Robotic Identification of Kinesthetic Deficits After Stroke
Jennifer A. Semrau, PhD; Troy M. Herter, PhD; Stephen H. Scott, PhD; Sean P. Dukelow, MD, PhD
Downloaded from http://stroke.ahajournals.org/ by guest on June 18, 2017
Background and Purpose—Kinesthesia, the sense of body motion, is essential to proper control and execution of movement.
Despite its importance for activities of daily living, no current clinical measures can objectively measure kinesthetic
deficits. The goal of this study was to use robotic technology to quantify prevalence and severity of kinesthetic deficits
of the upper limb poststroke.
Methods—Seventy-four neurologically intact subjects and 113 subjects with stroke (62 left-affected, 51 right-affected)
performed a robot-based kinesthetic matching task with vision occluded. The robot moved the most affected arm at a
preset speed, direction, and magnitude. Subjects were instructed to mirror-match the movement with their opposite arm
(active arm).
Results—A large number of subjects with stroke were significantly impaired on measures of kinesthesia. We observed
impairments in ability to match movement direction (69% and 49% impaired for left- and right-affected subjects,
respectively) and movement magnitude (42% and 31%). We observed impairments to match movement speed (32% and
27%) and increased response latencies (48% and 20%). Movement direction errors and response latencies were related to
clinical measures of function, motor recovery, and dexterity.
Conclusions—Using a robotic approach, we found that 61% of acute stroke survivors (n=69) had kinesthetic deficits.
Additionally, these deficits were highly related to existing clinical measures, suggesting the importance of kinesthesia in
day-to-day function. Our methods allow for more sensitive, accurate, and objective identification of kinesthetic deficits
after stroke. With this information, we can better inform clinical treatment strategies to improve poststroke rehabilitative
care and outcomes. (Stroke. 2013;44:3414-3421.)
Key Words: kinesthesis ◼ proprioception ◼ stroke
P
roprioception has been defined as the sense of limb
geometry (position sense) and limb motion (kinesthesia).1 Proprioceptive deficits affect more than half of stroke
survivors2–4 and can lead to significant problems with balance,5 coordination,6 and performance of activities of daily
living.4,7 Unfortunately, clinicians are faced with significant
challenges to measure proprioception accurately, because
clinical assessments of proprioception have been identified
as insensitive, unreliable, and subjective.8 We commonly
see patients after stroke who are unable to perform activities of daily living, such as brushing their hair, or reaching
for a cup; yet current clinical assessments make it difficult to
discern whether the underlying problem is a result of damage to the motor system, sensory system, or both. The current
situation in stroke rehabilitation is problematic, because poststroke proprioceptive deficits are recognized as important and
exceedingly common, but there is limited ability to identify
their nature or magnitude accurately.
The use of robotic technology to advance stroke recovery
and rehabilitation is a rapidly developing field. Work from
many groups has shown much promise for stroke rehabilitation through robotics.9–13 As well, a growing area of research
is the use of robotic tools to quantify sensorimotor dysfunction poststroke.4,14–16 Robotics promise increased precision
and accuracy of measurement, in addition to better reliability
compared with standardized observer-based ordinal scales.
Work from our group has detailed the success of using
robotic technology to objectively identify and assess position sense deficits. We have found that position sense deficits
occur in ≈50% of stroke patients.4,7 However, the incidence
and severity of kinesthetic deficits is largely unknown after
stroke. Although many rehabilitation clinicians suggest that
intact kinesthesia is important for recovery, there are few
standardized ways to assess it. By developing techniques
to quantify and assess kinesthesia, we will be better able
to develop and monitor rehabilitative strategies directed at
individuals who display predominantly kinesthetic and proprioceptive deficits. We hypothesize that stroke may result in
significant kinesthetic deficits that can be reliably quantified
using robotic technology.
Received May 7, 2013; accepted September 17, 2013.
From the Hotchkiss Brain Institute (J.A.S., T.M.H., S.P.D.), and Department of Clinical Neurosciences (J.A.S., T.M.H., S.P.D.), University of Calgary,
Alberta, Canada; Centre for Neuroscience Studies (T.M.H., S.H.S.), Department of Anatomy and Cell Biology (S.H.S.), and School of Medicine (S.H.S.),
Queen’s University, Kingston, Ontario, Canada; and Department of Exercise Science, University of South Carolina, Columbia (T.M.H.).
The online-only Data Supplement is available with this article at http://stroke.ahajournals.org/lookup/suppl/doi:10.1161/STROKEAHA.
113.002058/-/DC1.
Correspondence to Sean P. Dukelow, MD, PhD, 1403 29th St NW, Foothills Medical Centre, South Tower, Room 905, Calgary, Alberta T2N 2T9,
Canada. E-mail [email protected]
© 2013 American Heart Association, Inc.
Stroke is available at http://stroke.ahajournals.org
DOI: 10.1161/STROKEAHA.113.002058
3414
Semrau et al Kinesthetic Deficits Poststroke 3415
Methods
Subjects
Subjects with stroke were recruited from the acute stroke unit at
Foothills Hospital and the stroke rehabilitation units at Foothills
Hospital and Dr Vernon Fanning Center in Calgary, Alberta, Canada.
