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643
JRRD
JRRD
Volume 43, Number 5, Pages 643–656
August/September 2006
Journal of Rehabilitation Research & Development
Custom-designed haptic training for restoring reaching ability
to individuals with poststroke hemiparesis
James L. Patton, PhD;
1–2
*
Mark Kovic, BS, COTA/L;
1
Ferdinando A. Mussa-Ivaldi, PhD;
1–3
1
Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL;
2
Departments of Physical
Medicine and Rehabilitation, Mechanical Engineering, and Biomedical Engineering and
3
Department of Physiology
and the Institute for Neuroscience, Northwestern University, Chicago, IL
Abstract—We present an initial test of a technique for retrain-
ing reaching skills in patients with poststroke hemiparesis, in
which errors are temporarily magnified to encourage learning
and compensation. Individuals with poststroke hemiparesis
held a horizontal plane robotic manipulandum that could exert
a variety of forces while recording patients’ movements. We
measured how well the patients recovered movement straight
-
ness in a single visit to the laboratory (~3 h). Following train-
ing, we returned forces to zero for an additional 50 movements
to discern if aftereffects lasted. We found that all subjects
showed immediate benefit from the training, although 3 of the
10 subjects did not retain these benefits for the remainder of
the experiment. We discuss how these approaches demonstrate
great potential for rehabilitation tools that augment error to
facilitate functional recovery.
Key words: adaptation, control, cortex, force fields, haptics,
hemiparesis, human, human-machine interface, impairment,
lesion, motor learning, rehabilitation, robots, stroke, teaching.
INTRODUCTION
The range of robotic possibilities for teaching and
rehabilitation has yet to be established, but the options do
go beyond what a therapist can do—robots are precise,
tireless devices that can measure progress with high
accuracy. We have been focusing on robotic forces that
may facilitate recovery from brain injuries such as stroke.
Some conventional therapeutic interventions use guid
-
ance and resistance principles to promote motor recovery
in the hemiparetic upper limb. Some traditional rehabili
-
tation sources recommend therapeutic intervention that
eliminates unwanted muscle activity and muscle tone and
then introduces normal movement patterns, which may
facilitate rehabilitation [1]. Other theories suggest that
facilitating reaching patterns promotes improvements in
motor function. One component of this approach is the
use of resistance in a direction opposite the movement
[2]. However, the most effective rehabilitation algorithms
have yet to be determined, which leaves a fertile area for
scientific inquiry.
Interestingly, several researchers are exploring
robotic techniques that are not necessarily designed to
imitate the conventional therapeutic process but to
instead uniquely probe new capabilities. For example,
one possible technique is to have the robot guide (pull)
the hand toward the desired trajectory and have the guid
-
ance transition to resistance as recovery progresses [3–4].
Another technique for hemiparetic stroke patients is pro
-
viding the patient with a bimanual master-slave robot
system, which guides the paretic arm by the actions of
Abbreviations: DOF = degrees of freedom, FM = Fugl-Meyer.
*
Address all correspondence to James L. Patton, PhD; Sen-
sory Motor Performance Program, Rehabilitation Institute
of Chicago, 345 East Superior St, Room 1406, Chicago, IL
60611; 312-238-1232; fax: 312-238-2208.
Email: j-patton@northwestern.edu
DOI: 10.1682/JRRD.2005.05.0088
644
JRRD, Volume 43, Number 5, 2006
the patient’s nonparetic limb [4]. Still another technique
is to provide force biofeedback during movement along a
constraining rail, which encourages the patient to push in
the appropriate direction [4–6]. Others have also
attempted sophisticated virtual reality techniques [7–15].
Our research and the work of others suggest that one
promising novel approach may be adaptive training [16–
20]. In this technique, we use the natural adaptive tenden
-
cies of the nervous system to facilitate motor recovery.
Motor adaptation studies have demonstrated that when peo
-
ple are repeatedly exposed to a force field that systemati-
cally disturbs arm motion, subjects learn to anticipate and
cancel out the forces and recover their original kinematic
patterns. After the disturbing force field is unexpectedly
removed, the subjects make erroneous movements in direc
-
tions opposite the perturbing forces (aftereffects). This
technique has recently been shown to alter and hasten the
learning process in nondisabled individuals [19,21].
