Mapping upper-limb motor performance after stroke - a novel method with utility for individualized motor training
Rosenthal et al. Journal of NeuroEngineering and Rehabilitation
Mapping upper-limb motor performance after stroke - a novel method with utility for individualized motor training
Orna Rosenthal 0
Alan M. Wing 0
Jeremy L. Wyatt 2
David Punt 1
R. Chris Miall 0
0 School of Psychology, University of Birmingham , B15 2TT, Birmingham , UK
1 School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham , B15 2TT, Birmingham , UK
2 School of Computer Science, University of Birmingham , B15 2TT, Birmingham , UK
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.
Stroke; rehabilitation; Motor assessment; Robot-assisted therapy; Upper-limb movements; Reaching task
Impaired upper-limb (UL) function is one of the most
common consequences of stroke [
], 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 [
Robotic assistive technologies can be beneficial for
improving clinical scores of UL motor impairment [
allowing intensive training [
]. However, currently
there is no consistent evidence for the effectiveness of
robotassisted UL therapy for improving daily living activity .
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
]). However, the effectiveness of
training for motor recovery is likely to depend on the difficulty to
perform the task due to motor impairment [
]. 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 [
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 [
], and behavioural
studies support the essential underlying role of these parameters
in planning of reaching movements [
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
learninginduced plasticity, compared to training with very difficult or
easy tasks [
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.
Ethics, consent and permissions
In this report we demonstrate examples of the principle
and utility of the novel performance and impairment
mapping method using the data of two adults with UL
hemiparesis due to a stroke (in the chronic stage), who
participated in an on-going study. This study was approved by
the Science, Technology, Engineering and Mathematics
Ethical Review Committee of the University of Birmingham
(ERN_09-528). Prior to their participation, participants
received detailed information about the study and provided
Procedure and Task
The method of performance mapping presented here is
applied to a robot-assisted reaching task, although the
mapping principles do not depend on a specific
robotassisted algorithm and could be used with other forms
of motor performance data.
The participants took part in multiple sessions of
robot-assisted start-to-target reaching exercises where
different sessions served different purposes. Both
participants first completed parameter tuning and performance
mapping sessions (see below). Participant 2 also took
part in 15 training sessions, which we report to
demonstrate our method of performance-based training.
During each of the task sessions the participants held a
robotic manipulandum (vBot[
]) with the arm supported
against gravity using a SaeboMass device
(http://www.saebo.com/). At the beginning of each trial, the robot gently
moved the participant’s hand towards a start position and
maintained the hand there until a blue target appeared on
the display. The vBot then released hold of the hand and
the participant was asked to reach the target, as accurately
as possible, within an individually-set allotted time (Fig. 1a).
Then the target disappeared and an animated explosion
feedback (not shown) informed the proximity of the final
hand position to the target (see the additional file for full
task details [Additional file 1, ‘Task and settings’]). Assistive
and guiding forces were provided by the vBot during the
reach, as needed (see below and the additional file
[Additional file 1, ‘Robot assistance algorithm’]).
During each trial the vBot provided assistance and guidance
as needed, employing a revised version of the robot-assist
algorithm that has been developed for MIT-MANUS[
that has been reported to improve clinical scores for UL
motor performance [
]. Since the focus of this report is
on the mapping procedure, we provide only a summary of
the robot-assistance algorithm. Briefly, throughout each trial,
assisting (Assist) forces were provided in the direction of the
start-target axis and aimed to promote smooth forward
movement within the allotted time and/or to impede
abnormally fast rebound movements due to high muscle
tone or impaired motor control (see Fig. 3b and the
additional file [Additional file 1, ‘Robot assistance
algorithm’]). Guiding (Guide) forces were provided
perpendicularly to the start-target axis and acted to oppose deviation
from the intended movement direction.