Control subjects were recruited from the community. Subjects with
stroke were included if they had first onset, clinically diagnosed unilateral stroke and could understand the task instructions. Forty-three
subjects were excluded because of visuospatial neglect, significant
active medical issues (eg, angina, uncontrolled hypertension, respiratory distress), bilateral stroke (confirmed via scan), other neurological
diagnoses, or significant issues caused by previous orthopedic injuries to either upper limb. This study was approved by the University
of Calgary Ethics Board, and all subjects provided informed consent.
Clinical Assessments
of 4 parameters were used to characterize the spatial (X,Y) and temporal (hand speed) performance of the subject. These included:
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1. Response Latency (RL; Figure 1C). The difference in movement
onset time between the active and passive arms. Movement initiation was defined as the point when subjects reached 10% of
the maximum movement speed.
2.Peak Speed Ratio (PSR; Figure 1C). The ratio between the
maximum hand speed of the active and passive arms. A ratio of
1 indicates perfect speed matching, and ratios <1 or >1 would
indicate that the active arm moved slower or faster, respectively, than the passive, robotically moved arm.
3. Initial Direction Error (IDE; Figure 1D). This parameter quantified the sense of direction by measuring absolute angular deviation at peak hand speed between the active and passive arms.
4. Path Length Ratio (PLR; Figure 1D). This quantified a subject’s
ability to match the distance of hand movement. Movement
end was defined as the point when subjects slowed to 10% of
maximum speed after achieving maximum speed. PLR was calculated by dividing the total movement length of the subject’s
active arm by the length moved by the passive arm. A ratio of
1 indicated perfect matching, and ratios <1 or >1 indicated that
movement of the active arm was smaller or larger in magnitude,
respectively, than the passive arm moved by the robot.
Clinical assessments for subjects were conducted by either the study
physician or a study therapist with expertise in stroke rehabilitation.
Subjects with stroke were assessed using the Edinburgh Handedness
Inventory,17 Chedoke–McMaster Stroke Assessment–impairment inventory for the arm and hand (CMSA), the Purdue Peg Board (PPB),
the Thumb Localizing Test (TLT, a clinical assessment of position
sense),18 the Behavioral Inattention Test (BIT) of visuospatial neglect,
and the Functional Independence Measure (FIM). Subjects with
hemineglect (BIT score <130) were excluded. For all clinical assessments, we observed no significant differences between left-affected
(LA) and right-affected (RA) subjects (unpaired t test; P>0.05).
As ≈15% of stroke survivors with unilateral brain lesions exhibit
ipsilesional deficits poststroke,16 in the remainder of the article we will
refer to subjects with stroke by their most affected side of their body.
Average responses for RL, PSR, IDE, and PLR were calculated
for each subject by taking the mean response of all 36 trials performed. To determine kinesthetic deficits in the stroke groups, we
derived normative control ranges for RL, PSR, IDE, and PLR from
the distribution of control data as the 95% confidence interval (95%
CI, 0%–95%) for 1-sided metrics (IDE, RL) and 2-sided metrics
(2.5%–97.5%; PLR, PSR).
Kinesthesia Task
Variability
The kinesiological instrument for normal and altered reaching movements (KINARM, BKIN Technologies, Kingston) exoskeleton
robotic device has been previously described4,7,16,19 (Figure I in the
online-only Data Supplement). Subjects sat in the wheelchair base of
the robot with their arms supported against gravity by the exoskeleton. Subjects performed a kinesthetic matching task with vision of the
upper limbs occluded. Before beginning each trial, the robot moved
1 arm to 1 of 3 possible locations in the workspace. Simultaneously,
subjects saw a red circle on the opposite side of the workspace
(located at 1 of 3 possible locations, mirrored across the midline) and
a white circle representing the position of the index finger of their opposite arm. Subjects were asked to move the white circle into the red
circle, which effectively brought the 2 limbs to a mirrored start position and initiated the trial. The visual targets and the white dots representing the hands were then extinguished. After a random time period
(1500±250 ms), the robot moved the subjects’ arm (passive arm) to
1 of the other 2 targets at a preset speed (Figure 1). The straight-line
movement between targets was 20 cm in length, with a bell-shape
velocity profile with a peak speed of 0.28 m/s. As soon as subjects felt
movement of their passive arm, they were instructed to mirror-match
this movement with the opposite arm (active arm). For subjects with
stroke, the robot only ever moved the most affected upper limb, and
subjects actively mirror-matched with their opposite arm. For control subjects, both arms were tested. All subjects performed 36 total
movements, for a total of 6 movements in each of 6 movement directions (Figure 1). Subjects were also required to complete the movement within 10 s after the robot initiated movement. If subjects did
not fulfill this time requirement the movement was categorized as a
non-movement.
For the 4 abovementioned measures, we computed variability as the
standard deviation across all movements within a single subjects’ performance on that particular parameter (RL variability [RLv], PSR variability [PSRv], IDE variability [IDEv], and PLR variability [PLRv]).
Data Analysis
Movement in the x-direction of the active arm was mirror-transposed
to compare the direction of movement of the 2 limbs. Minor compliance in the robots created small variations in peak speed (±0.028
m/s) and distance (±0.016 cm) of the passively moved arm, and these
variations were accounted for in the analysis. The mean and variance
Statistical Analyses
To determine statistical significance for each of the abovementioned
measures, we calculated normative ranges derived from the 95% CI
for 1-sided measures (IDE, RL, all variability measures) and 2-sided
measures (PLR, PSR) of our control group (Figure 2C, grey box).