*
Motor adaptation and its related aftereffects have been
demonstrated by many investigators under many condi
-
tions, ranging from simple position-, velocity-, and accel-
eration-dependent force fields [22–26] to Coriolis forces
caused by movement in a rotating room [27] and skew-
symmetric “curl” fields that produce forces in a direction
perpendicular to the velocity of the hand [25]. Similar
results have also been observed after manipulations of sub
-
jects’ visual perception that altered the visual feedback of
movement [28–31]. Recent results support the view that
subjects adapt by learning the appropriate internal model
of the perturbation rather than learning a temporal
sequence of muscle activations [25–26]. The most encour
-
aging result is that engineering techniques have been suc-
cessful in predicting both how the arm is disturbed by a
force field and the aftereffects of training [24,32–33]. Con
-
sequently, one possible rehabilitation method may be for
investigators to reverse-engineer the adaptation process by
using the models to design an appropriate force field that
will eventually result in the desired aftereffect.
Adaptive training will only work, however, if stroke
patients can adapt. Several studies have demonstrated
that at least a large subpopulation of stroke patients retain
their ability to adapt to a force field [16–20] or other dis
-
turbances [34–37]. However, severely affected individu-
als used atypical correction strategies [18], and the
amount of adaptation in individuals with more severe
impairment is somewhat diminished compared with non
-
disabled individuals [20]. Our recent work agrees with
these findings [38]. Furthermore, our preliminary studies
on stroke patients have revealed that aftereffects may
persist longer when the aftereffects resemble nondisabled
unperturbed movement [16–17,39].
We focused on adaptive training by using robot-applied
forces to restore function to hemiparetic stroke patients.
This investigation is a pilot study for determining the poten
-
tial of this approach to rehabilitation. We simply applied a
technique to stroke patients that has already proven effec
-
tive at causing desired results in nondisabled individuals
[19]. While straightening slightly curved movements is
generally not perceived as the most important clinical goal,
it represents our initial effort at testing the promise of this
approach in a well-known scientific framework. We
addressed two questions. First, we sought to determine
whether adaptation can be exploited for restoring move
-
ment ability. Second, we sought to determine whether the
benefit persists for the duration of the experiment [17].
METHODS
Subjects
Fifteen stroke patients without any other musculoskele-
tal injury volunteered to participate. Their demographic
details are listed in Table 1. The Northwestern University
Internal Review Board approved the research to conform to
ethical standards from the 1964 Declaration of Helsinki and
Federal mandates that protect research subjects. Before
beginning, each subject signed a consent form that con
-
formed to these Northwestern University guidelines. All
stroke participants were in the chronic stage, having suffered
a stroke 19 to 132 months prior to the experiment. Our
exclusion criteria were (1) bilateral impairment; (2) severe
sensory deficits in the limb; (3) aphasia, cognitive impair
-
ment, or affective dysfunction that would influence the abil-
ity to comprehend or to perform the experiment; (4) inability
to provide an informed consent; and (5) other current severe
medical problems. Subjects were randomly assigned to one
of two groups: a treatment group (n = 12) that received cus
-
tom-designed forces for part of the experiment or a control
group (n = 9) that received no forces but otherwise per
-
formed the same experimental protocol. We were able to
have six subjects return on a separate day for a second visit,
so some subjects served as their own controls. The order of
*
Wei Y, Patton JL. Forces that supplement visuomotor learning: A ‘sensory
crossover’ experiment. Exp Brain Res. Unpublished observations, 2006.
645
PATTON et al. Custom-designed haptic training for individuals with poststroke hemiparesis
presentation was randomized. The research therapy staff
was blinded to the type of forces the subjects received and
performed the modified Ashworth scale to assess spasticity
before beginning the experiment. We performed the upper-
limb portion of the Fugl-Meyer (FM) examination before
and after the robotic experiment to assess general changes in
motor capability.
Apparatus
Subjects held the free limb (here referred to as the “end-
point”) of a 2 degree-of-freedom DOF robot (Figure 1)
described elsewhere [26,40]. Endpoint forces and torques
were monitored with a 6 DOF load cell that was fixed to the
handle of the robot (ATI Industrial Automation, Inc, Apex,
North Carolina, model F/T Gamma 30/100). The robot was
equipped with position encoders that record the angular
position of the two robotic joints with a resolution exceeding
20'' of rotation (Gurley Precision Instruments, Inc, Troy,
New York, model 25/045-NB17-TA-PPA-QAR1S). The
position, velocity, and acceleration of the handle were
derived from these two position encoders. We used two
torque motors to apply programmed forces to the subjects’
hands (PMI Motor Technologies, Wood Dale, Illinois,
model JR24M4CH). Motion and force data were collected
at 100 Hz. At all times during the experiment, the software
generated an additional set of compensatory torques that
canceled the inertial effects of the robot-arm linkage and
resulted in the feeling of free movement on a slippery sur
-
face when the force field was not present.