The allotted movement time governing the Assist forces
and the stiffness parameters governing the Guide force
were individually set in an initial parameter tuning
session, but were then maintained at fixed levels
throughout the rest of the protocol. For the tuning session,
subsets of the test movements were presented in short
blocks to allow staircase-wise parameter adjustment,
based on the participant’s performance. Usually, tuning
of the assisted parameters approached a plateau within a
single session. In rare cases there was a need of an extra
tuning session. For further details of this tuning, see the
additional file [Additional file 1, ‘Robot-assistance
parameter tuning session’].
Performance mapping principle
To create a performance (or impairment) map,
participants were tested with 5cm planar start-to-target reaches
in different directions and locations. Eight possible
targets were located a fixed 5cm distance from a pre-set
central point, and each target could be approached from
eight different start locations (Figs 1b and 2a). Note that
a start location defined in angular coordinates relative to
the target also corresponds to the intended reach
direction. Using fixed movement displacement and fixed
radius of target distance allows the creation of a 2D map
for any scalar metric of motor performance or
impairment, by specifying each movement condition in terms
of the angular coordinates of the target location and the
relative start location (Fig. 2b). Each movement from a
start location to a target location becomes a single point
on the 2D map. In principle, other aspects of movement
could be added to create 3- or high-dimensional maps.
For example, distance could be a third variable.
However, we restrict our work to two dimensions, movement
direction and target location.
The performance mapping procedure usually took a
single session of less than one hour to complete. During
a mapping session, motor performance was assessed
across all 64 reaching movement conditions, defined by
all combinations of 8 target locations and 8 relative start
locations (Fig. 2a). The targets were located equidistant
on an imaginary circle of 5cm radius centred 24 cm in
front of the headrest and specified by their angular
coordinates. For each target, the 8 possible start locations
were arranged equidistant on an imaginary circle of 5
cm radius, centred on the particular target (Figs. 1b, 2a).
In each performance mapping session, the 64 conditions
were repeated 5 times in a pseudo-random order (320
trials in total).
Because the robot assistance applied during reaching
confounds many of the usual kinematic measures of
motor performance (e.g. reach errors, movement time,
peak velocity) we chose to map the movement
impairment principally in terms of the levels of assisting and
guiding forces that were provided. Specifically, the Assist
parameter is defined as the root-mean-square force (Fy),
which was provided during the attempt to move along
the start-to-target axis (y):
Assist ¼ sign Fy
N P FyðiÞ2 , where
>>> −1; XFyðiÞ<0
sign Fy ¼ >< i¼N1
>> 1; XFyðiÞ≥0
(i=1..N indicates the time points during the allotted
The Guide parameter is defined as the
root-meansquare of the guiding force (Fx) provided normal to the
start-target axis (x):
Guide ¼ −qffi1ffiffiffiffiffiffiffiffiNffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi2ffiffi
N Pi¼1 F xðiÞ .
Negative values mean that the force direction opposed
the hand’s movement direction. This was always the case
for the Guide parameter, as it always opposed lateral
deviation. Positive values of the Assist parameter indicate that
the force was provided in the direction of the hand
movement towards the target, while negative Assist values mean
that the force impeded abnormally very fast movements.
Separate 2D impairment maps were created for the Assist
and Guide data, collected across all the 64 conditions. The
raw data from all 320 trials was interpolated using a
Gaussian process regression toolbox (www.GaussianProcess.org/
gpml; version 3.1 for Matlab) to create a higher resolution
and smoother map (with 32 x 32, or 1024 locations). Note
that both the target and relative start locations were specified
in angular coordinates (θ and σ, respectively). To allow
Gaussian process regression and interpolation across the full
angular range, these coordinates were transformed to a pair
of sine and cosine coordinates, creating a 4-dimensional
space (i.e., [ϑ, σ] → [sinϑ, cosϑ, sinσ, cosσ]). The interpolated
4D data were then transformed back to be graphically
presented in original 2D angular coordinates (Fig. 2b). Finally,
note that, for cross-participant comparison, the spatial
coordinates of participants with left arm motor impairment
should be mirror-flipped before the mapping.
