A Novel Robotic Task for Assessing Impairments in Bimanual Coordination Post-Stroke

Abstract

Copyright: © 2014 Lowrey CR, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Full text

International Journal of
Physical Medicine & Rehabilitation
Lowrey et al., Int J Phys Med Rehabil 2014, S3
http://dx.doi.org/10.4172/2329-9096.S3-002
Research Article Open Access
Int J Phys Med Rehabil ISSN: 2329-9096 JPMR, an open access journal
Stroke rehabilitation
A Novel Robotic Task for Assessing Impairments in Bimanual
Coordination Post-Stroke
Catherine R Lowrey1*, Carl PT Jackson1, Stephen D Bagg3,4, Sean P Dukelow5,6 and Stephen H Scott1,2,4
1Centre for Neuroscience Studies, Queen’s University, Kingston, Ontario, Canada
2Department of Anatomy and Cell Biology, Queen’s University, Kingston, Ontario, Canada
3Department of Physical Medicine and Rehabilitation, Queen’s University, Kingston, Ontario, Canada
4School of Medicine, Queen’s University, Kingston, Ontario, Canada
5Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
6Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
*Corresponding author: Catherine R Lowrey, Center for Neuroscience Studies,
Botterell Hall, 18 Stuart St., Kingston, Ontario, Canada, K7L 2V4, Tel: 613 533-
6000; E-mail: lowrey@queensu.ca
Received January 20, 2014; Accepted February 10, 2014; Published February
14, 2014
Citation: Lowrey CR, Jackson CPT, Bagg SD, Dukelow SP, Scott SH (2014) A
Novel Robotic Task for Assessing Impairments in Bimanual Coordination Post-
Stroke. Int J Phys Med Rehabil S3: 002. doi:10.4172/2329-9096.S3-002
Copyright: © 2014 Lowrey CR, et al. This is an open-access article distributed
under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the
original author and source are credited.
Keywords: Stroke; Bimanual; Robotics; Assessment
Introduction
Stroke survivors exhibit a wide range of sensory and motor
impairments. ese impairments result in disabilities that limit
performance of daily activities [1]. While rehabilitation is important to
regain function aer stroke, assessment of the initial impairment is an
essential rst step. Accurate assessment of the nature and magnitude of
impairment is benecial both on an individual level, to determine the
appropriate course of treatment, and also on a broader scale to guide
the development of novel rehabilitation approaches [2].
e majority of assessment tools used for the upper limb
examine single limb function, evaluating task performance in one
limb or the other [3-7]. ese tools have provided valuable insight
into sensorimotor impairments following stroke, oen categorizing
the performance of the aected limb and, in some tasks, contrasting
aected with unaected limb performance [5]. However, we oen
need to use both limbs in a coordinated manner to perform tasks (i.e.,
opening a jar or balancing items on a tray held with both hands). In
a survey of post-stroke individuals, 90% of patient-selected recovery
goals included dressing, washing and eating/drinking, and over one-
third of these tasks involved the use of both hands [8]. Moreover, in two
recent studies that tracked limb use in daily life post-stroke, it was found
that the aected limb was used almost exclusively in bimanual tasks
(vs. unimanual) [9] and increased bimanual use was associated with
better performance on instrumental activities of daily living [10]. ese
ndings emphasize the need for current assessment and rehabilitation
protocols to consider bimanual movements.
Many rehabilitation strategies include a bimanual component, in
part to improve bimanual function, but also with the hope that the
intact neural circuitry in the contralesional (unaected) hemisphere
will improve neural function in the ipsilesional hemisphere [11,12].
However, the ecacy of bimanual therapy is controversial [13]. In the
short term, some studies have found that bimanual movements do
not improve the movement of the aected limb, and at times actually
decrease performance in the unaected limb [14-16]. Although motor
performance on the specic bimanual training task itself may improve
[17] this improvement does not always extend to performance of
functional bimanual tasks [18,19]. ese divergent results necessitate
an improved understanding of bimanual impairments following stroke,
starting with accurate assessment of bimanual control.
Previous evaluations of bimanual impairments post-stroke have
focused on the ability of the limbs to move with similar metrics towards
relatively independent goals. For example, tasks include reaching
Abstract
Background: Bimanual tasks are integral to the performance of many activities of daily living, but impairments in
bimanual coordination following stroke are not well quantied with existing clinical tools.
Objective: The current study outlines a novel robotic task for the objective and quantitative assessment of
bimanual impairment following stroke.
Methods: We developed a robotic, bimanual assessment task using the KINARM robot. The task involved moving
a virtual ball on a bar linking the two hands, to targets displayed using a virtual reality system. Seventy-ve healthy
control participants and 23 participants with sub-acute stroke were assessed using the task. Task performance of
participants with stroke was compared with the healthy control group, as well as to standard clinical tests (Chedoke-
McMaster Stroke Assessment (CMSA) arm and hand, Functional Independence Measure (FIM), Montreal Cognitive
Assessment (MoCA) and Behavioural Inattention Test (BIT)).
Results: A range of impairments in bimanual task performance was found for participants with stroke. As a group,
85% of participants with stroke had impairments on more task parameters than 95% of healthy controls. Participants
with stroke commonly displayed impairments in task success (fewer targets hit); movement metrics (slower movement
speed) and bimanual coordination (larger difference in reaction time between hands, greater number of speed peaks
with unaffected versus affected limb and greater absolute tilt of the bar). Overall performance of the robotic task (total
number of parameters ‘failed’) was signicantly correlated with motor performance scores (CMSA, r=-0.6) and strongly
correlated with measures of functional ability (FIM motor, r=-0.8).