Protocol
Subjects were seated so that the starting point of
the
targets was approximately at the center of their theo-
retical workspace, directly anterior from the shoulder
(Figure 1(b)). The experiment involved only the hemip
-
aretic limbs of the stroke subjects, which corresponded to
the dominant limb in 9 of the 11 subjects (Table 1). If
subjects had difficulty reaching the center point, we
adjusted their chair position slightly. To avoid fatigue,
subjects rested their elbow and forearm on a lightweight
frictionless linkage (Figure 1(a)), and they could choose
to rest between movements (subjects rarely rested longer
than a few seconds every hundred movements).
Starting from a point centered in front of the shoulder,
subjects were presented a target at one of two locations
10
cm distant (out and to the left or out and to the right).
Table 1.
Subject information for treatment and control subjects of this study (N = 15).
Subject Sex
Assisted
Vision
Dominant
Hand
Pathology
Affected
Hand
Affected Neural Region
1 Female Yes Right Hemorrhagic stroke Right Left intercerebral hemorrhage
2 Male Yes
*
Right Stroke Left Unknown
3 Male No Right TBI followed by 2 strokes Right Unknown, left CVA
4 Female Yes
*
Right Stroke Left Right frontal cortex
5 Male Yes
†
Right AVM hemorrhage Left Right frontal and parietal lobe
6 Female No Right Hemorrhagic stroke, RIND
1 yr previous
Left Right posterior internal capsule
infarct
7 Female No Right Hemorrhagic stroke Left Right intercerebral hemorrhage
8 Male Yes Right Stroke Left Right thalamus
9 Male Yes
*
Left Stroke Right Left parietal lobe
10 Male Yes Right Stroke Left Right carotid artery, thrombotic
11 Female Yes Left Stroke Right Pons and midbrain
12 Male Yes Right Stroke Left Unknown
13 Male Yes Right Stroke Left Left subcortical lacunar
14 Female No Right Thrombotic stroke Right Left MCA, posterior
15 Male Yes
*
Right Thrombotic stroke with
neurosurgery
Left Right subcortical
*
Vision not assisted for experiment.
†
Bifocals used for experiment.
AVM = arteriovenous malformation, CVA = cerebrovascular accident, MCA = middle cerebral artery, RIND = reversible ischemic neurological deficit, TBI = traumatic
brain injury.
646
JRRD, Volume 43, Number 5, 2006
These two “main” targets were 90° from each other and
formed a “v” pattern that was centered along the paramid
-
line extending from the shoulder (Figure 1(b)). Subjects
were given a cue to return to the center point after they had
either (1) initiated an attempt and 3 seconds had elapsed or
(2) reached and stayed in the target (1 cm radius) for at
least
0.5 seconds. Subjects’ arms were eclipsed by the projection
platform. Hence, they could only see the target and a cursor
that represented the instantaneous location of the hand.
Subjects were neither told about nor shown the desired
movement, although they were instructed to try to move to
the target at the appropriate speed in a straight line. Only
the outward movements were recorded for this experiment.
Additionally, we included two extra “generalization” tar
-
gets that were not practiced but only experienced briefly at
the beginning and end of the experiment. These targets
were only 30° from each other and formed a “v” in the area
between the main targets, also centered on the paramidline
extending from the shoulder. The generalization targets
were used to determine if any learning was carried to move
-
ment directions that were not practiced.
We controlled for a peak speed of 0.288 m/s by giving
subjects feedback at the end of each movement using col
-
ored dots and auditory tones. These cues let subjects know
if
they were going too fast, too slow, or within a range
of
±0.05 m/s. Consequently, subjects’ speeds remained
roughly constant across the entire experiment. Subjects were
instructed to initiate their movements at a self-determined
time after they saw the target appear. To prevent fatigue, we
instructed subjects to rest anytime they chose.
Machine Learning and Force-Field Design
As explained in greater detail elsewhere, a time-
record
of training forces was custom-designed with an
iterative machine-learning algorithm [19]. A machine-
learning phase iteratively determined the forces that shifted a
subject’s movement to the “desired” trajectory,
by
intermittently exerting forces (once every four movements,
randomly presented) and adjusting them based on the
response of the subject. For this experiment, we used a
smooth, minimum-jerk trajectory along a straight line to the
target for
which was believed to resemble a
“healthy” trajectory [41]. For each iteration, a force
was applied to the robot handle in the first 200 ms of the
movement, where
and (for i > 1) was
adjusted from one movement to the next based on the error
between the actual,
and the desired trajectories with
the simple machine-learning rule
Here, the parameter
μ
is a learning rate, which has
been heuristically found to work in the range from 10 to
Figure 1.
