Paretic versus non-paretic stepping responses following pelvis perturbations in walking chronic-stage stroke survivors
Haarman et al. Journal of NeuroEngineering and Rehabilitation
Paretic versus non-paretic stepping responses following pelvis perturbations in walking chronic-stage stroke survivors
Juliet A. M. Haarman 0 2
Mark Vlutters 0 1
Richelle A. C. M. Olde Keizer 2
Edwin H. F. van Asseldonk 1
Jaap H. Buurke 2
Jasper Reenalda 2
Johan S. Rietman 1 2
Herman van der Kooij 1
0 Equal contributors
1 Department of Biomechanical Engineering, University of Twente , Horstring W119, PO Box 217, 7500 AE Enschede , The Netherlands
2 Roessingh Research and Development , Enschede , The Netherlands
Background: The effects of a stroke, such as hemiparesis, can severely hamper the ability to walk and to maintain balance during gait. Providing support to stroke survivors through a robotic exoskeleton, either to provide training or daily-life support, requires an understanding of the balance impairments that result from a stroke. Here, we investigate the differences between the paretic and non-paretic leg in making recovery steps to restore balance following a disturbance during walking. Methods: We perturbed 10 chronic-stage stroke survivors during walking using mediolateral perturbations of various amplitudes. Kinematic data as well as gluteus medius muscle activity levels during the first recovery step were recorded and analyzed. Results: The results show that this group of subjects is able to modulate foot placement in response to the perturbations regardless of the leg being paretic or not. Modulation in gluteus medius activity with the various perturbations is in line with this observation. In general, the foot of the paretic leg was laterally placed further away from the center of mass than that of the non-paretic leg, while subjects spent more time standing on the non-paretic leg. Conclusions: The findings suggest that, though stroke-related gait characteristics are present, the modulation with the various perturbations remains unaffected. This might be because all subjects were only mildly impaired, or because these stepping responses partly occur through involuntary pathways which remain unaffected by the complications after the stroke.
Stroke; Balance during gait; Perturbed walking; Reactive foot placement; Muscle activity changes
Stroke survivors often experience problems with
maintaining their balance. A variety of neurological deficits
can hamper balance control during walking, such as
hemiparesis, sensory impairments, as well as cognitive
problems such as fear of falling. As a consequence, fall
rates in stroke survivors are 2–8 times higher than those
in healthy, age-matched subjects [
]. In general,
especially balance control in the frontal plane is often
considered challenging, requiring adequate foot placement
to continue walking [
]. This might be an additional
challenge when suffering from hemiparesis following a
stroke, which could lead to differences in recovery steps
made with the paretic and the non-paretic leg in
response to a disturbance during walking. To reduce the
fall risk of stroke survivors and make their rehabilitation
more effective, it is required to characterize how balance
control is affected by a stroke. Such knowledge might be
used to provide limb-specific support in robot-assisted
Stroke survivors typically show differences in gait
characteristics between the paretic and non-paretic leg
during unperturbed walking, for example as a result of
decreased motor control in the paretic leg. In a study by
Balasubramanian et al. subjects placed the paretic leg at
an increased lateral distance from the pelvis compared
to the non-paretic leg in mediolateral (ML) foot
placement during unperturbed walking [
]. However, no
differences in step width were found with regard to the leg
used for stepping. It is therefore of importance to
consider both legs individually, in a body referenced frame
such as that of the center of mass (COM). Dean et al.
studied the relation between gluteus medius muscle
activity in the swing leg and both the ML position and
velocity of the COM relative to the stance foot [
]. For low
fall-risk subjects the results suggest a stronger activity
modulation in the non-paretic swing leg than in the
paretic swing leg, though it did not show how both legs
respond to actual destabilizing conditions such as external
Perturbations can be used to affect the body state,
such as the position and velocity of the COM relative to
the stance foot. This may lead to adjustments in foot
placement location and timing to maintain balance. In
Krasovsky et al. perturbations were applied by
unexpectedly arresting the ankle of the leg at early swing [
Stroke survivors showed shorter step lengths and shorter
swing times compared to healthy controls when stepping
in response to perturbations applied to the non-paretic
swing leg. Furthermore, in Hak et al. continuous ML
support surface translations were used to assess if and
how low-fall risk stroke survivors change their base of
support (BoS) during walking through foot placement
]. It was found that stroke survivors more
strongly shortened their step length and step time, and
increased their step width, as compared to healthy
controls. However these studies did not compare the
perturbation responses of the individual paretic and
In healthy subjects, ML foot placement adjustments in
response to ML perturbations relate to the ML COM
]. This is reflected in a concept called the
extrapolated center of mass (XCOM), which has been
previously used to indicate fall risk in relation to foot
]. The XCOM can be derived from a
linear inverted pendulum model [
] and can be
regarded as a point on the floor at a horizontal distance
from the COM. This distance is directly proportional to
the horizontal COM velocity through a proportionality
constant ω0−1. If the inverted pendulum model would
place its point-foot onto the XCOM, the model will stop
moving in an upright position. Placing the foot further
away from the COM than the XCOM will cause the
model to fall back in the direction from which it came.