Conclusions: A robotic bimanual task can identify impairments in a population of stroke participants and provides
a quantitative measure of bimanual coordination.

Citation: Lowrey CR, Jackson CPT, Bagg SD, Dukelow SP, Scott SH (2014) A Novel Robotic Task for Assessing Impairments in Bimanual Coordination
Post-Stroke. Int J Phys Med Rehabil S3: 002. doi:10.4172/2329-9096.S3-002
Page 2 of 10
Int J Phys Med Rehabil ISSN: 2329-9096 JPMR, an open access journal
Stroke rehabilitation
both arms towards single or dual targets [15,16,20,21], symmetric
force production with both arms [22] or simultaneously drawing
concentric circles [19]. While these tasks allow a measurement of
synchrony or symmetry in the behaviour of each hand, they lack the
added complexity of coordination of the two hands towards a common
goal. In many functional tasks, the limbs are required to work together,
including opposing a motion applied by one limb with the other. A few
studies have quantied impairments on more coordinated, functional
tasks such as pouring/drinking from a glass or opening a jar [18,23].
However the unconstrained nature of these tasks requires 3D motion
capture to evaluate movement metrics, which is complex and time
consuming [18]. In addition, the use of these functional tasks necessarily
excludes patients with severe motor impairments who cannot perform
the task at all [23]. An ideal assessment tool should be quick and easy to
administer, repeatable, continuous and captures performance of a wide
range of impairment with minimal oor or ceiling eects.
To that aim, we have developed a multi-level robotic task to assess
bimanual function. Robotic technology provides an objective approach
to quantify sensory and motor function of the upper limb [5-7,24-26].
e basic bimanual task involved a circular ball located on a bar that
virtually connected the two hands. e task objective was to move the
ball to illuminated targets and task diculty was modied by altering
the relationship between the ball and bar (from xed to ball rolling
on the bar). Multiple targets allowed assessment across a range of the
work space and multiple levels of diculty modied the challenge and
thus skill required to perform the task. e goal was to assess various
aspects of performance: task success parameters indicated how well
participants achieved the task goal, whereas movement parameters
quantied decits in bimanual coordination. We hypothesized that
participants with stroke would be less successful in achieving the task
goal compared to a healthy control population, and that we would
identify underlying decits in movement parameters that indicate
decreased bimanual coordination. In addition, we hypothesized that as
the task increased in diculty (higher levels) we would identify more
participants with impairments.
Method
Participants
Stroke participants were recruited from the inpatient acute stroke
unit and stroke rehabilitation units at Foothills Medical Centre and
the inpatient stroke rehabilitation units at Dr. Vernon Fanning Care
Centres in Calgary, Alberta and St. Mary’s of the Lake Hospital in
Kingston, Ontario. Control participants were recruited from the
Kingston community. Participants with stroke were included in the
study if they had a conrmed diagnosis of stroke, were older than 18
years of age, and could understand the task instructions. Participants
were excluded if they had signicant medical comorbidities (e.g. angina
or active cardiac disease), had a previous stroke, or other neurologic or
musculoskeletal diagnoses aecting their upper limbs. All participants
provided informed consent prior to participation in the study. Ethical
approval was provided by Queen’s University and the University of
Calgary.
Clinical examinations
Clinical evaluations of participants with stroke were administered
by a physical or occupational therapist and included the Edinburgh
Handedness Inventory for hand dominance as well as the Montreal
Cognitive Assessment (MoCA) for assessment of cognitive impairment
[27]. e conventional subtests of the Behavioural Inattention Test
(BITc) were performed. e BITc consists of a variety of pencil and
paper tests (e.g. line bisection, letter cancelation) to screen for the
presence of visual neglect [28]. e test is scored out of 146 and a value
of 129 or below is indicative of visual neglect. e Modied Ashworth
Scale was used to evaluate spasticity at the elbow[3]. e Chedoke-
McMaster Assessment of the arm (CMSAa) and hand (CMSAh) was
used to assess the upper limb on a 7-point scale reecting stages of
motor recovery following stroke (7–highest recovery stage, 1–lowest
recovery; [4]). e Functional Independence Measure (FIMTM) was
used to rate physical and cognitive disability and level of assistance
required, intended to measure the burden of care [29]. e motor
portion (FIM motor) measures functional ability, such as washing,
dressing, toileting and mobility. e cognitive portion (FIM cognitive)
evaluates comprehension, expression, social interaction, problem
solving and memory.
Approximately 15-20% of stroke survivors that experience unilateral
brain lesions also exhibit mild impairment of their ipsilesional limb
[5,30]. e aected side of participants with stroke was characterized
using their CMSA scores, and in the current study the aected side
reects the upper limb that was most aected (contralesional limb).
Robotic set-up
All experimental tasks were performed in the KINARM robotic
exoskeleton (BKIN Technologies Ltd., Kingston, Ontario). e robot
measures kinematics of the shoulder and elbow and can apply joint-
or hand-based loads [31]. Full details of the robotic set-up have
been described previously [5]. Briey, participants were seated in a
modied wheelchair base with the feet in adjustable rests and arms
fully supported in exoskeleton robots with their shoulder and elbow
joints aligned with the linkages of the robot (Figure 1A). e seat
height was adjusted for each participant to achieve shoulder abduction
(~85°). Plastic arm troughs within the frame were adjusted to support
A
B
10 cm
Figure 1: A) Participant set-up in the KINARM robotic exoskeleton. Task
is presented in the horizontal plane to correspond with hand motion. B)
Visual representation of task workspace. Virtual bar connects the hands and
participants move the ball in the direction of the arrows as the targets are
illuminated. (Note: hands are shown for visualization but hands and arms are
occluded from view).