Performance-based selection of training sets
Once a motor performance/impairment map has been
created for a participant, it can be used for prescribing
performance-based training tasks. Here we apply a
“steepest gradients” (SG) principle for selection of training
tasks that is based on performance gradients across the 2D
map (Fig. 5a, b). This principle is based on our suggestion
that training with movements represented on the
performance map at regions of transition (steep gradient) from low
to high difficulty levels would be most beneficial .
The SG principle for selection of movement conditions
could be based on the gradients across many kinds of
performance map. However, the Assist and Guide parameters
are unsuitable for gradient-based analysis because they are
not sensitive to unimpaired motor performance, i.e. when
no assistance is provided and hence these parameters are
limited to zero. Hence, for movement selection we mapped
performance based on two performance measures, PM2
and PM3, which have been in used in other studies on
robot-assisted rehabilitation [
], as they provide
unbounded measures of motor performance. Briefly, PM2 and
PM3 measure the ability to move and aim, relative to their
expected performance criteria, which are set individually in
the initial tuning session (see Additional file 1,
‘Robot-assistance parameter tuning session’). Positive and negative PM
values indicate performance better and worse than
criterion, respectively. Individually set performance criteria are,
for PM2, the allotted movement time, and for PM3, the
tolerated deviation value. For further details see the additional
file [Additional file 1, ‘PM2 and PM3 parameters’]. The
smoothed and interpolated PM maps were created using
the same procedure used for the Assist and Guide
impairment maps. While PM2 and PM3 provide continuous
metrics suitable for the SG-based selection procedure, they are
not directly comparable across individuals; PM3 is also a
kinematic measure that can be confounded by the robot
assistance. Hence we use the PM maps for SG selection, but
use the Assist and Guide force maps to compare motor
impairment (for further explanation see Additional file 1,
‘PM2 and PM3 maps’).
To apply the SG principle to select task conditions, 2D
gradients were computed across each PM map using the
Matlab function ‘gradient’ and a subset of 102 of the total
of 1024 conditions per map was selected, corresponding
to map locations with the top 10% of its steepest
gradients. From each of these two sets of 102 training
conditions, trials were pseudo-randomly selected. Finally, the
number of training conditions selected from each map
(PM2 or PM3) was weighted in proportion to the mean
performance over the worst 25% of each map, such that
more training conditions were selected based on the map
that showed worse motor performance [Additional file 1,
‘Impairment-based proportion of movement selection’]. In
principle, very good or bad performance might produce a
flat map, without gradient. In practice we only
encountered this for unimpaired participants, and then selected
movements at random; we do not think it an issue for use
even with mildly impaired Individuals.
Performance-based training sessions
All participants attended an initial robot parameter tuning
session, followed by a first mapping session. For
participant 2, these initial sessions were followed by a 5-week
period of training with 3 training sessions per week, using
the performance-based selection of movement conditions.
At the end of each training week, participant 2 completed
an additional testing session and updated PM2 and PM3
maps were created and served for re-selecting movements
for the following week, again based on the SG principle.
Hence the training selection varied week-by-week, as the
maps were updated. A final post-training mapping session
served for evaluating training outcome.
Results (examples of utilizing the mapping method)
The main aim of this report is to present the principles and
methods of mapping UL motor performance and of
selection of training conditions based on such maps. Therefore,
at this stage we only present here examples of maps of two
participants from our on-going study, to demonstrate their
utility. The results of our full randomized and controlled
study and the assessment of the benefit of the steepest
gradients training principle will be detailed elsewhere.
Figure 3a presents an example of the Assist and Guide
maps from an individual with severe right arm hemiparesis
due to a left hemisphere stroke 4 years previously
(participant 1; UL Fugl-Meyer (F-M) score 11/66), who’s elbow
flexor muscles show high tone (Modified Ashworth Scale
score 2). The Assist map clearly illustrates two regions of
high motor impairment, with opposite polarities. The small
white and black squares mark two movement examples,
near the centres of the two high-impairment regions. In
both movements, the target coordinate is 90o, indicating
this participant’s difficulty to reach far targets located in the
body midline (see Fig. 1b). The starting direction of the two
examples are 90o (white square) and 270o (black square),
indicating inward and outward movements along the body
midline; Fig. 4 indicates the arm posture at the start of
these two movements.
