training provided by robotic assistive devices has emerged. Robot-assisted therapy is attractive because it provides a means of increasing practice intensity without increasing the workload of physical therapists. However, movements practised through robotic assistive devices are commonly pre-defined and fixed across individuals. More optimal training may result from individualizing the selection of the trained movements based on the individual’s impairment profile. This requires quantitative assessment of the degree of the motor impairment prior to training, in relevant movement tasks. However, standard clinical measures for profiling motor impairment after stroke are often subjective and lack precision. We have developed a novel robot-mediated method for systematic and fine-grained mapping (or profiling) of individual performance across a wide range of planar arm reaching movements. Here we describe and demonstrate this mapping method and its utilization for individualized training. We also present a novel principle for the individualized selection of training movements based on the performance maps.

Methods and Results

To demonstrate the utility of our method we present examples of 2D performance maps produced from the kinetic and kinematics data of two individuals with stroke-related upper limb hemiparesis. The maps outline distinct regions of high motor impairment. The procedure of map-based selection of training movements and the change in motor performance following training is demonstrated for one participant.

Conclusions

The performance mapping method is feasible to produce (online or offline). The 2D maps are easy to interpret and to be utilized for selecting individual performance-based training. Different performance maps can be easily compared within and between individuals, which potentially has diagnostic utility.

Background

Impaired upper-limb (UL) function is one of the most common consequences of stroke [123], which can severely hamper activities of daily living and reduce quality of life. Certain intervention methods can promote some recovery of UL motor function though their outcome shows high variability and depends on the intensity (repetition) of the intervention [456789].

Robotic assistive technologies can be beneficial for improving clinical scores of UL motor impairment [910], by allowing intensive training [911121314]. However, currently there is no consistent evidence for the effectiveness of robot-assisted UL therapy for improving daily living activity [15]. One possibility is that the tasks performed with robotic assistance do not generalise to everyday tasks. Another possibility is that the tasks are not optimised for the trained individuals. Currently, in robot-assisted therapy the set of practiced movements are usually pre-determined, with limited regard to the motor profile of the individual (e.g. ‘centre-out’ point-to-point reaches, or forearm pronation/supination, wrist extension/flexion [161718]). However, the effectiveness of training for motor recovery is likely to depend on the difficulty to perform the task due to motor impairment [19]. For example, training focused on unimpaired movements or on tasks that are either too easy or too difficult is likely to contribute relatively little to motor learning and recovery [192021]. An advantage of the robot-mediated approach is that it allows the collection of various accurate and real-time data about motor performance that would be potentially useful for individualized adjustments of the therapy; e.g. selection of training tasks based on the profile of motor performance. Yet, prescribing training conditions based on a motor performance profile requires characterising motor performance across a range of movement conditions for each individual. Here we present a novel computerised method for systematically mapping individuals’ UL motor performance (or impairment) across a wide range of robot-mediated reaching movements. The map can then serve as a basis for individualised and performance-based selection of training movements.

For optimal utilization of a motor performance map, the mapped metrics should reflect basic components of sensorimotor control, so that the map can be directly linked to processes underlying the movements (e.g. muscle activity and movement representation). Continuous metrics, allowing smoothing and interpolation from tested movements to neighbouring untested regions are also valuable. Accordingly, our mapping of reaching performance is done across the two dimensions of target location (in angular coordinates relative to a central position) and of prescribed starting location (again in angular coordinates relative to the selected target, which indicates the dictated movement direction). The range of target and start locations tests both postural and movement-related aspects of motor control, respectively. Importantly, muscle activation patterns and population neural activity in the motor-related cortices show tuning to one or both task dimensions [22232425], and behavioural studies support the essential underlying role of these parameters in planning of reaching movements [2627].

Of course, the usefulness of a motor performance map for prescribing performance-based training also depends on an appropriate principle for the selection of movements to be practiced. Here we demonstrate the utility of our mapping method for individualized task selection based on a principle which we term “steepest gradients” (SG), although the motor performance map can be the basis for alternative task selection principles. The SG principle is founded on the idea that training with tasks performed with an intermediate range of difficulty would allow more improvement and learning-induced plasticity, compared to training with very difficult or easy tasks [1928] .