Experimental apparatus from (a) side view and (b) top view. (c) Han
d
strap and posture used. Ball handle was on low-friction axle, so it was
free to pivot in horizontal plane. (d) Subject seated at projection
p
latform with movement target displayed. Only target and cursor
representing hand were visible to subject.
x
D
t(),
x
D
t(),
F
D
i
t()
F
D
1
t() 0= F
D
i
t()
xt(),
F
D
i 1+
t() F
D
i
t()
μ
xt() x
D
t()–().+=
647
PATTON et al. Custom-designed haptic training for individuals with poststroke hemiparesis
30 N
•
m
–1
. A
μ
that is too large leads to unstable learning,
and a
μ
that is too small results in a lengthy machine-
learning session. We chose
μ
= 20 N
•
m
–1
for our experi-
ments. The algorithm allowed the subject to initiate the
movement when they chose. Forces initiated when veloc
-
ity exceeded 0.1 m/s or the subject had exited the starting
window. All subjects performed a total of 744 movements
(trials), broken down into the following experimental
phases:
• Unperturbed familiarization: 58 movements (~6.5 min)
for becoming familiar with the system and the task of
moving the manipulandum.
• Unperturbed baseline: 10 movements (~1.5 min) for
establishing a baseline pattern of reaching movements.
• Unperturbed baseline, generalization targets: 10 move-
ments (~1.5 min) for establishing a baseline pattern
of reaching movements on the generalization targets
described previously.
• Machine learning: 200 movements (~25 min) with
forces exerted intermittently and randomly once in
every 4 movements. The robot gradually learned the
average forces necessary to push the subject to the
“desired” trajectory. Note that because these forces
occurred intermittently and randomly, these move-
ments did not lead to adaptation, because any small
amount of adaptation was washed out in the move-
ments between the movements with forces.
• Second unperturbed baseline: 10 movements (~1.5 min)
for determining if the baseline pattern changed.
• Second unperturbed baseline, generalization targets:
10 movements (~1.5 min) for determining if the base-
line pattern changed on the generalization targets.
• Learning: 222 movements (~30 min) with constant
exposure to the training forces. These forces were the
vector opposite of the forces that were determined in
the machine-learning phase.
• Aftereffects catch trials: 80 movements (~10 min)
with random, intermittent removal of the force field
for 1 in 8 of the movements (catch trials) for deter-
mining the aftereffects.
• Aftereffects catch trials, generalization targets: 80 move-
ments (~10 min) with random, intermittent removal of
the force field for 1 in 8 of the movements (catch trials)
for determining the aftereffects.
• Training refresher: 2 movements (~15 s) identical to the
learning phase.
• Washout: 50 movements (~6 min) with no forces
applied.
• Final movements, generalization targets: 10 move-
ments (~2 min) with no forces applied on the generali-
zation targets.
The movements in each direction were divided
equally in each phase. Subjects were also required to take
breaks (approximately 1–2 min) after movements 54,
278, 510, and 682 so they could rest and our data collec
-
tion equipment could be reset. We chose the instance of
these breaks to minimally disrupt the learning process
and provide the subject with a chance to rest. The sub
-
jects in the control group received no forces for the entire
experiment but otherwise experienced the same protocol.
The entire session lasted approximately 3 hours, which
included screening by therapist and pre- and postclinical
measures by the research occupational therapist.
Analysis
We restricted our focus in this study to the early part of
movements for two reasons. First, stroke patients often
make excessively large corrections later in their movements
that may depend on earlier errors [42–43]. Second, we were
primarily interested in the early phase of the movement that
best reflects the operation of a feed-forward controller
based on an internal model of the arm-environment dynam
-
ics. Our measure, the initial direction error, reflected this
early phase of movement by forming a vector from the start
point to 25 percent of the distance to the target (2.5 cm).
This measurement corresponded to approximately the first
200 to 300 ms of a movement that, if no error corrections
existed at the end of the movement, lasted about 1.1 s. Posi
-
tive error corresponded to a counter clockwise rotation from
the actual trajectory to the desired trajectory, and zero corre
-
sponded to a straight line to the target. Initial direction error
was used for testing our hypotheses on the feed-forward
controller and also was found to be highly correlated with
the perpendicular distance measure used in other adaptation
studies [26,44–45]. Even though we were studying the ini
-
tial part of movement, we were nonetheless also curious
about whether “fixing” earlier stages of movement will cor
-
rect (or improve) the latter part of movement.
All hypotheses were tested at an
α
level of 0.05. We
tested for a shift in initial direction from baseline to after
-
effects and for tendency of the aftereffects to disappear in
the washout phase.