The positive red region of the Assist map reflects very
slow outward movements that required considerable
assistance (~6N) towards the target. An example from one
trial, for the movement marked by small black square in
Fig. 2a, is shown in Fig. 3b and c, on the right. Movement
velocity is shown in Fig. 3c. Notice the very slow speed,
relative to the expected by minimum jerk speed profile.
The negative, blue, region of the Assist map reflects
abnormally fast, rebound-like inward movements, which
required strong robotic restraining forces to dampen the
speed and to oppose target over-shooting. The left graphs
of Fig. 3b and c present the trajectory and speed for an
example from one trial, for the movement marked by
small white square in Fig. 3a. Note the very fast initial
speed (Fig. 3c), just after the vBot released its hold on
participant’s hand at the distal start location. This
participant’s difficulty in progressing towards the distal target
and in controlling the rebound movement in the opposite
direction reflects the high tonus of the elbow flexors.
The Guide map in Fig. 3a (right) also shows two regions
of high motor impairment. As in the Assist map the two
impairment regions are horizontally aligned on the map,
indicated the same range of target locations (the vertical axis),
and are again centred approximately 180o apart of each
other in movement direction (horizontal axis). Yet, the
directional tuning of these regimes is somewhat shifted
Fig. 4 Example of arm postures at start location. Side view
illustrations of the arm posture at the start location of the two
reaching movements shown in Fig. 3. Both reaches are to same
target location (90o; see inset). The start conditions are labelled by a
small white and black square (see also Fig. 3). The arrows indicate
the required movement direction. The upper start posture (‘start 1’;
White Square), which corresponds to the left example in Fig. 2b and
c, involves greater elbow extension. Thus, in the case of high elbow
extensor tone (as for the participant in Fig. 3), holding the hand at
that start position would lead to tension directed towards the target
and to a fast rebound-like movement (in this example towards the
target), after the release of the robotic hold. The bottom posture
(‘start 2’; black square) corresponds to the right example in Fig. 3b
and c. Here the reaching movement requires elbow extension.
Hence, high elbow extensor muscle tone and weak elbow flexors
would impede the movement. The illustrations are based on
photographs of a healthy representative participant
relative to the directional tuning of the impairment regimes
in the Assist map: the white and black squares lie between
the regions of high Guide forces
Figure 5b presents impairment maps of another
chronic stroke participant (participant 2; right
hemiparesis, 1 year after a stroke of the left hemisphere; UL F-M
score 12/66), taken before and after 15 training sessions.
Prior to training (i.e. at a baseline session), both Assist and
Guide maps show a double-blob pattern (Fig. 5b top row;
note that the two separate red areas in the Assist map
actually belong to a single region, given the angular map
coordinates), similar to that of Participant 1 (Fig. 3a). However, the
baseline maps of Participant 2 differ in the orientation and
locations of the high impairment regions. For example, in
the Assist map of Participant 2, the positive, red, impairment
region includes target locations that are closer to the body
(at the top half of the map, >180o), which are much less
prominent for Participant 1. In addition, for Participant 2
the positive region includes movements which show
impairment of internal rotation (starting directions between
0o90o), and less impairment of external rotation movements
(starting directions between 180o-270o); Participant 1
showed the opposite trends. Moreover, unlike the vertical
nature of the regions in the Assist map of Participant 1,
which reflects a relatively consistent range of impaired
movement directions across a wide range of (distal) target
locations, the positive impairment region of Participant 2
shows a diagonal pattern, indicating impairment in
movement direction that co-varies with target location. Finally,
for Participant 2, the positive red region (indicting slowness
in moving) dominates in extent and magnitude. For
Participant 1 the negative, blue, regions that indicate abnormally
fast movements, dominate (with peak resisting forces ≥8N).
Utilizing the map for individualized training
Apart from quantifying motor performance, we are using
the performance maps to systematically and individually
select movement conditions for training. Here we
demonstrate their use in applying the SG principle of
individualized selection for Participant 2.