Citation: Lowrey CR, Jackson CPT, Bagg SD, Dukelow SP, Scott SH (2014) A Novel Robotic Task for Assessing Impairments in Bimanual Coordination
Post-Stroke. Int J Phys Med Rehabil S3: 002. doi:10.4172/2329-9096.S3-002
Page 3 of 10
Int J Phys Med Rehabil ISSN: 2329-9096 JPMR, an open access journal
Stroke rehabilitation
the upper arm and forearm/hand. is allowed free arm movement in
the horizontal plane. e robot was calibrated for each participant and
the arms and hands were occluded from view. A virtual reality system
projected visual targets and visual representation of ngertip location
(as a virtual bar linking the two hands) on the screen in the same plane
as the arm.
Task characteristics
In the current task, a 30 cm virtual ‘bar’ (1 cm thickness) was
displayed connecting the index ngers of each limb (Figure 1B). e
robot modelled the bar (mechanically and visually) as a sti spring
creating repulsive/attractive forces aligned with the bar when the
hands moved it from its default length to maintain the bar length at
30 cm. A virtual ball (1 cm radius) rested on the middle of the bar and
participants were instructed to move the ball ‘quickly and accurately’ to
four circular targets (1cm in radius) as they appeared on the screen. e
four targets were located 10cm from a centrally located origin (Figure
1B). A gravitational constant acted on the virtual environment so that
participants felt the ‘weight’ of the bar and the ball in the frontal plane
(bar mass: 0.166 kg; ball mass: 0.4 kg).
Task objectives
Participants were required to use the bar to move the ball to
successive targets appearing clockwise around the work space. e
targets appeared red, signalling ‘go’ and once the ball entered the target,
and it turned yellow. Participants had to hold the ball in the target for
1second, aer which the next red target appeared. It was more dicult
to maintain the ball within the target circle as task diculty increased.
erefore, the size of the acceptance window (initially 1 cm radius)
within which participants had to hold the ball for 1 second increased
by 1cm/s, although the visual radius of the target remained constant.
is ‘logical radius’ was included aer pilot testing to decrease the
task diculty but still encourage accurate placement of the ball within
the target. e task included 6 levels that increased in diculty by
modifying the relationship between the ball and the bar:
Level 1: Ball xed to center of bar.
Level 2: Ball moved along bar as a function of bar tilt. e greater
the tilt the further the ball moved. e ball fell o when the bar tilted ≥
20° relative to the frontal plane.
Levels 3-6: Ball had the ability to ‘roll’ with no friction on the bar.
Ball rolled faster with each increase in level (Supplemental File).
e overall goal of the task was to successfully reach as many targets
as possible within one minute (1 min per level, total task time=6 min).
Data processing and analyses
Kinematic data were recorded for the ball and hands (position
and velocity) and were sampled at 1000 Hz using Dexterit-E soware
(BKIN Technologies Ltd., Kingston, Ontario). Data were digitally
ltered oine with a 4th order dual-pass, Butterworth low pass lter,
with a cut-o frequency of 10 Hz using Matlab (e MathWorks Inc.,
Natick, MA, USA). For each level, 14 parameters were calculated from
the kinematic data, reecting overall task performance, metrics of
hand and ball movement and bimanual coordination of movements
throughout the task.
Task parameters
Task success parameters:
i. Hits: Number of successful targets hit.
ii. Drops: Number of times the ball fell o of the bar. Drops were
only possible in Levels 2-6 as the ball was xed in Level 1.
iii. Hits/Drop: Number of hits divided by the number of ball drops
(only possible for Levels 2-6).
Hand and Ball Parameters
i. Reaction time + Movement time (RT+MT): Overall time
elapsed from when target appears to when ball reaches target.
ii. Ball speed: Mean ball speed over the entire level.
iii. Hand speed: Mean hand speed over the entire level.
iv. Hand speed peaks: Number of hand speed maxima recorded
over the entire level.
Bimanual Parameters
i. Absolute Tilt: Absolute angle of the bar relative to frontal plane
and then averaged over the entire level (°).
ii. Reaction time dierence (RT di): (Level 1 only). An algorithm
was used to identify movement onset for each limb. First,
the algorithm identied the time point when the ball moved
10% of the distance to the next target, then movement onset
was dened by searching back in time to the next hand speed
minimum. Reaction time (RT) was dened as the time elapsed
from target illumination to movement onset. Absolute RT
Dierence was computed for each movement and averaged
over the entire level.
iii. Change in bar length: Identied if the subject compressed or
lengthened the bar throughout the task. Absolute change from
the resting length of the bar was computed at each time step
and averaged over the entire level.
iv. Dierence in hand speed: e cumulative sum of the absolute
dierence in speed between the two hands identied at each
time point over the entire level.
v. Dierence in hand speed peaks: Dierence in the number of
speed peaks recorded for each hand over the entire level.
vi. Dierence in hand path length: Dierence in the total hand
path calculated for each hand over the entire level.
For dierence parameters of hand speed peaks and path length,
the dierence was calculated as the performance of the aected limb
subtracted from the unaected limb for participants with stroke, and
the non-dominant limb subtracted from the dominant limb for control
participants. us positive values reect lower values for the aected
(stroke) or non-dominant (control) limb, and negative values reect
lower values for the unaected (stroke) or dominant limb (control).
Statistical analyses
Statistical analyses were performed in Matlab (e MathWorks Inc.,
Natick, MA, USA).e data were age-regressed and Box Cox transforms
[32] were used to normalize control distributions. Participants with
outliers in any parameter were removed from all analyses for that level.