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JRRD, Volume 43, Number 5, 2006
RESULTS
For all the movements observed, unperturbed baseline
movements for stroke subjects were approximately twice
as variable as in nondisabled subjects for the same experi
-
mental conditions (Figure 2) [19]. All subjects showed
significant errors in one or more of the movement direc
-
tions in the unperturbed baseline phase before any treat-
ment (Figure 2(a)). Initial errors averaged 17° of initial
direction error. Intermittently in the machine-learning
phase, these errors were pushed by corrective forces that
evolved to eventually shift movements closer to a straight
line (Figure 2(b)). The forces resulting from the machine-
learning phase ranged from 0
to 12 N and differed from
subject to subject.
Subjects then began the learning phase in which the
forces learned by the robot were inverted and applied repeat
-
edly (Figure 2(c)). These training forces tended to amplify
(double) the initial errors we saw in the unperturbed baseline
phase. By the end of the learning phase, however, subjects
had reduced their errors (Figure 2(d)) to a level similar to
their unperturbed baseline phase. Beneficial aftereffects
were observed in some catch trials, where for a single
motion, the forces were removed and the subject was
returned to the “normal” world (Figure 2(e)). Finally, we
tested to see if the effects of the adaptation washed out by
leaving the forces off for 60 more movements. For most of
the subjects, the benefits were retained until the end, as was
the case for the subject shown in Figure 2(g).
However, in this initial investigation, we failed to
detect, from direct analysis of the data whether, as a
group, subjects statistically benefited from training by
having aftereffects lie closer to nondisabled movement.
Several subjects did not appear to adapt for one or both of
the movement directions (Figure 3). We suspected that
this was due to a combination of several factors. First, it
could have been because of a small (or no) effect size.
Second, it could have been because of a large amount of
variability in the data. Third, and most importantly, it
could have been because some subjects’ movements had
no room for improvement.
To test the hypothesis that benefit was difficult to
detect because there was nothing to improve upon, we sin
-
gled out subjects’ movement directions that actually
showed significant error before training (i.e., their baseline
movement errors were significantly different from zero).
This reduced the analysis to 13 movement directions for
the treatment group and 8 movement directions for the
control group (about half the data). By this criterion, data
from two subjects of the treatment group and four subjects
of the control group was not considered further because
their baseline movement errors were not significantly dif
-
ferent from zero (Tables 2 and 3).
Figure 2.
Typical patterns for successive phases of experiment for single stroke subject: (a) unperturbed baseline, (b) late machine learning, (c) early training,
(d) late training, (e) aftereffects, (f) early washout, and (g) late washout. Shown 315° movement direction for clarity only. Desired trajectories are
b
old dotted lines, average trajectories are bold solid lines, individual trajectories are thin lines, and shaded areas indicate running 95% confidence
intervals of ensemble. Note that although dotted tracing indicates an ideal trajectory, only target and cursor representing hand were visible to subject.
649
PATTON et al. Custom-designed haptic training for individuals with poststroke hemiparesis
Surprisingly, all but one of the treatment group move-
ments showed beneficial aftereffects (Figure 3, bars,
average reduction in error of –54%, p < 0.05, t-test on per
-
cent reduction in error). One subject’s error reduction was
300 percent because the training process caused a rotation
of the movement direction that went beyond the goal.
Nevertheless, if this outlying data point was not consid
-
ered, the error reduction for all the remaining subjects was
still significant (p < 0.01). Interestingly, this subject’s
overcorrection snapped back to almost zero error by the
end of the experiment.
The most critical question, however, was whether
these beneficial aftereffects could be retained. While we
chose this initial experiment not to extend our examina
-
tion beyond the single 3-hour visit to the lab oratory
(days or weeks would be necessary to be clinically con
-
vincing), we decided to check if beneficial aftereffects
were retained beyond the normal time that nondisabled
people wash out their aftereffects (about 20 movements).
Similar to the evaluation of aftereffects (Figure 3,
“Change to second baseline”), the final 10 movements of
the 50 movements of the washout phase were also evalu
-
ated for a reduction in error from baseline. We found that
10 of the 13 subjects’ error remained low and some were
even lower than in the aftereffects phase. As a group, this
reduction in error was marginally significant (p < 0.054,
t-test on percent reduction in error, Figure 3, diamond
symbols and bars for “Persistence”).
An additional evaluation was whether subjects could
generalize what they had learned and perform better in
directions that were not practiced. We inspected the
reduction in error movements that subjects made in the
unpracticed generalization-target trials. However, we
found no evidence of benefit either in the aftereffects or
by the end of the experiment (not displayed).