Figure 5a presents the baseline (pre-training) PM2 and
PM3 performance maps (see the Methods section and the
additional file [Additional file 1, ‘PM2 and PM3 maps’] for
justification for using PM rather than force maps).
Movement conditions that were selected for the first week of
training are indicated on these maps by small black ‘x’ and
white ‘+’ symbols, respectively. The PM2 and PM3 map
patterns correspond closely to the baseline Assist and
Guide force maps, respectively, (Fig. 5b, top row; note that
positive red values in PM maps indicate better than
expected motor performance and negative blue values
indicate motor impairment; thus the polarity of the PM2 map
is opposite to the Assist map). Indeed, the selected
conditions are located at regions of steep transitions from
higher to lower performance, regardless of the mapped
parameters (PM2 or Assist, PM3 or Guide). Note also that
while the regions differ between the PM2 and PM3 maps
(in a similar fashion to the differences between the Assist
vs. Guide maps) this difference between the two PM maps
leads to different conditions being selected for training
(compare the locations of the black ‘x’ vs. white ’+’
symbols in Fig. 5b). We return to this in the discussion.
Rosenthal et al. Journal of NeuroEngineering and Rehabilitation (2017) 14:127
Fig. 5 (See legend on next page.)
(See figure on previous page.)
Fig. 5 Impairment-based training. a Examples of selected reaching training conditions for Participant 2 (allotted movement time: 1.1 sec; guiding
stiffness: 2.0 N cm-1). The conditions for the first week of training were selected based on the PM2 and PM3 maps of the baseline (pre-training)
mapping session. The selected conditions (small black ‘x’ and white ’+’ symbols, superimposed in the PM2 and PM3 maps, respectively) are within
regions of 10% steepest map gradients. The selected conditions of both maps were used in the actual training. b The effect 5 weeks of training
(3 session per week) on Assist and Guide force maps of participants 2 (left) and 3 (right). The Assist and Guide maps at the baseline session are
shown at the upper row, with the selected conditions, corresponding to panel a. The lower row presents the corresponding post-training maps.
c Learning maps – the difference between baseline and post-training sessions per the Assist and Guide maps of each participant. Improvement is depicted
as negative values. The selected training conditions (PM2-based and PM3-based combined) in all the training sessions are superimposed on the maps
(small black ‘x’ and white ‘+’ symbols, respectively). The sets of training conditions were re-selected for each week based on the impairment map of the
Comparing the baseline versus post-training Assist and
Guide maps in Fig. 5b (top vs. bottom maps, respectively)
reveals the effect of the 15 training sessions on Participant
2’s reaching performance. Overall, the magnitude of
assistance and guidance by the robot decreased, indicating
improved performance. This is evident visually as shrinkage
of the impairment regions in each of the maps. It is also
evident quantitatively, with a decrease in mean impairment
levels for each map, accompanied by a decrease in the
standard deviation across the map, indicating flatter maps
post-training (Table 1). Figure 5c summarises the pattern of
change from baseline to post-training. As can be seen, the
major improvement (blue areas, reduced forces) occurred
in or near the regions selected for training. Still, there was
some limited increase in Guide force, mostly around targets
located at 90o for movement directions between 270o-330o
(i.e. reaching diagonally left, toward distal targets across the
body midline). This need for increased guidance was
balanced by some reduction in the Assist force (indicated
improved speed) in that region. This might suggest a
speed-accuracy trade-off, pointing to a need for some
future adjustments to the SG principle. However, this is
beyond the scope of this report.