Establishing this control range (95% CI) allows us to determine
whether individuals in our stroke group display abnormal behavior
relative to the behavior of control subjects. Those individuals lying
outside of the 95% CI are significantly different from controls with
P<0.05. Linear regressions were performed to compare robotic measures with clinical assessments, and Bonferroni corrections adjusted
for multiple comparisons where needed.
Results
Subjects
We measured kinesthesia in 74 controls and 113 subjects with
subacute stroke. Subject demographics and clinical measurements are described in Table 1. Despite all subjects with stroke
having a unilateral lesion, some demonstrated mild bilateral
impairments, with the ipsilesional arm being affected in 9 predominantly LA and 3 predominantly RA stroke subjects. The
majority of these individuals had CMSA arm scores of 6, with
2 having CMSA arm scores of 5 for the ipsilesional arm. In
general, patients in the LA and RA groups exhibited relatively
similar demographic and clinical profiles.
Many subjects with stroke demonstrated significantly
impaired kinesthesia. Several stroke subjects failed to even
3416 Stroke December 2013
A
Exemplar Control Data - Hand Position
B
Exemplar Control − Hand Speed
0.5
Robot − Median
Control − Median
Control − Individual Movements
0.4
5 cm
Hand Speed (m/s)
Robot − Median
Control − Median
Control −
Individual Movements
0.3
0.2
0.1
0
Temporal Measurements
1000
1500
2000
Time (ms)
2500
3000
3500
4000
Spatial Measurements
Robot Movement
Subject Movement
*
Y Position
Peak Speed Ratio
500
D
Robot Movement
Subject Movement
Hand Speed
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C
0
Initial Direction Error
Response Latency
X Position
Time
Figure 1. A, Overhead view of the kinesthetic matching task showing the spatial trajectories of an exemplar control for all movements
performed in a single direction. B, Hand speed profiles corresponding to the spatial trajectories. C, Schematic of temporal movement
parameters depicting response latency (RL) and peak speed ratio (PSR). D, Schematic of spatial movement parameters illustrating initial
direction error (IDE) and path length ratio (PLR)*.
acknowledge that the robot had moved their limb (a nonmovement as described in Methods section). Although 95%
of control subjects made ≤1 non-movement error, 42% of
LA (n=26; 95% CI; P<0.05) and 29% of RA (n=15; 95% CI;
P<0.05) subjects with stroke had >1 non-movement error.
In fact, 15% of LA (n=9; 95% CI; P<0.05) and 14% of RA
(n=7; 95% CI; P<0.05) failed to move in >25% of trials. Nonmovement trials were discarded from further analysis.
Response Latency
Compared with controls, many subjects with stroke had difficulty quickly and consistently initiating movements in
response to the movement of the robot. In our exemplar control
subject (Figure 1B), we see that this subject regularly initiated
movement of the opposite arm quickly after the robot began to
move (mean: RL, 314 ms; RLv, 148 ms). Exemplar data from
a stroke subject (Figure 2A) show significant difficulty with
initiation of matching movements and high variability in RL
(mean: RL, 1288 ms; RLv, 613 ms). Forty-seven percent of
LA (n=29; median, 817 ms) and 20% of RA (n=10; median,
500 ms) subjects with stroke fell outside the normative control
range for RL (95% range, <893 ms; Figure 2C). Variability
of RL (RLv; Figure IIA in the online-only Data Supplement)
also showed that 42% of LA (n=26; median, 355 ms) and 22%
of RA (n=11; median, 237 ms) strokes fell significantly outside of the normative control range (95% range, <414 ms).
Peak Speed Ratio
Many subjects with stroke had difficulty modulating their
active arm speed to match the speed of the passive arm. The
exemplar control (Figure 1B) demonstrated good speed matching with a PSR close to 1 (mean PSR, 0.90) and low variability
(mean PSRv, 0.13). In Figure 2A, a LA exemplar subject with
stroke displayed low PSR (mean, 0.66), with high variability
(PSRv; mean, 0.36). Similarly, a RA exemplar (Figure 2B)
subject with stroke displayed high PSR (mean, 1.69), with
high variability (PSRv; mean, 0.42). Thirty-two percent of LA
(n=20; median, 1.02) and 27% of RA (n=14; median, 1.11)
subjects with stroke had difficulty matching speed, with values significantly outside the normative control range for PSR
(95% range, 0.80–1.40; Figure 2D). Additionally, many subjects with stroke demonstrated high variability in matching the
Semrau et al Kinesthetic Deficits Poststroke 3417
A
Exemplar Left Affected Stroke − Hand Speed
B
0.5
Exemplar Right Affected Stroke − Hand Speed
0.5
Robot − Median
LA Stroke − Median
LA Stroke − Individual Movements
Robot − Median
RA Stroke − Median
RA Stroke − Individual Movements
0.4
Hand Speed (m/s)
Hand Speed (m/s)
0.4
0.3
0.2
0.3
0.2
0.1
0.1
0
500
1000
1500
2000
2500
Time (ms)
3000
3500
0
4000
D
Response Latency
100
0
500
90
80
70
70
Control
Stroke − LA
Stroke − RA
50
30
30
20
20
10
10
500
1000
1500
2000
3000
3500
4000
Control
Stroke − LA
Stroke − RA
50
40
0
2000
2500
Time (ms)
60
40
0
1500
Peak Speed Matching
80
60
1000
100
90
Percent
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Percent
C
0
0
0
0.5
Response Latency (ms)
1
1.5
2
2.5
Peak Speed Ratio
Figure 2. A and B, Hand speed profiles of exemplar left-affected (LA; A) and right affected (RA; B) subjects with stroke. C and D, Group
results presented as a cumulative sum histogram for response latency (C) and peak speed ratio (D). Grey rectangles show the normative
reference ranges for 95% of control subjects.
speed of the passive arm (PSRv), with 44% of LA (n=27) and
39% of RA (n=20) falling significantly outside the normative
control range (95% range, <0.37; Figure IIB in the onlineonly Data Supplement).