Here we report the details of the mapping methods, and show its efficacy in portraying relevant motor impairment patterns for individual subjects. We also briefly demonstrate its utility for individually-tailored selection of practiced movement using the SG principle. However, our evidence for the utility and benefit of the mapping method for individualizing UL robot-mediated rehabilitation after stroke will be reported in subsequent publications.[…]

 

Continue —> Mapping upper-limb motor performance after stroke – a novel method with utility for individualized motor training | Journal of NeuroEngineering and Rehabilitation | Full Text

Fig. 1Schematic description of the experimental setting (top view). a The participant held the handle of a robotic manipulandum (indicated onscreen by a red disc; not shown), which allowed planar reaching movements from a start position (white onscreen disc (here gray) to a target position (blue onscreen disc; here black) and provided assisting and guiding forces as needed. Hand’s grip was maintained via a special glove and the forearm was supported against gravity (not shown). The participant leaned his/her head against a headrest, maintaining upright seating posture (ensured using a harness). The horizontal display occluded the hand and the manipulandum from vision. The start-to-target axis (y) and its perpendicular axis (x) correspond to the axes of the assisting and guiding forces, respectively, which were provided during the arm movement as needed by the robot. Adapted from Howard et al. (2009). b The reaching workspace used for mapping performance. The locations of the 8 targets, used in the mapping sessions, are indicated by small open circles. An example of the arm posture when the hand located at the 90o target is shown. Participants were tested with 5cm reaches to each target from 8 start locations (indicated, for the example target, by small black dots). The dashed circle indicates the extent of the mapped workspace. The drawing reflects the actual relationship of target and start locations and arm posture, based on a photograph taken with a healthy participant

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[Abstract] The Combined Effects of Adaptive Control and Virtual Reality on Robot-Assisted Fine Hand Motion Rehabilitation in Chronic Stroke Patients: A Case Study

Robot-assisted therapy is regarded as an effective and reliable method for the delivery of highly repetitive training that is needed to trigger neuroplasticity following a stroke. However, the lack of fully adaptive assist-as-needed control of the robotic devices and an inadequate immersive virtual environment that can promote active participation during training are obstacles hindering the achievement of better training results with fewer training sessions required. This study thus focuses on these research gaps by combining these 2 key components into a rehabilitation system, with special attention on the rehabilitation of fine hand motion skills. The effectiveness of the proposed system is tested by conducting clinical trials on a chronic stroke patient and verified through clinical evaluation methods by measuring the key kinematic features such as active range of motion (ROM), finger strength, and velocity. By comparing the pretraining and post-training results, the study demonstrates that the proposed method can further enhance the effectiveness of fine hand motion rehabilitation training by improving finger ROM, strength, and coordination.

Source: The Combined Effects of Adaptive Control and Virtual Reality on Robot-Assisted Fine Hand Motion Rehabilitation in Chronic Stroke Patients: A Case Study

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[REVIEW] Is robot-assisted therapy effective in upper extremity recovery in early stage stroke? —a systematic literature review

Abstract

[Purpose] The aim of this study was to systematically investigate the effects of robot-assisted therapy on the upper extremity in acute and subacute stroke patients.

[Subjects and Methods] The papers retrieved were evaluated based on the following inclusion criteria: 1) design: randomized controlled trials; 2) population: stroke patients 3) intervention: robot-assisted therapy; and 4) year of publication: May 2012 to April 2016. Databased searched were: EMBASE, PubMed and COCHRAN databases. The Physiotherapy Evidence Database (PEDro) scale was used to assess the methodological quality of the included studies. [Results] Of the 637 articles searched, six studies were included in this systematic review. The PEDro scores range from 7 to 9 points.

[Conclusion] This review confirmed that the robot-assisted therapy with three-dimensional movement and a high degree of freedom had positive effects on the recovery of upper extremity motor function in patients with early-stage stroke. We think that the robot-assisted therapy could be used to improve upper extremity function for early stage stroke patients in
clinical setting.
 

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[ARTICLE] Effect of Upper Extremity Robot-Assisted Exercise on Spasticity in Stroke Patients – Full Text

ABSTRACT

Objective: To determine the efficacy of a stretching and strengthening exercise program using an upper extremity robot, as compared with a conventional occupational therapy program for upper extremity spasticity in stroke patients.