We have presented a novel method for the systematic
mapping of motor performance and/or impairment of planar
reaching movements across a workspace. We demonstrated
the use of the method for identifying regions of movement
impairment, in individuals with upper limb hemiparesis
due to a stroke, and how it can be applied as a basis for
performance-based selection of training movements for
rehabilitation. The advantages of the performance mapping
The values represent the reduction in the mean force levels (i.e. improvement)
across the Assist and Guide maps (before smoothing interpolation), recorded
after 15 sessions of training, compared to baseline (Participant 2 only); the
reduction in standard deviation of forces across the maps reflects a flattening
of the performance maps
principle are that (1) mapping spans a wide region of the
reaching movement conditions, and is fine-grained,
potentially allowing high sensitivity to patterns of motor
impairment, which may be overlooked by coarser clinical
diagnostic scores (2) it is reasonably quick and easy to
produce and to interpret, and (3) the maps’ coordinates
(movement and target directions) relate to established basic
elements of movement coding [
]. The mapping
principle can be easily applied to any quantifiable scalar
measures of reaching performance – behavioural metrics
(e.g. kinetics and kinematics), physiological metrics (e.g.
EMG; not demonstrated here), or impairment metrics
(amount of assistance provided et cetera). Thus, that
principle allows direct comparisons between maps of
different modalities, for example EMG and kinetics. Here we
demonstrated the mapping of arm movement impairment
for individuals who had stroke. However, the method is
likely to be beneficial also for profiling upper limb motor
impairment as a result of other conditions including
multiple sclerosis, cerebral palsy, or trauma.
The 2D mapping of motor performance is conducted
across movement conditions defined by intended
direction and target location, allowing mapping of a range of
movements. The mapped movement conditions were
confined to fixed start-to-target displacements and to targets
that were located equidistant from a pre-defined centre
location. For feasibility of testing and for ease of
interpretation, the proposed method is limited to map reaching
movements across a single 2D horizontal plane. These are
typical of the training tasks used in end-effector
robotmediated UL therapy (e.g. [
]). Mapping motor
performance of reaching movements across a full 3D range of
movement, and across different target locations is possible
in principle, but testing motor performance across the full
3D volume would be time consuming in a rehabilitation
setting. Yet, if needed and if time allowed, multiple planar
2D maps could be prepared to approximate a full 3D map.
There have been few other attempts to develop
systematic and fine profiling of arm motor impairment across a
range of movement conditions, based on quantifiable
motor performance measures. An early attempt by Kamper
et al. [
] to map kinematic parameters of frontal reaches
across a range of target locations and movement directions
showed limited success in revealing individual patterns of
impairment variation across the workspace. The success of
our mapping method in revealing clear patterns of
impairment suggests that the lower sensitivity of Kamper et al’s
method might be related to the limited sensitivity of the
performance measurements, perhaps due to the frontal
planar arrangement of the targets and the lack of
antigravity arm support, likely leading performance to be
dominated by the challenge of opposing gravity [
performance variability due to loose constraints on the
movement trajectory may be another contributor to the
limited sensitivity of their approach. Recently, a preliminary
–but more sophisticated - approach for mapping
performance distribution has been suggested [
], though is not yet
utilizable as a tool for assessment of motor impairment.
Our mapping method is simple to use. This was
demonstrated here in the maps of two participants (Figs. 3a and
5b), showing simple and clear impairment profiles, where
impaired movements were concentrated in two map
subregions. At the same time, the mapping exposed individual
variation in the extent, location and orientation of the high
impairment regions, emphasizing the potential importance
of individualizing the selection of training conditions. Note,
though, that the optimal exploitation of the performance
map data for individualized therapy still needs to be
demonstrated. We will present the results of a randomised
and controlled study, comparing the steepest
gradientbased selection of trained movements with more common
“centre-out” training in one of our next publications.
Besides its potential utility as a basis for individualized
UL therapy, the mapping method may contribute an added
diagnostic utility. For example, mapping motor impairment
across larger populations of people with stroke may allow
the classification of their motor impairment, based on
similarities in their maps’ patterns. Finding a few canonical
motor impairment characteristics could then reduce the
time spent on the full profiling of individuals’ impairment.
In other words, a smaller number of probe locations might
serve to discriminate and classify performance.