(n=31; median, 23.2º) and 26% of RA strokes (n=13; median,
16.4º) fell significantly outside the control range for IDEv
(95% range, <23.2º; median, 12.0º; Figure IIC in the onlineonly Data Supplement).
Initial Direction Error
Path Length Ratio
Compared with controls, subjects with stroke commonly failed
to match the direction accurately or consistently of the passive movement generated by the robot. Exemplar control data
are shown in Figure 1A, where the subject accurately mirrormatched the direction the robot had moved their left arm (mean
IDE, 15.9º; mean IDEv, 14.9º). In contrast, exemplar stroke subjects displayed high IDE (Figure 3A; mean, 54.9º; Figure 3B;
mean, 62.3º) and IDEv (Figure 3A; mean, 55.7º; Figure 3B;
mean, 45.6º), suggesting impaired sense of direction.
We found that the majority of subjects with stroke (69% LA
[n=43]; median, 28.0º; 49% RA [n=25]; median, 22.1º) made
significantly larger IDE errors than 95% of the controls (95%
range, <22.4º; median, 14.8º). In addition to making larger
IDE errors, subjects with stroke were also more variable about
the IDE errors they made (IDEv). Fifty percent of LA strokes
We measured PLR as a function of subjects’ ability to match
the distance the robot moved the passive arm. The exemplar
control (Figure 1A) accurately and consistently matched the
length of the robot movement (PLR mean, 1.05; PLRv mean,
0.12). In contrast, the LA exemplar stroke subject (Figure 3A)
had difficulty matching PLR with movements that were too
long (PLR mean, 1.64) and highly variable (PLRv mean,
0.50) compared with the passive movement of the robot. We
established a normative control range for PLR (95% range,
0.86–1.23; median, 1.06), where values below this range indicated a shorter movement distance and values above indicated
a longer movement distance. Many subjects with stroke displayed abnormal PLR matching, where 42% of LA (n=26;
median, 1.13) and 31% of RA (n=16; median, 1.10) subjects
with stroke fell significantly outside of the normative range
3418 Stroke December 2013
Table 1. Subject Demographics
Stroke
Control (n=74)
Left-Affected (n=62)
Age
61 (18–88)
64 (24–89)
Right-Affected (n=51)
Sex
37 F, 37 M
17 F, 45 M
22 F, 29 M
Dominant hand
44 R, 3 L, 4 A
63 (18–89)
64 R, 10 L
58 R, 1 L, 3 A
Visual field deficit
…
n=8
Type of stroke
…
Days since stroke
…
11 (1–48)
9 (2–51)
TLT [0, 1, 2, 3]*
…
[33, 19, 8, 2]
[32, 11, 5, 3]
50 I, 12 H
n=3
44 I, 7 H
CMSA [1, 2, 3, 4, 5, 6, 7]†
Affected arm
…
[5, 5, 5, 2 9, 7, 28]
[5, 3, 6, 3, 10, 7, 17]
Unaffected arm
…
[0, 0, 0, 0, 2, 7, 52]
[0, 0, 0, 0, 0, 3, 48]
Affected hand
…
[5, 4, 3, 4, 17, 15, 13]
[5, 3, 6, 3, 11, 10, 13]
Unaffected hand
…
[0, 0, 0, 0, 0, 20, 41]
[0, 0, 0, 0, 2, 16, 33]
5.0 (0–16.5)
CMSA [1, 2, 3, 4, 5, 6, 7]
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PPB
Affected side
…
5.5 (0–13)
Unaffected side
…
10.5 (4–16)
FIM (total score)
…
109 (54–126)
106 (61–126)
FIM (motor)
…
80 (25–91)
78 (30–91)
10 (4.5–15.5)
A indicates ambidextrous; CMSA, Chedoke–McMaster Stroke Assessment; F, female; FIM, Functional
Independence Measure; H, hemorrhagic; I, ischemic; L, left; M, male; PPB, Purdue Peg Board; R, right; and
TLT, Thumb Localizing Test.
*TLT scores of 0 are considered normal, 3 are markedly abnormal.
†CMSA scores of 7 are considered normal, 1 is markedly abnormal.
of controls (Figure 3D). PLRv (Figure 3B; Figure IID in the
online-only Data Supplement) showed that, in addition to poor
PLR matching, both LA (56%; n=35; median, 0.34) and RA
(37%; n=19; median, 0.26) subjects with stroke were more
variable about the distance they moved to match movements
(control: 95% range, <0.30; median, 0.18).
Parameter Summary
Figure 4 shows a summary of the number of parameters that
failed as a function of subject group. Across the 8 parameters
described previously (IDE, IDEv, PLR, PLRv, RL, RLv, PSR,
PSRv), only 5% of control subjects were outside the normative
reference range on >2 parameters. In contrast, 71% of LA (n=44)
and 49% of RA (n=25) subjects with stroke failed ≥3 parameters.
Clinical and Anatomic Significance
We found that several of our kinesthestic parameters were
significantly related to clinical measures. For subjects with
stroke, nearly all parameters were significantly related to
FIM and FIM motor subscore measures (Table 2; P<0.0063).