Parameters were then assessed for sex eects. Percentiles were calculated
for each parameter and used as cut-o values for comparing individual
stroke performance. For one-tailed comparisons, 5th or 95th percentiles
were used where appropriate, and for two-tailed comparisons, 2.5
and 97.5 percentiles were used to determine when stroke participant
performance fell outside of 95% of controls. Correlations between
the number of parameters failed on the task and clinical scores were
performed using Spearman’s rank correlation.

Citation: Lowrey CR, Jackson CPT, Bagg SD, Dukelow SP, Scott SH (2014) A Novel Robotic Task for Assessing Impairments in Bimanual Coordination
Post-Stroke. Int J Phys Med Rehabil S3: 002. doi:10.4172/2329-9096.S3-002
Page 4 of 10
Int J Phys Med Rehabil ISSN: 2329-9096 JPMR, an open access journal
Stroke rehabilitation
Results
Participant demographics and clinical scores
Data were collected for 75 control (41 Female) and 23 stroke
participants (9 Female; Table 1). e stroke participants consisted of
more le (n=18) than right aected participants (n=5). e type of
stroke was primarily ischemic (21/23) and days post-stroke varied from
1–46 days with the following distribution: ≤ 1week post stroke (n=7),
1-3 weeks (n=6), 3–6 weeks (n=8),>6 weeks (n=2; 43 and 46 days).
Clinical scores show a range of impairment. FIM motor scores
(Table 1) ranged from 37 to 126. BIT scores ranged from 100-146, with
3 participants scoring below the cut-o of 129, indicating the presence
of visuospatial neglect (28-Wilson et al., 1987). CMSA scores ranged
from 1-7 with 16/22 participants scoring 6 or below for the aected arm
and 19/22 scoring 6 or below for the aected hand. For the unaected
limb, scores ranged from 5-7 with 8/22 participants scoring 5 or 6 for
the arm and 5/22 participants scoring 6 for the hand indicating that
36% of our participants had mild ipsilesional arm impairment and 22%
had mild ipsilesional impairment in the hand. Five of the 6 participants
with ipsilesional hand impairment also exhibited ipsilesional arm
impairment. All participants with ipsilesional upper limb impairment
were more impaired on their contralesional side, scoring at least
A
B
C
Control Participant
(#31): 72 years old
Left-aected
stroke participant
(#12): 68 years old
Right-aected
stroke participant
(#15): 78 years old
Left Hand Right HandBall Path
10 cm
50
0 1.0 2.0
cm/s
0
25
50
cm/s
0
25
Time (s)
Target on Target on
Target on Target on
Target on Target on
50
cm/s
0
25
50
cm/s
0
25
Left Hand Speed Right Hand Speed
0 1.0 2.0
Time (s)
Figure 2: Hand and ball path traces from exemplar participants, for Level 1. A) Traces from a 72 year old control participant. Solid black lines represent hand
path of each hand and dotted line represents the path of the ball. At this level, the ball is coupled to the hand path as it is xed to the bar linking both hands (i.e.,
ball represents the mid-point of the distance between both hands). Note: traces would normally overlap but are separated for illustrative purposes. Far right plots
represent hand speed for the left and right hands overlaid for multiple reaches to each target. Dotted line represents the time point when the target was illuminated.
B) Traces from a left-affected stroke participant (68 years old) with a lesion in the right middle cerebral artery (MCA) assessed 26 days post-stroke. This participant
scored 7/7 for left (affected) arm CMSA and 4/7 for right (unaffected) arm CMSA. C) Traces from a right-affected stroke participant (78 years old) with a lesion in
the left MCA assessed 12 days post-stroke. This participant scored 2/7 for right (affected) arm CMSA and 6/7 for left (unaffected) arm CMSA.
Participant Group Stroke (n = 23) Control (n = 75)
Age mean (range) 61 (26-87) 53 (18-87)
Gender 9 F/14 M 41 F/34 M
Handedness 2 LH/21 RH 8 LH/67 RH
Affected Side 18 LA / 5 RA -
Days Since Stroke 20 (1–46) -
Type of Stroke (I/H) 21/2 -
BIT 138 (100–146) (n=21) -
MoCA (/30) 25 (19–30) (n=19) -
CMSA (possible ratings) [1,2,3,4,5,6,7]
Arm Affected [0,4,2,2,4,4,6] (n=22) -
Unaffected [0,0,0,0,3,5,14] -
Hand Affected [1,2,1,1,6,7,4] -
Unaffected [0,0,0,0,0,5,17] -
FIM (cognitive /35) 32 (20-35) -
FIM (motor /91) 77 (17-91) -
FIM (total /126) 109 (37-126) -
Abbreviations: F/M: female/male; LH/RH: left/right-handed; LA/RA: Left/ Right Affected
I/H: Ischemic/Hemmorrhagic; C/SC/B/Cr/M/U: Cortical/Sub-cortical/Brainstem/Cerebellum/Mixed/Unknown; BIT: Behavioural Inattention Test; MoCA: Montreal Cognitive
Assessment; CMSA: Chedoke-McMaster Stroke Assessment; FIM: Functional Independence Measure
Table 1: Clinical and demographic data

Citation: Lowrey CR, Jackson CPT, Bagg SD, Dukelow SP, Scott SH (2014) A Novel Robotic Task for Assessing Impairments in Bimanual Coordination
Post-Stroke. Int J Phys Med Rehabil S3: 002. doi:10.4172/2329-9096.S3-002
Page 5 of 10
Int J Phys Med Rehabil ISSN: 2329-9096 JPMR, an open access journal
Stroke rehabilitation
one Level lower on the CMSA. MoCA scores were recorded for 19
participants and ranged from 19-30 with a mean of 25.
