[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 [1, 2, 3]. Training-based therapy can promote recovery with the outcome depending on the intensity and duration of the intervention [4, 5, 6]. Robot-assisted training allows intense practice without increasing the individual’s dependence on a therapist and can improve clinical scores of UL motor capacity [7, 8, 9]. However, the effects are usually small and provide limited improvement in motor function, especially in more severe hemiparesis [6, 7, 10, 11, 12]. Identifying training methods that can boost outcome is thus vital. Considering the extent of effort and sophistication invested in robot-assisted technology (e.g. [13, 14]) 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. [16, 17]). 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 [20, 21]). 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)

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 [23, 24, 25]. 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 90^otarget 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

[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 [1, 2, 3]. Training-based therapy can promote recovery with the outcome depending on the intensity and duration of the intervention [4, 5, 6]. Robot-assisted training allows intense practice without increasing the individual’s dependence on a therapist and can improve clinical scores of UL motor capacity [7, 8, 9]. However, the effects are usually small and provide limited improvement in motor function, especially in more severe hemiparesis [6, 7, 10, 11, 12]. Identifying training methods that can boost outcome is thus vital. Considering the extent of effort and sophistication invested in robot-assisted technology (e.g. [13, 14]) 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. [16, 17]). 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 [20, 21]). 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)

[…]

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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 [1, 2, 3], 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 [4, 5, 6, 7, 8, 9].

Robotic assistive technologies can be beneficial for improving clinical scores of UL motor impairment [9, 10], by allowing intensive training [9, 11, 12, 13, 14]. 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 [16, 17, 18]). 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 [19, 20, 21]. 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 [22, 23, 24, 25], and behavioural studies support the essential underlying role of these parameters in planning of reaching movements [26, 27].

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 [19, 28] .

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 90^o 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