Furthermore, IDE and IDEv were significant predictors of
poor performance on almost all clinical measures evaluated
(FIM, FIM motor subscore, PPB, CMSA arm, CMSA hand
[IDE only], and TLT [P<0.0063]).
Additionally, we tested whether there was a difference in
the mean scores on all clinical measures for subjects that
failed the kinesthesia task versus those who did not. We found
that subjects that failed kinesthesia performed significantly
worse on clinical tests compared with subjects that passed
kinesthesia (1-way ANOVA; FIM total: pass kinesthesia
[mean±SD], 115.25±12.55; fail kinesthesia, 96.19±20.70;
P<0.0083; F(1,110)=30.18; FIM motor: pass kinesthesia, 84.05±9.93; fail kinesthesia, 66.03±19.43; P<0.0083;
F(1,110)=32.36; PPB: pass kinesthesia, 7.68±4.44; fail
kinesthesia, 3.68±3.82; P<0.0083; F(1,110)=25.99; CMSA
arm: pass kinesthesia, 6.20±1.23; fail kinesthesia, 4.41±2.19;
P<0.0083; F(1,110)=24.37; CMSA hand: pass kinesthesia, 5.89±1.33; fail kinesthesia, 4.29±1.93; P<0.0083;
F(1,110)=22.81; TLT: pass kinesthesia, 0.36±0.65; fail kinesthesia, 0.80±0.93; P<0.0083; F(1,110)=7.25; Bonferroni
correction, α=0.05; n=6).
Anatomically, we observed that the majority of our stroke
subjects experienced cortical, subcortical, or combined cortical and subcortical strokes (Table I in the online-only Data
Supplement). Additionally, the majority of subjects in our
LA and RA subject groups had middle cerebral artery strokes
(LA, n=41; RA, n=35; Table II in the online-only Data
Supplement). We observed significantly impaired kinesthesia
in 76% of right middle cerebral artery strokes and 49% of left
middle cerebral artery strokes.
Discussion
We found that many subjects with stroke had significantly
impaired kinesthesia. Across the 8 parameters examined, 20%
to 69% of stroke subjects were impaired on any individual
parameter and 61% (n=69) were impaired on >3 parameters.
Semrau et al Kinesthetic Deficits Poststroke 3419
A
Exemplar Left Affected Stroke - Hand Position
B Exemplar Right Affected Stroke - Hand Position
5 cm
Robot − Median
LA Stroke − Median
LA Stroke − Individual Movements
Mean Initial Direction Error
C
Path Length Matching
D
100
90
90
80
80
70
70
60
Percent
100
Control
Stroke − LA
Stroke − RA
50
40
60
40
30
20
20
10
10
0
0
20
40
60
80
100
Control
Stroke − LA
Stroke − RA
50
30
120
Initial Direction Error (deg)
0
0
0.5
1
1.5
2
2.5
Path Length Ratio
Figure 3. A and B, Spatial trajectories of exemplar left-affected (LA; A) and right affected (RA; B) subjects with stroke. C and D, Group results
for initial direction error (C) and path length ratio (D). Grey rectangles show the normative reference ranges for 95% of control subjects.
Many of the parameters measured demonstrated significant
correlations with clinical measures.
Robotic Quantification of Kinesthesia
Implementing robotic technology for proprioceptive and
motor assessments poststroke could benefit rehabilitation in
several ways.13 Objective, quantitative, and sensitive measures
Number of Parameters Failed by Group
100
Control
Stroke LA
Stroke RA
90
80
Percent of Subjects
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Percent
Robot − Median
RA Stroke − Median
RA Stroke − Individual Movements
70
60
50
40
30
20
10
0
0
1
2
3
4
5
6
7
Number of Parameters Failed
8
Figure 4. Summary figure depicting the number of parameters
failed (out of 8) as a function of group percentage. Normative cut
off for control behavior was ≤2 parameters, with 95% of control
subjects falling within this range. LA indicates left-affected; and
RA, right-affected.
should allow for a more accurate identification of impairments. In turn, this could allow for better tailoring of interventions to the individual and, ultimately, improved outcomes.
Furthermore, better assessment tools could potentially result
in more accurate prognostication around recovery.
It is important to highlight that we evaluated active movements of the less affected arm in all our stroke subjects. Using
this methodology, subjects relied on sensation derived from
the stroke-affected arm to signal and drive movement of the
opposite arm. This technique has been modified from a widely
accepted technique for examining position sense, known as
position matching.4,7,20 We are currently unaware of other published quantitative measures to assess kinesthesia after stroke.