 

Methods: Subjects were randomly divided into a robot-assisted therapy (RT) group and a conventional rehabilitation therapy (CT) group. RT group patients received RT and CT once daily for 30 minutes each, 5 days a week, for 2 weeks. RT was performed using an upper-extremity robot (Neuro-X; Apsun Inc., Seoul, Korea), and CT was administered by occupational therapists. CT group patients received CT alone twice daily for 30 minutes, 5 days a week, for 2 weeks. Modified Ashworth Scale (MAS) was used to measure the spasticity of upper extremity. Manual muscle tests (MMT), Manual Function Tests (MFT), Brunnstrom stage, and the Korean version of Modified Barthel Index (K-MBI) were used to measure the strength and function of upper extremity. All measurements were obtained before and after 2-week treatment.

 

Results: The RT and CT groups included 22 subjects each. After treatment, both groups showed significantly lower MAS scores and significant improvement in the MMT, MFT, Brunnstrom stage, and K-MBI scores. Treatment effects showed no significant differences between the two groups.

 

Conclusion: RT showed similar treatment benefits on spasticity, as compared to CT. The study results suggested that RT could be a useful method for continuous, repeatable, and relatively accurate range of motion exercise in stroke patients with spasticity.

INTRODUCTION

Spasticity is defined as a velocity-dependent increase in tonic stretch reflex, resulting from over-excitation of the stretch reflex due to upper motor neuron lesions [1]. It occurs frequently in patients with post-stroke hemiplegia. Excessive spasticity reduces patients’ range of motion (ROM) to the extent that it obstructs daily living activities and functional improvement, thereby adversely affecting successful rehabilitation.

Various treatment methods are used to control spasticity, such as exercise, drug therapy, electrostimulation, surgery, and local nerve block using botulinum toxin [2, 3, 4, 5]. Conventional rehabilitation therapy for spasticity administered by therapists includes passive stretching and ROM exercise treatment. The amount and effects of repetitive exercise manually induced by therapists may differ according to the therapists’ levels of experience [6].

In recent decades, rehabilitation treatment using a robot has been developed to reproduce accurate motions repeatedly with less input of physical effort and time by therapists. Upper extremity rehabilitation treatment using robots has been available since the 1990s and the clinical effects on upper extremity function recovery are reported.

Studies on robotic assisted rehabilitation therapy in stroke patients have shown significant improvement in motor abilities of the exercised limb and enhanced functional outcomes [7, 8, 9, 10, 11]. However, some studies indicated that when the duration and intensity of conventional treatment is matched with robotic treatment, motor recovery, activities of daily living, strength, and motor control show no group-wise differences [7]. Nevertheless, additional sessions of robotic treatment promote better motor recovery in patients with stroke, as compared with additional conventional treatment [12].

Previously, studies indicated variable treatment effects of robot-assisted rehabilitation treatment on upper extremity spasticity. Fazekas et al. [13] reported significant change in Modified Ashworth Score (MAS) of shoulder adductors and elbow flexor only in the robotic treatment group. However, it reportedly has a small, non-significant effect on muscle tone based on MAS in other studies [10, 11, 14].

The aim of the present study was to evaluate the effect of upper extremity rehabilitation robots on spasticity in stroke patients. We conducted a randomized controlled trial to evaluate upper extremity spasticity, motor power and functions in response to therapy.

 

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[Abstract] Robot-assisted post-stroke motion rehabilitation in upper extremities: a survey

Abstract

Recent neurological research indicates that the impaired motor skills of post-stroke patients can be enhanced and possibly restored through task-oriented repetitive training.

This is due to neuroplasticity – the ability of the brain to change through adulthood. Various rehabilitation processes have been developed to take advantage of neuroplasticity to retrain neural pathways and restore or improve motor skills lost as a result of stroke or spinal cord injuries (SCI).

Research in this area over the last few decades has resulted in a better understanding of the dynamics of rehabilitation in post-stroke patients and development of auxiliary devices and tools to induce repeated targeted body movements. With the growing number of stroke rehabilitation therapies, the application of robotics within the rehabilitation process has received much attention. As such, numerous mechanical and robot-assisted upper limb and hand function training devices have been proposed.

A systematic review of robotic-assisted upper extremity (UE) motion rehabilitation therapies was carried out in this study. The strengths and limitations of each method and its effectiveness in arm and hand function recovery were evaluated. The study provides a comparative analysis of the latest developments and trends in this field, and assists in identifying research gaps and potential future work.

Source: Robot-assisted post-stroke motion rehabilitation in upper extremities: a survey : International Journal on Disability and Human Development

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