Bimanual robotic task: LEVEL 1
General participant performance: Overall, participants with
stroke had impaired performance on the bimanual task compared to
control participants. Exemplar participant hand and ball trajectories for
Level 1 indicate that control participants moved their hands relatively
straight as they progressed to each target with minimal corrective
movements (Figure 2A). Movements were consistent from trial to trial,
with low variability in successive reaches to targets. Patterns of hand
motions are similar to movements of the ball.
In contrast movements of an exemplar le-aected participant
(Figure 2B) and right-aected participant (Figure 2C) were variable
from reach to reach. Both participants showed less movement area
and smaller path lengths with their aected limb. In particular, for the
right-aected participant, the right hand moved back and forth with no
‘diamond’ shape to the trajectory. In general participants with stroke
hit fewer targets and moved more slowly from target to target but with
more speed peaks (i.e. jerky, less smooth movements: right panels,
Figure 2).
Performance of healthy controls: Statistical analyses identied
which of the parameters were inuenced by age and/or sex. Age eects
were found for path length dierence and RT di in Level 1, and sex
eects were found for bar length changes and hand speed peaks (aected
and unaected). ese factors were taken into account when calculating
percentiles for the normative ranges for control performance.
Performance of stroke participants on individual parameters:
Normative ranges calculated from control data were used to identify the
number of stroke participants whose performance fell outside of 95%
of healthy controls for each parameter. Cumulative sum histograms
of participant performance are illustrated for Level 1 (Figure 3). e
parameter that identied the most stroke participants overall was
number of hits in Level 1: 96% of stroke participants (22/23) hit fewer
targets than 95% of control participants. e parameter that identied
the second most participants as impaired was RT+MT (78%) (Figure
3, Table 2).
0 10 20 30 40
0
20
40
60
80
100
Cumulative sum
0 1000 2000 3000
0
20
40
60
80
100
Cumulative sum
RT + MT (ms)
0.04 0.06 0.08 0.1 0.12
0
20
40
60
80
100
Cumulative sum
Ball speed (m/s)
100 200 300 400
0
20
40
60
80
100
Cumulative sum
Speed peaks a.
100 200 300 400 500
0
20
40
60
80
100
Cumulative sum
Speed peaks una.
0 20 40 60
0
20
40
60
80
100
Cumulative sum
Absolute tilt (deg) 0 1 2 3
0
20
40
60
80
100
Cumulative sum
Change in bar length (m) −200 −100 0 100
0
20
40
60
80
100
Cumulative sum
Di speed peaks
0 2000 4000 6000
0
20
40
60
80
100
Cumulative sum
Di hand speed (m/s)
Impairment
20 40 60 80 100
−3
−2
−1
0
1
2
Age (years)
Di path length (m)
20 40 60 80 100
0
50
100
150
Age (years)
RT di
0 0.05 0.1 0.15 0.2
0
20
40
60
80
100
Cumulative sum
Hand speed (m/s)
Hits
control - male
control - female
stroke - male
stroke - female
control
stroke
A
B
C
D
control - m
control - f
stroke - m
stroke - f
control - m
control - f
stroke - m
stroke - f
control - m
control - f
stroke - m
stroke - f
control
stroke
97.5 p
2.5 p
95th p
5th p
Figure 3: Overall participant performance on parameters at Level 1. Row A: solid black data line (thick) represents control participant data distribution and dashed
line represents stroke participant data distribution. Shaded grey areas represent 95% of control performance. Row B: hand speed is presented in the rst plot for
both affected/non-dominant side for stroke (black dashed) and control (solid black) and for the unaffected/dominant side (red dashed and solid lines respectively).
The number of speed peaks is shown in the next two plots in row B, separated into male (black) and female (red) groups. For both plots (as well as bar length
changes in row C), signicant effects of sex were found at Level 1, therefore comparisons were made with the groups split. Row D: Signicant age effects were
found for RT diff and Diff in path length; these parameters are plotted against age with age-corrected control percentiles (RT diff was one-tailed; 95th percentile
was used, Diff path length was two-tailed; 2.5 and 97.5 percentiles were used; negative values reect greater path length with unaffected (or dominant for control)
side). No data are shown for number of drops or drops/hit at Level 1 because drops are not possible at this level.

Citation: Lowrey CR, Jackson CPT, Bagg SD, Dukelow SP, Scott SH (2014) A Novel Robotic Task for Assessing Impairments in Bimanual Coordination
Post-Stroke. Int J Phys Med Rehabil S3: 002. doi:10.4172/2329-9096.S3-002
Page 6 of 10
Int J Phys Med Rehabil ISSN: 2329-9096 JPMR, an open access journal
Stroke rehabilitation
With regards to measures that specically quantied bimanual
performance in stroke, the best parameters in Level 1were RT di
(57%), dierence in hand speed peaks, (48%) and absolute tilt (43%).
e majority of participants that displayed dierences in number of
hand speed peaks had more peaks with their unaected limb (>35 more
speed peaks than with aected limb). Impairment in absolute tilt was
associated with a greater amount of tilt (>9° on average).
Individual proles of impairment: A primary aim of the current
task was to develop individual proles of impairment rather than group
comparisons. ese patterns are displayed in Figure 4 and show the
unique pattern of impairments across participants. In Level 1 some
participants had impairments primarily in task success, and hand
and ball parameters, but displayed minimal impairments in bimanual
performance (i.e. participants 4 and 19). Conversely, some participants
failed the majority of bimanual parameters, but passed most of the
other parameters (i.e. participants 8 and 16). Several participants were
observed to have impairments across all recorded parameter groups
(i.e. participants 14 and 23).