Two important intermediate questions were whether
(1) the machine-learning phase alone had any influence on
movement error and (2) mere practice without forces
might also result in some benefit (tested by the control
group). Both of these questions are related and relevant to
the assertion that a small amount of practice alone might
lead to benefits that could confound results from the
custom-designed force fields [46]. To test this, we first
evaluated the error reduction by the final 15 trials of
machine-learning phase and the corresponding trial num
-
bers for the control group (Figure 3, “Change to second
baseline”). Second, we evaluated the error reduction for
the control group for trials that corresponded to those eval
-
uated for the treatment group (Figure 3, circles). Although
we failed to detect any significant difference between the
control and the treatment groups, the treatment group
showed a significant benefit while the control group did
not (Figure 3, circles vs diamonds).
Figure 3.
Percent improvements from training only for movements in which
error existed before training. Reduction of errors are negative values
and were evaluated at second baseline phase (after end of machine
learning; left two columns of data), at aftereffects phase (after force-
field training; center two columns of data), or at end of experiment
(after forces had been off for 50 movements; right two columns o
f
data). Each subjects’ average improvement for each movement
direction is indicated by symbol: data from treatment group (
◊
) an
d
control groups ({) are separated into columns. 95% and 90%
confidence intervals are indicated as thin and thick vertical shade
d
b
ars behind symbols, respectively, indicating where t-tests showe
d
significant benefit from training. Bars indicate significant benefit fo
r
treatment group in aftereffects trials and marginally significant
p
ersistence of benefit by experiment end. *Indicates significantly
different than zero at 0.05 level. (*)Indicates significantly different
than zero at 0.07 level.
650
JRRD, Volume 43, Number 5, 2006
Finally, the FM clinical scores also improved
slightly in the treatment group. For the treatment group,
FM scores marginally increased an average of 1.6
(Table 2, p = 0.06). No such improvement was seen in
the control group (Table 3, p > 0.27). This mild
improvement was also only loosely correlated to partic
-
ipants’ error reduction (i.e., a Pearson correlation coef-
ficient of 0.21).
Table 2.
Selected characteristics and experimental data for treatment group (n = 12).
Subject
Age
(yr)
Time Since
Stroke (mo)
Height
(m)
Mass
(kg)
Elbow
MAS
*
Fugl-Meyer Upper Extremity
Initial
Significant
Error
†
Movements
Final Mean Δ
Error
‡
Pretreatment
Δ Pre- to
Posttreatment
1
§
30 129 1.60 70.45 — 26 0 — —
2 48 55 1.73 75.45 — 37 2 R –114
3
§
37 132 1.75 71.82 — 52 4 R 15
4 76 19 1.73 75.00 — 44 5 R –143
5
¶
56 94 1.80 106.82 2 43 6 R –58
6 49 54 1.47 60.00 2 32 –1 LR –124
7
¶
51 73 1.60 47.73 2 51 –2 R –37
8 48 26 1.78 84.09 3 15 1 — —
9
§
72 96 1.70 72.72 2 33 1 LR –53
10
¶
53 113 1.85 90.89 2 22 2 L –63
11 40 26 1.65 68.18 4 — — LR –10
12 48 110 1.80 102.27 2 23 0 R 135
Mean 50.7 77.3 1.7 77.1 2 34 1.6 — –45.2
*
Scale 0 to 4.
†
L indicates movement out and to left and R indicates movement out and to right.
‡
Percent of baseline error.
§
Subjects served as their own control and were randomized into treatment group first.
¶
Subjects served as their own control and were randomized into control group first.
MAS = modified Ashworth scale.
Table 3.
Selected characteristics and experimental data for control group (n = 9).
Subject
Age
(yr)
Time Since
Stroke (mo)
Height
(m)
Mass
(kg)
Elbow
MAS
*
Fugl-Meyer Upper Extremity
Initial
Significant
Error
†
Movements
Final Mean Δ
Error
‡
Pretreatment
Δ Pre- to
Posttreatment
1
§
30 131 1.60 70.45 — 23 1 — —
3
§
37 132 1.75 71.82 — 52 0 LR –46
5
¶
56 93 1.80 106.82 2 47 0 L –45
7
¶
51 72 1.60 47.73 2 46 2 R –24
9
§
72 97 1.70 72.72 2 30 1 R –28
10
¶
53 111 1.85 90.89 2 23 1 R –5
13 55 11 1.83 79.09 2 43 0 LR 82
14 57 85 1.63 50.91 3 26 –2 — —
15 46 167 1.80 81.82 — 25 1 — —
Mean 50.8 99.9 1.7 74.7 2.1 35 0.4 — –11.1
*
Scale 0 to 4.
†
L indicates movement out and to left and R indicates movement out and to right.
‡
Percent of baseline error.
§
Subjects served as their own control and were randomized into treatment group first.
¶
Subjects served as their own control and were randomized into control group first.
MAS = modified Ashworth scale.