The technique itself is not without limitations. In a previous
study,16 we examined visually guided reaching in individuals
with stroke. In that study, ≈20% of stroke subjects displayed
deficits in ipsilesional reaching for a number of robotic parameters, with ≈30% having deficits in IDE. Additionally, in that
study ≈20% of patients had ipsilesional motor deficits as measured by CMSA, whereas our sample had a smaller percentage (11%) of stroke subjects with mild motor impairments
(CMSA, 5 or 6) of the ipsilesional arm. These individuals
may have had difficulty with the present task because of motor
impairment. However, when we removed these subjects from
our data analyses and recalculated percent impairment in each
parameter (Figures 2C, 2D, 3C, and 3D), we saw little change
3420 Stroke December 2013
Table 2. Relationship of Kinesthetic Parameters to Clinical Scores
Parameters
IDE
IDEv
PLR
PLRv
RL
RLv
PSR
PSRv
FIM (total)
0.229*
0.171*
0.008
0.220*
0.107*
0.133*
0.021
0.109*
FIM (motor)
0.190*
0.139*
0.012
0.217*
0.105*
0.126*
0.010
0.127*
Clinical Score
PPB
0.144*
0.112*
0.007
0.184*
0.052
0.053
0.004
0.091*
CMSA arm
0.195*
0.097*
0.017
0.170*
0.015
0.017
<0.001
0.126*
CMSA hand
0.163*
0.080
0.026
0.185*
0.010
0.010
<0.001
TLT
0.130*
0.112*
0.085*
0.015
0.069*
0.032
0.061
0.134*
<0.001
Linear regressions of clinical scores to kinesthetic robotic measures. Values indicate r2 value. CMSA indicates Chedoke–McMaster Stroke Assessment; FIM, Functional
Independence Measure; IDE, initial direction error; IDEv, IDE variability; PLR, path length ratio; PLRv, PLR variability; PPB, Purdue Peg Board; PSR, peak speed ratio; PSRv,
PSR variability; R, right; RL, response latency; RLv, RL variability; and TLT, Thumb Localizing Test.
*P<0.0063 (Bonferroni correction, α=0.05; n=8).
Downloaded from http://stroke.ahajournals.org/ by guest on June 18, 2017
across all parameters (≈3% change, on average). This would
suggest that the effects of mild ipsilesional motor impairments
on kinesthesia are negligible. Furthermore, it is possible that
individuals with lesions disrupting interhemispheric transfer
of sensory information may perform abnormally on the task,
despite having intact sensory tracts on the stroke-affected side
of the brain. However, we suspect this occurs in a minority
of individuals. The presence of ipsilesional motor deficits in
some of our subjects is not surprising given previous work
characterizing hemispheric specialization for the execution of
ipsilesional movements in stroke.21
We also observed that 20% of stroke subjects had difficulty
sensing that their arm had moved in ≥25% of the trials in the
task. Although it is notable that this resulted in a lesser amount
of data in a small subset of our subjects, it is more important to
note that this is indicative of significantly impaired kinesthesia poststroke. In agreement with this, 100% of these subjects
failed on ≥3 parameters of kinesthesia.
Relationship of Robotic Measures to
Clinical Scores
In general, subjects with stroke (Table 1) presented with
clinical evidence of mild to moderate sensory (TLT), motor
(CMSA, PPB), and functional (FIM) deficits. Although these
assessments do not specifically examine deficits in kinesthesia, many subjects with stroke displayed significant problems
with kinesthesia that may have contributed to their poor sensorimotor function.
We found that these robotic scores were not only useful in
identifying kinesthetic deficits, but many were also significantly related with clinical measures, including the FIM and
CMSA (Table 2). Notably, impaired sense of direction (IDE)
for LA and RA stroke and its variability (IDEv) for LA stroke
were related to nearly all clinical measures. These relationships are not surprising given that sense of direction is a key
component of kinesthesia and is necessary for the proper planning and execution of movement. Impaired sense of direction and other kinesthetic parameters may directly contribute
to poor scores on clinical measures by looking like motor
impairments. The correlations that we see are likely because
of the contribution of significant sensory deficits to the overall
level of functional impairment seen in individuals with stroke,
rather than just pure motor deficit.
Differences in LA and RA Strokes
Overall, we did not observe any significant differences in the
severity of stroke between the 2 groups as measured by FIM,
CMSA, and PPB. However, robotic measures of kinesthesia
demonstrated a number of differences between LA and RA
subjects with stroke. Overall, 17% more LA subjects exhibited impaired kinesthesia compared with RA subjects. This
finding is potentially consistent with previous literature suggesting right hemispheric dominance for the processing of
kinesthetic information.4,16,22
Clinical Use of Measuring Kinesthestic Deficits
We observed that 61% of stroke survivors we tested had significant deficits in kinesthesia. This high percentage of subjects
with kinesthetic deficits stresses the necessity for addressing proprioceptive deficits in a clinical setting. Our clinical
experience has been that it is possible for proprioceptive deficits to be mistaken for motor deficits. A better understanding of the relative contribution of sensory and motor deficits
to functional impairment after stroke is critical to developing
a comprehensive treatment plan aimed at maximizing stroke
recovery and functional outcomes.
As improved technology for assessment and robotics
become more common in stroke rehabilitation centers, the
possibility of implementing an assessment of kinesthesia,
such as the one described here, becomes more achievable.
However, the challenge remains to develop a treatment that
specifically addresses kinesthetic deficits. Recent reviews of
sensory retraining in stroke3,23 have not identified any treatment specifically targeted at kinesthesia. This is not surprising because, to date, no tools to identify and monitor patients
accurately with kinesthetic deficits have existed. In a treatment setting, once kinesthetic deficits have been identified it is
possible that existing strategies that are thought to aid sensory
function, such as thermal24 or electric stimulation,25 could be
attempted to determine if they benefit recovery. Additionally,
we know that motor performance after stroke improves substantially with the implementation of repetitive motor training
paradigms26; so, repetitive training on kinesthetically focused
tasks might be a potential treatment strategy. It is likely that
this model be used to develop similar kinesthetic training paradigms to improve kinesthetic function after stroke.