Another potential benefit of the mapping method is that
it allows easy comparisons between maps of different
impairment measures within and even between subjects. Such
comparisons may potentially provide insightful information
about causal relationships between different motor
impairment factors. For example, high correlation between
movement impairment maps and EMG maps (e.g. mapping
levels of activity in the major arm muscles during the
initiation of each reaching movement) may highlight the
potential underlying roles of different muscles in different
Some further improvements may be possible to optimize
the principle of map-based selection of practiced
movements. For instance, it is currently not clear whether
combining selections based on both performance maps (as we
have done, in our case selecting from PM2 and PM3 in
proportion to the performance deficit each map showed) would
be more beneficial than focusing on the one map that
showed more impairment. Answering this requires further
study, which we hope to take in the future. In addition, we
have restricted the workspace to two dimensions, and the
area, while covering much of the space involved in everyday
hand action [
], is by necessity small compared to the full
range of upper limb movement. However, as we will report
in a subsequent paper, there is evidence of generalization of
the training in these robotic environments to everyday
actions, with improvement in clinical rating scores.
Theoretically, our mapping principle does not necessitate
the use of a robot manipulandum and other movement
measurement systems might be more affordably utilized to
assess motor impairment, as long as they can provide
quantifiable measures of motor performance. However, we do
believe that robot-assisted forces are likely to provide sensitive
measures of motor impairment – especially when
impairment is more severe. Without the robotic assistance, motor
impairment above some critical functional limit would block
the participant’s ability to move, leading to a ‘floor effect’ in
the measured performance, and so jeopardise the sensitive
selection and quantification of training. This limitation
should be considered when selecting the metrics of motor
performance or impairment to be mapped.
Our novel computerized mapping method is a feasible
and simple approach for profiling upper limb performance
across a wide movement workspace. It outlines regions of
motor impairment in a clear way, allowing comparisons of
impairment patterns within individuals, and between
groups and can allow comparison of different motor
impairment/performance measures. The performance maps
can be utilized as a basis for individually-tailored therapy.
Specifically, our mapping method allows for selection of
training movement conditions that can be updated as
rehabilitation progresses, dynamically tracking the changing
performance of each participant. Comparison of
individualized versus standardized training regimes is underway –
but this is beyond the scope of the current report.
Additional file 1: Methods - supplemental details (text and a figure).
This file contains further details about the robot-assisted reaching task,
the robot assistance algorithm, robot-assistance parameter tuning, the
definition of PM2 and PM3 parameters and their maps. (PDF 529 kb)
Assist: parameter measuring the overall amount of robotic force provided
along the start-to-target axis during the full trial; ‘Assist force’ refers to the
force provided at each moment along that axis.; F-M: Fugl Meyer clinical
assessment of motor impairment; Guide: parameter measuring the overall
amount of robotic force provided normal to the start-target axis.; ‘Guide
force’ refers to the force provided at each moment along that axis.; PM2 &
PM3: Performance measure parameters, adopted from [
], measuring the
ability to move and aim, respectively (see Additional file for further details
[Additional file 1, ‘PM2 and PM3 parameters’]).; SG: Steepest gradients;
UL: Upper limb; vBot: robotic manipulandum used in the study
We thank Dr. Chit Ko-Ko for clinical-related suggestions and advice, Briony
Brownless for her help with data collection, and Jonathan Winter for his
technical expertise. We also thank Ian Howard and colleagues for permission
to adapt Fig. 1a from their paper in J. Neurosci. Methods (2009) 199-211.
This work was directly supported by the MRC (grant MR/J012610/1 and MRC
CiC grant MC_PC_15032) and by Wellcome Trust (grant WT087554 and WT
FoF grant 105611-Z-14-Z).
Availability of data and materials
All data are available upon request to the corresponding author, at the
OR and RCM conceived the study. AMW and JW contributed to the project plan.
OR designed the experiment, programmed the robot-assisted procedure and
analysis and analyzed the data. DP conducted the clinical assessments. OR, RCM,
AMW and JW interpreted the results. OR and RCM wrote the manuscript. All
authors commented on the draft manuscript and approved the final manuscript.
Ethics approval and consent to participate
This study was approved by the Science, Technology, Engineering and
Mathematics Ethical Review Committee of the University of Birmingham
(ERN_09-528). Prior to their participation, participants received detailed
information about the study and provided written consent.
Consent for publication
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
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