The further the model places its foot beyond the XCOM,
the sooner the model is redirected in the opposite
direction. Such movement is relevant to the ML direction of
walking, where toppling over the leg is often not desired.
Hence, investigating paretic and non-paretic foot
placement in relation to the XCOM allows analysis from
a simple model perspective.
The main purpose of this study is to characterize
differences between the paretic and the non-paretic legs in
walking chronic-stage stroke survivors in terms of ML
stepping following ML pelvis perturbations. Rather than
applying continuous disturbances, we will use transient
perturbations directed inward and outward. We will also
investigate how the steps are made in relation to the
XCOM. Only low fall-risk subjects will be considered, to
reduce the risk of actual falls in response to the
Ten stroke survivors in the chronic stage (7 male, age: 52
± 16 years, weight: 82.5 ± 13.6 kg, height: 1.75 ± 0.06 m,
mean ± std) were recruited. All gave written informed
consent before participation. All participants had a
Functional Ambulatory Category score of 4, meaning that these
subjects can walk independently on even surfaces, but
might require assistance in more challenging situations
such as uneven terrain [
]. Related clinical measures can
be found in Table 1. The experimental setup and protocol
were approved by the local ethics committee, and was in
accordance with the Declaration of Helsinki.
Subjects walked on an instrumented treadmill
(custom Y-mill, Motekforce Link, Culemborg, the
Netherlands), used to collect 3 degrees-of-freedom
ground reaction forces and torques per foot. Subjects
could be perturbed in the ML direction using a
motor (SMH60, Moog, Nieuw-Vennep, the
Netherlands) bolted to a support structure clamped
to the treadmill’s exterior frame. A brace (Distrac
Wellcare, Hoegaarden, Belgium) worn by the subject
could be connected to the motor through an
aluminum horizontal rod and a vertical lever arm.
The rod was connected to both the brace and the
lever arm through ball joints, to allow freedom of
movement in the anteroposterior (AP) direction.
Given the motor range of motion, the maximum
lateral excursion from the center of the treadmill was
approximately 0.55 m in each direction. The motor
was admittance controlled over Ethernet (User
Datagram Protocol) at 1000 Hz, using xPC-target (The
Mathworks, Natick, US). A schematic overview of
the experimental setup can be found in Fig. 1a.
Additional details can be found in Vlutters et al. [
Subject kinematic data were collected at 100 Hz using a
9 camera motion capture system (Visualeyez II, Phoenix
Time post- Affected Weight Distr. Stance Weight Distr. Walking BBS
stroke (yrs.) leg (L/R) (%) (nonPar/Par) (%) (nonPar/Par) (pts)
10 R 54/46 58/42 51
10MWT Treadmill speed
Technologies Inc., Burnaby, Canada). Marker clusters,
each consisting of 3 LEDs, were placed on the feet, lower
legs, upper legs, pelvis, sternum and head. Additional
single LEDs were placed on both lateral malleoli, and both
lateral epicondyles of the femur. Gluteus medius EMG
activity in both legs was recorded using a Bagnoli Desktop
EMG system (Delsys, Natick, MA, USA) at 1000 Hz.
Finally, subjects were filmed from the rear using a video
camera to be able to detect any instances during which
the subject grasped the handrails of the treadmill.
Various bony landmarks were indicated using an LED
based probe [
], prior to the experiment. These
landmarks were the 1st and 5th metatarsal heads, calcaneus,
medial and lateral malleoli, fibula head, medial and
lateral epicondyles of the femur, greater trochanter of the
femur, anterior and posterior superior iliac spines,
xiphoid process, jugular notch, 7th cervical vertebra, occiput,
head vertex and nasal sellion [
]. Subjects wore a safety
harness at all times to prevent injury in case of a fall.
The preferred walking speed was determined by
gradually increasing the speed of the treadmill, until the
subject felt comfortable. This was also used to have subjects
familiarize with walking on the treadmill.
During the experiment subjects wore the brace
around the pelvis and were instructed to walk at the
center of the treadmill. First, a 2 min baseline
measurement at the preferred walking speed was recorded
without applying perturbations. When no
perturbations were applied the motors were admittance
controlled such that the interaction force between the
subject and the motor was as low as possible, see
Fig. 1b. In subsequent trials, perturbations could be
randomly applied at the instance of toe-off of either
leg. This way subjects were required to use both the
paretic and the non-paretic leg in the first recovery
step. The toe-off instances were detected using the
vertical ground reaction forces. Perturbations were
150 ms block pulses of magnitudes equal to 4, 7, and
10% of the subject’s body weight. The direction could
be inward (e.g. leftward for left stance) or outward (e.g.