Overall, stroke participants failed more task parameters than control
participants (Figure 5). Approximately 78% of stroke participants
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
Subject Number
Level 1
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
Parameters
Level 2
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
Level 4
Task success Hand and Ball Bimanual Task success Hand and Ball Bimanual Hand and Ball Bimanual
Hits
Drops
Drops/Hit
RT + MT
Ball speed
Hand speed a.
Hand speed una.
Speed peaks a.
Speed peaks una.
RT di
Absolute tilt
Bar length
Di hand speed
Di path length
Di speed peaks
Hits
Drops
Drops/Hit
RT + MT
Ball speed
Hand speed a.
Hand speed una.
Speed peaks a.
Speed peaks una.
Absolute tilt
Bar length
Di hand speed
Di path length
Di speed peaks
Hits
Drops
Drops/Hit
RT + MT
Ball speed
Hand speed a.
Hand speed una.
Speed peaks a.
Speed peaks una.
Absolute tilt
Bar length
Di hand speed
Di path length
Di speed peaks
Task success
Figure 4: Individual stroke participant performance represented for Levels 1, 2 and 4. Parameters are grouped into task success, hand and ball and bimanual
parameters. Black squares reect failure of the parameter (fell outside of 95% of control participant performance). Results show that the majority of participants
failed task success parameters across levels but individualized patterns of parameters failed are shown across the remaining two parameter groups.
0 2 4 6 8 10 12
0
20
40
60
80
100
Cumulative sum
Level 1
Number of parameters failed
0 2 4 6 8 10 12
0
20
40
60
80
100
Cumulative sum
Level 2
Number of parameters failed
0 2 4 6 8 10
0
20
40
60
80
100
Cumulative sum
Level 4
Number of parameters failed
0 5 10 15 20 25 30
0
20
40
60
80
100
Cumulative sum
All 3 Levels
Number of parameters failed
Impairment
Control
Stroke
Figure 5: Cumulative sum traces of the number of parameters failed for stroke and control participants. Comparisons are displayed for Levels 1, 2, 4 and the total
number of parameters failed for all 3 levels combined. Black data traces represent the control participants and dotted data traces represent the stroke participants.
Solid vertical lines indicate the 95th percentile of controls. Note that for Levels 1 and 2 and the combination of all three levels, over 75% of stroke participants failed
more parameters than 95% of controls.

Citation: Lowrey CR, Jackson CPT, Bagg SD, Dukelow SP, Scott SH (2014) A Novel Robotic Task for Assessing Impairments in Bimanual Coordination
Post-Stroke. Int J Phys Med Rehabil S3: 002. doi:10.4172/2329-9096.S3-002
Page 7 of 10
Int J Phys Med Rehabil ISSN: 2329-9096 JPMR, an open access journal
Stroke rehabilitation
0 10 20 30
18
20
22
24
26
28
30
Score
MoCA
r = −0.34
p = 0.16
0 10 20 30
90
100
110
120
130
140
150
Score
BIT score
r = −0.36
p = 0.12
0 10 20 30
16
18
20
22
24
26
28
Score
Combined CMSA
r = −0.60
p = 0.003
0 10 20 30
15
20
25
30
35
Score
Number of Parameters Failed
(3 levels)
FIM cognitive
r = −0.47
p = 0.02
0 10 20 30
0
20
40
60
80
100
Score
Number of Parameters Failed
(3 levels)
FIM motor
r = −0.80
p = 4.89e−06
0 10 20 30
20
40
60
80
100
120
140
Score
Number of Parameters Failed
(3 levels)
FIM total
r = −0.77
p = 1.9e−05
Number of Parameters Failed
(3 levels)
Number of Parameters Failed
(3 levels)
Number of Parameters Failed
(3 levels)
Figure 6: Correlation of clinical scores with parameters failed overall. Number of parameters failed is the total number of parameters failed (at least once) across
Levels 1, 2 and 4. Combined CMSA is a combined score for hand and arm for the affected side only. Displayed r and p values are calculated using Spearman’s
rank correlation. MoCA: Montreal Cognitive Assessment, BIT: Behavioural Inattention Task, CMSA: Chedoke-McMaster Stroke Assessment, FIM: Functional
Independence Measure.
failed 4 parameters or more in Level 1 compared to only 5% of control
participants who failed 4 or more.
Bimanual robotic task: LEVEL 2-6
Performance of stroke participants across Levels: Level 1
identied the most performance impairments in stroke participants
with the highest number of parameters failed (151); (Table 2).
Unexpectedly, as the levels progressed in diculty (Level 2 to 6) the
number of parameters failed tended to decrease (101, 74, 85, 47, 38;
Table 2). Performance across levels was analyzed to determine the
number of ‘new fails’ that were introduced at each level (Table 2).
Levels 1, 2 and 4 showed the highest ability to identify impairments
in participants as they identied the most fails overall, as well as the
highest combination of new fails. As a result we focused our further
analyses on Levels 2 and 4.
Abbreviations: sp=speed, pks=peaks, aff=affected side, unaff=unaffected side (affected side always compared to control non-dominant side and unaffected side always
compared to control dominant side) Cusum=Cumulative sum; RT+MT=reaction time plus movement time.
Table 2: Number of stroke participants that were identied as outside of 95% of controls (number of fails) on each parameter across each level.