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PATTON et al. Custom-designed haptic training for individuals with poststroke hemiparesis
DISCUSSION
We conducted this initial pilot study to demonstrate
how adaptive training might be useful for restoring arm
movement. The stroke patients in this study showed less
conspicuous results compared with nondisabled subjects
exposed to the same algorithm [19]. Nevertheless, when
we restricted our analysis to movement directions that
were affected by significant error before training, results
were quite evident in the stroke group: movements
showed beneficial aftereffects after training (error
decreased) that persisted in all but three patients. This
persistence was twice as long as for nondisabled people.
While these results are only an encouraging hint at what
might be a possible therapeutic intervention, we believe
that these results suggest the need for a longer, more
comprehensive look at adaptive training as a means of
restoring function following brain injury.
A key assumption of our approach was that motion is
impaired because of an ineffective motor plan that can be
changed through adaptive training. However, one alterna
-
tive explanation of impairment is that passive contrac-
tures, commonly seen in chronic stroke patients, alter the
movement pattern. Indeed, this may explain the three sub
-
jects who deadapted to a pattern that was perhaps the most
biomechanically optimal for the characteristics of their
contracted limb. Another alternative explanation for the
three subjects is that some subjects may have less ability
to adapt. Indeed, other studies have reported diminished
ability to adapt in some stroke patients [18,20,38].
Another alternative explanation is that shifts in movement
patterns may have actually been present but were “buried
in the noise” of motor variability and therefore statistically
undetectable—a statistical power problem. Motor vari
-
ability is a commonly reported feature of stroke patients
[38,43,47–49]. Another possibility may be that subjects
could have been so highly functional that no improvement
was possible in the context of this experiment—the so
called “ceiling effect” in the learning process [50]. But
after all movements that did not exhibit significant error
before training were stripped away, the remaining move
-
ments showed signs of beneficial adaptation.
Whether this technique will lead to benefits that
might persist for days or weeks remains to be seen.
Another possibility is to consider prolonging this type of
training over many days to get ever closer to the desired
outcome. As rehabilitation training typically requires a
balance of repetitive practice, strengthening, and expert
guidance, we believe that the pilot results we present here
may inspire other new forms of rehabilitation.
One limitation of our approach is that the forces that
push the subject over to the desired trajectory x
D
(t) are
recorded in a Cartesian coordinate system. The assumption
is that these forces can be learned correctly even though
the hand is not moving along the desired positions with the
desired velocities. Because a nonlinear relationship exists
between joint torques and endpoint forces, the torques
applied along one trajectory are not necessarily appropriate
for another [51]. However, we argue that if that the desired
and expected trajectories are within a “domain of proper
generalization” [21], the forces applied are a good enough
approximation and can lead to desirable aftereffects.
Recent studies have presented evidence that motor learn
-
ing is broadly tuned so that training in one set of directions
can influence others [24,26,52–54]. Furthermore, this
learning process is quantifiable via an adaptation model
[45]. Still, more sophisticated machine-learning algorithms
would likely improve the performance by storing the
machine-learning forces in the intrinsic (joint or muscle)
coordinate system.
Most subject data (10 of 13) displayed persistent bene-
fits for the duration of the experiment (50 washout move-
ments)—beyond the time a nondisabled subject would
take to deadapt. Consequently, the data strongly suggests
that the benefits of the aftereffects are retained because
they are perceived to be an improvement. The motor con
-
trol system may respond to and retain the benefits of this
type of adaptive training for several reasons. One reason
may be that stroke patients have the confusing challenge of
being able to use only a few remaining motor pathways
after their injury. The brain attempts to send conventional
(preinjury) motor command signals, which are now inap
-
propriate because of injury, to the descending motor path-
ways. The training methods of this experiment may coax
the nervous system to attempt a new motor strategy that is
not intuitively obvious to the injured system but becomes a
“motor epiphany” following the removal of the training
forces. In this scenario, the nervous system is essentially
shown the right way to execute the task, much like a coach
may get an athlete to try a new strategy.
One might also speculate that change in reflex tone
leads to better movement. Reflex torque elicited by an
imposed stretch of the elbow has been shown to cause
decreases on average of 50 percent with tens of stretches
in a single experiment on flexor but not extensor muscles
[55]. However, if a spastic muscle pulls the limb to the
652
JRRD, Volume 43, Number 5, 2006
side and then a robot pushes to increase the error as is
done in this experiment, the spastic muscle would be
shortened. Therefore, this muscle would have less stimu
-
lation that might cause a spastic response.
Another reason adaptive training could lead to bene-
fits is that the impaired nervous system does not react to
nor does it try to learn from small errors in movement.