Semrau et al Kinesthetic Deficits Poststroke 3421
Conclusions
We have developed a novel robotic assessment tool to quantify kinesthesia after stroke. Our robotic assessment found that
many stroke subjects are significantly impaired in kinesthesia.
Improved assessment and identification of sensory deficits in
stroke may be fundamental to improving poststroke rehabilitation and recovery.
Acknowledgments
We acknowledge the assistance of Janice Yajure, Megan Metzler,
Helen Bretzke, Kim Moore, and Justin Peterson.
Sources of Funding
This project was funded through a Canadian Institutes of Health
Research operating grant (MOP 106662), a Heart and Stroke
Foundation of Alberta, Northwest Territories and Nunavut Grant-inAid, and an Ontario Research Fund grant (ORF-RE 04-47).
Downloaded from http://stroke.ahajournals.org/ by guest on June 18, 2017
Disclosures
Dr Scott is cofounder and chief scientific officer of BKIN
Technologies, which commercializes the KINARM robotic
technology.
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Robotic Identification of Kinesthetic Deficits After Stroke
Jennifer A. Semrau, Troy M. Herter, Stephen H. Scott and Sean P. Dukelow
Downloaded from http://stroke.ahajournals.org/ by guest on June 18, 2017
Stroke. 2013;44:3414-3421; originally published online November 5, 2013;
doi: 10.1161/STROKEAHA.113.002058
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ONLINE SUPPLEMENT
TITLE: Robotic identification of kinaesthetic deficits following stroke
COVER TITLE: Robotics for kinaesthesia post-stroke
AUTHORS: 1,2Jennifer A. Semrau, PhD, 1,2,3,6Troy M. Herter, PhD, 3,4,5Stephen H. Scott, PhD,
1,2
Sean P. Dukelow, MD, PhD
1
Hotchkiss Brain Institute, 2Department of Clinical Neurosciences, University of Calgary,
Calgary, Alberta, Canada; 3Centre for Neuroscience Studies, 4Department of Anatomy and Cell
Biology, 5School of Medicine, Queen’s University, Kingston, Ontario, Canada; 6Department of
Exercise Science, University of South Carolina, Columbia, South Carolina, USA.
ADDRESS: 1403 29th St NW
Foothills Medical Centre
South Tower – Room 905
Calgary, AB, T2N 2T9, Canada
CORRESPONDENCE:
Email: [email protected]
Telephone: (403) 944 - 5930
Fax: (403) 944 – 0977
KEYWORDS: Stroke, kinaesthesia, proprioception, reaching
FIGS/TABLES:
Supplemental Figure I: Picture of the KINARM robotic exoskeleton
Supplemental Figure II: Group data demonstrating variability of behaviour of subjects with
stroke across all four kinaesthetic parameters.
Supplemental Table I: Number of stroke patients who fail the Kinaesthesia Task and
Parameters by Lesion Location
Supplemental Table II: Number of subjects who fail the Kinaesthesia Task and Individual
Parameters based on Vascular Territory of Stroke
Supplemental Figure I: Picture of the KINARM robotic exoskeleton
A
100
90
80
80
Percent
60
50
50
40
30
30
20
20
10
10
0
500
1000
Response Latency Variability (ms)
C
100
90
90
80
80
70
70
60
60
50
Control
Stroke − LA
Stroke − RA
40
30
20
10
10
10
20
30
40
50
60
Initial Direction Error Variability (deg)
0.4
0.6
0.8
70
0
1.0
Peak Speed Ratio Variability
1.2
Path Length Matching Variability
Control
Stroke − LA
Stroke − RA
40
20
0
0.2
50
30
0
0
D
Initial Direction Error Variability
100
0
1500
Control
Stroke − LA
Stroke − RA
60
40
0
Peak Speed Matching Variability
70
Control
Stroke − LA
Stroke − RA
Percent
Percent
100
90
70
Percent
B
Response Latency Variability
0
0.5
1.0
Path Length Ratio Variability
1.5
Supplemental Figure II: Cumulative sum histograms depicting variability for each of the 4
kinaesthetic parameters (Fig. 3A and B, Fig. 4A and B). Grey areas on each plot indicate the
normative range (95% CI). We observed that stroke subjects displayed significantly increased
variability across all 4 parameters as compared to controls.