Level 1 2 3 4 5 6 Overall
Parameter Control percentile Fail # Fail # New fails Fail # New fails Fail # New fails Fail # New fails Fail # New fails % failed
Task Success
Hits < 5 22 14 012 017 10020100%
Drops > 95 -11 11 7162303057%
Drops/Hit > 95 -11 11 11 316 4522083%
Hand/Ball
RT+MT > 95 18 15 02020----83%
Ball sp < 2.5,> 97.5 15 5 38172811091%
Hand sp aff < 2.5,> 97.5 16 6 22010101074%
Hand sp unaff < 2.5,> 97.5 14 4 00010101061%
Sp pks aff > 95 7 2 04220302039%
Sp pks unaff > 95 10 5 05131102048%
Bimanual
Absolute tilt > 95 10 11 27280618161%
RT diff < 2.5,> 97.5 13 - ---------57%
Bar length > 95 2 3 20012000022%
Diff sp pks < 2.5,> 97.5 11 926090815065%
Diff hand sp > 95 5 2 00154310043%
Diff path length < 2.5,> 97.5 8 4 110 3709211 165%
Total num. fails 151 101 74 85 47 38
Total new fails 32 13 14 8 2

Citation: Lowrey CR, Jackson CPT, Bagg SD, Dukelow SP, Scott SH (2014) A Novel Robotic Task for Assessing Impairments in Bimanual Coordination
Post-Stroke. Int J Phys Med Rehabil S3: 002. doi:10.4172/2329-9096.S3-002
Page 8 of 10
Int J Phys Med Rehabil ISSN: 2329-9096 JPMR, an open access journal
Stroke rehabilitation
Participant performance: Levels 2 and 4: As in Level 1, the best
parameters for identifying participants with stroke in Level 2 were hits
(65%) and RT+MT (65%); (Table 2). Absolute tilt was the best bimanual
parameter for identifying impairments with stroke (48%). irty-two
‘new fails’ were captured in Level 2 primarily due to the fact that ball
drops were possible in this level. Drops and drops per hit identied 11
participants each (48%).
In Level 4, the number of hits was again the best parameter to
identify impairment in stroke performance with 17 fails (74%) and
the second best parameter was drops per hit (70%). e best bimanual
parameters were absolute tilt (35%), dierence in speed peaks (39%)
and path length dierence (30%). Fourteen new fails were captured
indicating that 14 participants failed a parameter in Level 4 that they
had not previously failed.
Across the Levels 1, 2 and 4, participants with stroke failed more task
parameters than healthy control participants (Figure 5). Approximately
78% of stroke participants failed 8 parameters or more when all three
levels were combined compared to only 5% of control participants who
failed 8 or more.
Finally, robotic task performance was compared between
participants with clinically identied, mild ipsilesional impairments
(n=9) and those without (n=14). We found no signicant dierences
for any parameter between these groups (Kolmogorov-Smirnov tests,
p>0.05).
Correlation with clinical scores: e number of parameters failed
was combined across Levels 1, 2 and 4 and was compared to other
clinical evaluation scores (Figure 6). Percentage of parameters failed
overall was signicantly correlated with FIM scores: FIM motor (r=-
0.80, p<0.001) FIM cognitive (r=-0.47, p=0.02) and FIM total (r=-0.77,
p<0.001). Combined CMSA (arm and hand) score was correlated with
percentage of parameters failed (r=-0.60, p=0.003). Negative correlation
values indicate that low clinical scores were correlated with more task
parameters failed.
Discussion
Bimanual task performance
e robotic ball on bar task quantied performance on a bimanual
activity and identied impairments in participants with stroke. e
primary goal of the task was to hit as many targets as possible, which
required coordinated movement of both limbs. e higher task levels
involved balancing the ball, thus increasing task diculty which we
hypothesized would highlight more impariments in stroke performance.
e principle was to stress the motor system with a more complex task
to help uncover subtle decits in coordination to separate participants
with stroke from healthy controls. However, the reverse was observed;
Level 1 identied the most impairments in participants with stroke.
is unexpected result likely reects that healthy control performance
was stereotyped for the initial levels in which participants simply
moved the ball to the spatial target, but became more idiosyncratic as
balancing the ball became more dicult. is increased the variability
of control performance and inuenced the 95% criteria used to
identify whether a participant with stroke was impaired on a particular
parameter. However, we found Levels 2 and 4 did identify new features
or individuals as being impaired compared to Level 1. us, we thus
focused our analysis on Levels 1, 2 and 4 as together they captured the
most impairments in performance. Another advantage of reducing the
number of levels is the reduction of task time from 6 to 3 minutes. In
order to integrate tasks into a standard assessment protocol and avoid
fatigue for the patient, shorter task time is highly benecial. In addition,
the more challenging levels were oen frustrating for participants, both
patients and controls. Removal of the two most challenging levels
makes the task more enjoyable and will encourage patient compliance.
Task success was determined by the number of targets hit and
number of drops (times the ball fell o of the bar). In the initial level,
the number of hits identied the most participants with stroke of all
parameters (96%). Number of hits was the most sensitive parameter
overall in the sense that it indicated impaired performance in 100% of
participants with stroke when the 3 levels were combined. Failure of
this parameter indicates global inability to complete the task but does
not provide information about the under-lying impairment that led to
task failure; the sub-categories of performance parameters provide this
insight.
e present bimanual task captures much motor impairment
observed in previous studies. In Level 1 hand and ball-related
parameters indicated that participants with stroke moved slower, took
longer to respond to target illumination and to move to the target
(RT+MT) and exhibited more corrective sub-movements. ese
ndings are in line with previous characterizations of visually-guided
reaching with one hand [5,21] or simultaneous reaching with both
hands [18,20,21,23]. Further, we found specic decits in parameters
related to bimanual control notably, dierences in RT between the
two limbs and dierences in hand speed. ese asymmetries in motor
performance are likely related to previous observations of decreased
movement synchrony [18,21,23,33]. Interestingly, the dierence in RT
between the two hands in our bimanual task was less than ~50ms for
healthy control participants. When both hands are assessed separately
in a reaction time reaching task, the dierence in RT was also found
to be less than ~50 ms for healthy controls [5]. is highlights that a
hallmark of healthy motor control is symmetry between the limbs.