Our approach might promote learning by making errors
more noticeable. One can imagine many possible strate
-
gies for amplifying error, and recent research in our
group has shown promising results with several different
types [38,56]. Models of learning systems, such as neural
networks, suggest that error drives learning. As a conse
-
quence, these systems can learn better and faster if error
is larger [18,57–59]. Such error-driven learning processes
are believed to be central to the acquisition and adapta
-
tion of skill in human movement [59–60]. Augmenting
error may heighten motivation and attention or lead to
anxiety, which has been suggested to correlate with learn
-
ing [61]. Moreover, intensifying error can raise the sig-
nal-to-noise ratio for sensory feedback and self-
evaluation. Errors that are more noticeable may trigger
responses that would otherwise not be perceived. Other
studies agree with the hypothesis that error augmentation
can enhance learning and “trick” the nervous system into
certain behaviors by giving altered sensory feedback
[62–71]. Conversely, suppression of visual feedback can
slow down the deadaptive process [19]. However, not all
kinds of augmented feedback on practice conditions have
proven to be therapeutically beneficial in stroke [72].
Hence, limits may exist to the amount and type of error
augmentation that is useful [64,73].
Straightening movements may be considered a small
clinical goal compared with extending the functional
workspace or enhancing reach-and-grasp capability. This
initial effort merely tested the promise of this approach in
a well-known framework. More protracted studies lasting
weeks—currently underway in our laboratories—are
needed to provide more clinical significance to this
approach by demonstrating lasting and functional bene
-
fits of repeated treatments.
Encouraging evidence points to future studies that
exploit the natural adaptive tendencies in the nervous sys
-
tem for restoring function. Perturbation and electromyo-
graphic studies may challenge the hypothesis of reflex
modulation. Imaging and transcranial magnetic stimula
-
tion studies may determine if alternate motor pathways
are used or if the cortex is used differently after training.
Lesion site locations from magnetic resonance images
may indicate whether damage to certain functional areas
leads to limits in one’s ability to recover via adaptive
training.
While this research focuses on adaptation to forces
(kinetics), researchers have also observed similar adapta
-
tion to a more easily implemented visuomotor distortion
(kinematics). These distortions involve complex transfor
-
mations using prisms [74], nonlinear mappings [65], or
simple rotations or stretches [75–76]. All of these distor
-
tions appear to also induce an adaptation process and can
trigger rapid recovery from sensory disorders, such as
hemispatial neglect, seen in stroke patients [36], which
shortens the recovery process from months to hours.
Moreover, adaptation to both visual and mechanical dis
-
tortions appears to involve the same mechanism [77].
*
One sensory system can facilitate the other, and a combi-
nation is the most powerful. One might consider distor-
tions to induce an inappropriate and indirect form of
learning, but the addition of more sensory inputs, such as
the cutaneous sensors in the hand, the proprioceptive mus
-
cle spindles, and Golgi tendon organs may facilitate the
learning process by providing more signals. Combining
haptics (robotic forces) with sophisticated graphics (such
as virtual reality) may provide the most promising form of
rehabilitation for individuals with brain injury. However,
recent work suggests going beyond virtual reality to dis
-
torted reality in order to facilitate functional recovery.
This is currently of great interest to our group [14].
CONCLUSIONS
New opportunities for recovery after stroke are
offered by extending intensive therapy beyond present
inpatient rehabilitation stays, and robotic therapy may be
one way to economically accomplish this [78]. While
specialized forces are useful for inducing adaptive
responses, they are likely to be most effective if com
-
bined with other rehabilitation strategies. We believe that
the error-amplification approach presented here for indi
-
viduals with stroke provides a new pathway for augment-
ing motor relearning in individuals with brain injury.
*
Wei Y, Patton JL. Forces that supplement visuomotor learning: A ‘sensory
crossover’ experiment. Unpublished observations, 2006.
653
PATTON et al. Custom-designed haptic training for individuals with poststroke hemiparesis
ACKNOWLEDGMENTS
We thank Jamie Hitchens, Kathy Stubblefield, Elisa
Pelosin, and Yejun Wei for their insights later in the anal
-
ysis phase of this study.
Preliminary data for this study were presented in con-
ference proceedings [17]. For additional information on
this and other research in our laboratory, please see
http://
www.SMPP.northwestern.edu/RobotLab/.
This material was based on work supported by
the
American Heart Association (grant 0330411Z),
the
National Institutes of Health (grants R24 HD39627,
5
RO1 NS 35673, and F32HD08658), the National Science
Foundation (grant BES0238442), and the Dr. Ralph and
Marion Falk Medical Research Trust, Chicago, Illinois.
The authors have declared that no competing inter-
ests exist.
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Submitted for publication May 23, 2005. Accepted in
revised form November 17, 2005.