Cortical
LA (17)
RA (18)
Cortical +
Subcortical
LA (19)
RA (9)
Subcortical
LA (17)
RA (19)
Cerebellar
LA (2)
RA (0)
Brainstem +
Cerebellar
LA (1)
RA (0)
Brainstem
LA (3)
RA (4)
Other*
LA (4)
RA (0)
IDEv
41% (7)
11% (2)
63% (12)
33% (3)
47% (8)
32% (6)
0% (0)
--100% (1)
--33% (1)
25% (1)
75% (3)
---
IDE
71% (12)
39% (7)
74% (14)
56% (5)
59% (10)
53% (10)
50% (1)
---
100% (1)
---
100% (3)
50% (2)
75% (3)
---
Kinaesthetic Parameter
50% (2)
---
33% (1)
25% (1)
100% (1)
---
50% (1)
---
41% (7)
32% (6)
53% (10)
22% (2)
29% (5)
33% (6)
PLR
75% (3)
---
66% (2)
75% (3)
0% (0)
---
0% (0)
---
53% (9)
37% (7)
74% (14)
22% (2)
47% (8)
33% (6)
PLRv
75% (3)
---
33% (1)
0% (0)
0% (0)
---
50% (1)
---
47% (8)
21% (4)
47% (9)
33% (3)
47% (8)
11% (2)
RL
50% (2)
---
33% (1)
25% (1)
100% (1)
---
50% (1)
---
41% (7)
21% (4)
47% (9)
22% (2)
35% (6)
17% (3)
RLv
Table SI. Number of stroke patients who fail the Kinaesthesia Task and Parameters by Lesion
Location
25% (1)
---
33% (1)
0% (0)
100% (1)
---
0% (0)
---
29% (5)
37% (7)
32% (6)
11% (1)
41% (7)
28% (5)
PSR
25% (1)
---
33% (1)
50% (2)
0% (0)
---
100% (2)
---
41% (7)
58% (11)
53% (10)
11% (1)
35% (6)
33% (6)
PSRv
75% (3)
---
66% (2)
50% (2)
100% (1)
---
50% (1)
---
65% (11)
58% (11)
84% (16)
33% (3)
65% (11)
44% (8)
Kinaesthesia
Task
Abbreviations: LA, Left Affected; RA, Right Affected; IDE, Initial Direction Error; IDEv, Initial
Direction Error Variability; PLR, Path Length Ratio; PLRv, Path Length Ratio Variability; RL,
Response Latency; RLv, Response Latency Variability; PSR, Peak Speed Ratio; PSRv, Peak
Speed Ratio Variability. *Four subjects did not fall into the above lesion location categories and
were categorized as "Other". For two, initial acute imaging (performed within the first 24 hours
of stroke) was inconclusive and while the treating stroke neurologist confirmed the clinical
diagnosis of stroke, they did not obtain a follow-up scan. Further, one subject experienced a
posterior spinal artery stroke at spinal level C1 and one was diagnosed with a sinus thrombosis.
ACA
LA (2)
RA (3)
MCA
LA (41)
RA (35)
PCA
LA (8)
RA (4)
ICA
LA (2)
RA (0)
BA
LA (2)
RA (3)
PICA
LA (3)
RA (0)
PA
LA (0)
RA (3)
VA
LA (1)
RA (2)
Other*
LA (4)
RA (0)
IDEv
0% (0)
33% (1)
51% (21)
17% (6)
50% (4)
50% (2)
50% (1)
--0% (0)
66% (2)
33% (1)
----33% (1)
100% (1)
0% (0)
100% (4)
---
IDE
50% (1)
66% (2)
68% (28)
40% (14)
63% (5)
75% (3)
50% (1)
---
100% (2)
100% (3)
66% (2)
---
--33% (1)
100% (1)
50% (1)
100% (4)
---
Kinaesthetic Parameter
50% (2)
---
0% (0)
0% (0)
--66% (2)
66% (2)
---
0% (0)
33% (1)
0% (0)
---
50% (4)
25% (1)
44% (18)
26% (10)
0% (0)
33% (1)
PLR
100% (4)
---
100% (1)
0% (0)
--66% (2)
0% (0)
---
50% (1)
100% (3)
50% (1)
---
38% (3)
25% (1)
63% (26)
26% (10)
0% (0)
66% (2)
PLRv
50% (2)
---
100% (1)
0% (0)
--0% (0)
33% (1)
---
0% (0)
0% (0)
100% (2)
---
25% (2)
25% (1)
51% (21)
23% (8)
50% (1)
0% (0)
RL
50% (2)
---
100% (1)
0% (0)
--0% (0)
66% (2)
---
0% (0)
66% (3)
100% (2)
---
25% (2)
0% (0)
44% (18)
20% (7)
0% (0)
33% (1)
RLv
25% (1)
---
0% (0)
0% (0)
--33% (1)
33% (1)
---
0% (0)
0% (0)
0% (0)
---
63% (5)
0% (0)
31% (13)
3% (1)
50% (1)
33% (1)
PSR
Table SII. Number of subjects who fail the Kinaesthesia Task and Individual Parameters based
on Vascular Territory of Stroke
75% (3)
---
0% (0)
0% (0)
--33% (1)
66% (2)
---
50% (1)
100% (3)
50% (1)
---
13% (1)
25% (1)
49% (20)
37% (13)
0% (0)
66% (2)
PSRv
100% (4)
---
100% (1)
0% (0)
--33% (1)
66% (2)
---
50% (1)
100% (3)
50% (1)
---
63% (5)
25% (1)
76% (31)
49% (17)
0% (0)
66% (2)
Kinaesthesia
Task
Abbreviations: ACA, Anterior Cerebral Artery; MCA, Middle Cerebral Artery; PCA, Posterior
Cerebral Artery; ICA, Internal Carotid Artery; BA, Basilar Artery; PICA, Posterior Inferior
Cerebellar Artery; PA, Pontine Artery; VA, Vertebral Artery; LA, Left Affected; RA, Right
Affected; IDE, Initial Direction Error; IDEv, Initial Direction Error Variability; PLR, Path
Length Ratio; PLRv, Path Length Ratio Variability; RL, Response Latency; RLv, Response
Latency Variability; PSR, Peak Speed Ratio; PSRv, Peak Speed Ratio Variability. *Four subjects
did not fall into the above lesion location categories and were categorized as "Other". For two,
initial acute imaging (performed within the first 24 hours of stroke) was inconclusive and while
the treating stroke neurologist confirmed the clinical diagnosis of stroke, they did not obtain a
follow-up scan. Further, one subject experienced a posterior spinal artery stroke at spinal level
C1 and one was diagnosed with a sinus thrombosis.