Such small dierences in motor performance are not easily observable
with visual inspection, which highlights the advantages that a robotic
paradigm has in capturing subtle dierences in performance.
Individual patterns of impairment
A primary aim of the task was to develop an individualized
‘nger print’ of impairment for each participant, rather than to
characterize group dierences in performance between stroke
and control participants. In a heterogeneous population of stroke
participants, dierent lesion severity and location is likely to lead to
vast dierences in decits, for example motor vs. sensory impairment
[34]. Unique impairments (or pattern of impairment) seen in one
participant may be lost when averaged across participants. Although
the current study focused on parameters that identied impairments
across many participants, parameters that identied fewer participants
are equally important to assess. For example, changes in bar length
identied impairments in only 6 individuals. In these individuals,
larger cumulative changes in bar length were oen caused by relatively
small but frequent oscillatory movements on the spring-like bar (data
not shown). ese oscillations may reect unique underlying injury
or impairment in these participants such as the onset of tremor [35]
and may necessitate novel strategies for rehabilitation. For example, the
application of biomechanical loads to the limbs can reduce tremor [36]
and may evolve as a benecial rehabilitation component for individuals
with tremor-like impairments post-stroke.
Relation to clinical scores
Performance on the task overall was signicantly correlated to
clinical scores. e number of parameters failed in the task correlated
most strongly (r=-0.80) with measures of functional motor abilities

Citation: Lowrey CR, Jackson CPT, Bagg SD, Dukelow SP, Scott SH (2014) A Novel Robotic Task for Assessing Impairments in Bimanual Coordination
Post-Stroke. Int J Phys Med Rehabil S3: 002. doi:10.4172/2329-9096.S3-002
Page 9 of 10
Int J Phys Med Rehabil ISSN: 2329-9096 JPMR, an open access journal
Stroke rehabilitation
(FIM motor). e strength of the correlation with FIM is greater than
that found previously for our other robotic tasks: visually-guided
reaching [5] and position sense [26] both of which evaluate each limb in
isolation. is observation may indicate that performance of functional
daily activities is better reected by a task that assesses the coordinated
use of both limbs. We noted that a number of participants failed the
majority of parameters on the bimanual task but scored almost perfect
on the FIM. Participants may score highly on the FIM if they have
learned compensatory strategies (i.e. tying shoes or buttoning a shirt
with one hand) or use assistive devices to complete the tasks specic
to the FIM. In a novel bimanual task such as ours, in which the use of
both hands is necessary, more decits were apparent. In this way, the
bimanual task provides a more sensitive measure of bimanual motor
function, and is likely more reective of performance on daily activities
that requires the use of both hands.
Limitations and future considerations
A limitation of the current study is the relatively small sample
size that did not aord the systematic comparison of anatomical
lesion characteristics and robotic task performance. For example,
right- vs. le-aected stroke participants may exhibit hemisphere-
specic impairment in dierent aspects of movement coordination
(i.e. trajectory vs. end-point accuracy) [37-39]. However, the cause
of bimanual impairment post-stroke is likely multi-faceted. It may
be aected by the suppression of inter-hemisphere communication
[40] asymmetry in hemispheric inhibition (or dis-inhibition) [41,42],
damage to the supplementary motor area [43], unilateral sensory [44]
or motor impairments[20] or a combination of these and potentially
other factors. Future work will combine the current task with other
robotic assessment tasks to provide further insight into whether
specic patterns of bimanual impairment is related to lesion anatomy
or to identied unilateral sensory and/or motor decits.
Summary
Our task provides a proof of principle for the quantication
of bimanual control post-stroke. Accurate assessment of bimanual
coordination is essential to reliably track improvement during
traditional or novel rehabilitation approaches [24,25]. rough
objective measurement of attributes of bimanual control, the robotic
task can provide an accurate baseline from which to assess changes over
the course of recovery or rehabilitation.
Acknowledgement
The authors would like to thank Helen Bretzke, Mary Jo Demers, Kim Moore,
Justin Peterson, Janice Yajure and Megan Metzler for their assistance and
contribution to this work.
Funding Sources
The present work was supported by a Canadian Institutes of Health Research
(MOP 106662), a Heart and Stroke Foundation of Alberta, Northwest Territories and
Nunavut Grant-in-Aid, an Alberta Innovates - Health Solutions Team Grant, and an
Ontario Research Fund Grant (ORF-RE 04-47). CPTJ was supported by NSERC
CREATE and a Neuro Dev Net postdoctoral fellowship. SHS was supported by a
GSK-CIHR chair in Neuroscience.
Disclosures
Dr. Scott is cofounder and chief scientic ofcer of BKIN Technologies, which
commercializes the KINARM robotic technology.
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This article was originally published in a special issue, Stroke Rehabilitation
handled by Editor. Naoyuki Takeuchi, Hospital of Tohoku University, Japan,
Shu Morioka, Kio University, Japan
Citation: Lowrey CR, Jackson CPT, Bagg SD, Dukelow SP, Scott SH (2014) A
Novel Robotic Task for Assessing Impairments in Bimanual Coordination Post-
Stroke. Int J Phys Med Rehabil S3: 002. doi:10.4172/2329-9096.S3-002
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