rightward for left stance). Though the motors cannot
track the desired reference force, the delivered impulse
is approximately as desired, see Fig. 1c. Each
perturbation condition was repeated 5 times, yielding 60
perturbations per subject. The perturbations were
randomized over magnitude, direction, and onset
instance (paretic or non-paretic toe-off ). The minimum
time between successive perturbations was also
randomized, and varied between 6 and 12 s. Finally, Berg
Balance Scale (BBS), Dynamic Gait Index (DGI),
MiniMental Scale Examination (MMSE), Fall Efficacy Scale
International (FES-I) and 10-m-walking-test (10MWT)
measures were collected within 1 week after the
Data were processed using Matlab (R2013b,
Mathworks, Natick, US). EMG data were detrended,
filtered with a 1st order 48–52 Hz Butterworth
bandstop filter, then rectified, and filtered with a 1st
order 20 Hz Butterworth low-pass filter. Marker data
were filtered with a 4th order 20 Hz zero-phase
Butterworth low-pass filter before reconstructing the
global bony landmark positions from the probe
measurements. Using the landmarks, the COM positions
of the captured segments were estimated following
Dumas et al. [
]. The total body COM was
calculated as a weighted average of the segment COM
positions, and was differentiated for a COM velocity. All
other position and velocity data were expressed
relative to those of the COM. Gait events of toe-off and
heel strike were detected using landmarks of the feet
]. Instances of perturbation onset were detected
from the motor input signals. The recorded video
data were visually inspected for perturbations during
which subjects touched the hand-rails of the
treadmill. Corresponding trials were removed from
For each subject, the position of the COM of both feet
relative to the total-body COM, as well as the COM
velocity, were extracted at the instances of heel strike and
toe-off after the perturbation. The gait phase durations
between these instants were also calculated. The EMG
data of each individual muscle was scaled to the median
of the maximum values occurring every gait cycle during
the unperturbed walking condition. For each subject and
in each condition, the scaled EMG time-averages
between toe-off and heel strike were calculated,
representing the averaged muscle activity level during the swing
phase. All these data were sorted on perturbation
magnitude, perturbation direction (inward or outward), and
leg used for stepping (paretic or non-paretic). Since five
right-sided and five left-sided paretic subjects
participated in the study, all position, velocity, and EMG data
resulting from the left side of the body were laterally
mirrored for all subjects. All data in this paper is
therefore presented as if the first recovery step was made with
the right leg, for both the paretic and the non-paretic leg
of all subjects. Next, for each subject all data were
averaged over the repeated conditions to obtain repetition
averages. These were in turn averaged for
subjectaverages and standard deviations. Finally, for each
subject an XCOM proportionality constant was calculated
following ω0−1 = √(l/g) [
], in which l is the subject’s leg
length, and g the earth’s gravitational acceleration. These
ω0−1 were averaged across subjects for a subject-average
Linear mixed models were used to determine the effect
of all perturbations (fixed factor, with intercept), the
effect of the leg used in the first recovery step (fixed factor,
with intercept), and their interaction effect on the
outcome measures. Outcome measures were the ML and
AP distance between the leading foot and the COM, the
ML and AP distance between both feet (step width and
step length, respectively), the ML COM velocity at heel
strike, the gait phase durations following the
perturbations, and the EMG signal averages. To account for
correlated repeated measures within the same subject, a
random subject factor (intercept) was included in the
model. The significance level was set to α = 0.05, and a
Dunn-Šidác correction was applied during post hoc
analysis. In the post hoc comparison, perturbation data were
only compared to the unperturbed condition and not
mutually. The Statistical Package for the Social Sciences
version 19.0 (IBM Corporation, Armonk, NY, USA) was
used for the statistical analysis.
All subjects completed the experiment without falling.
Subjects 1, 6, and 7 did not complete the DGI and
the 10MWT. Subjects 1, 2, and 3 grabbed the
handrails 4, 4, and 6 times respectively, mainly in response
to the largest magnitude inward perturbations. The
corresponding data were removed from analysis.
Subject characteristics are presented in Table 1.
Foot placement location in ML and AP directions
Perturbations resulted in foot placement modulation.
Here, we specifically focus on the location of the leading
foot. Perturbations were applied such that each subject
had to make the first recovery step with both the
pareticand the non-paretic leg. The locations of the feet relative
to the COM at the instant of the first heel strike after the
perturbation are presented in Fig. 2. Note that because the
data is presented relative to the COM, the stance foot data
changes with the perturbations because the body is
pushed away from it or towards it. Foot placement relative
to the COM as a result of the different perturbations was
not found to differ between the paretic and non-paretic
leg, for both ML (F6,117 = 0.981, p = 0.441) and AP (F6,117
= 0.068, p = 0.999) distances between the COM and the
leading foot. This indicates that no interaction effect was
present between factors perturbation and leg used for
When considering the effects of the leg used in the
stepping response, it is important to regard the foot
location relative to the COM rather than relative to the
trailing foot. In the frontal plane, the foot of the paretic
leg is always laterally placed further away from the COM
than the foot of the non-paretic leg (F1,123 = 26.956, p <
0.001). Instead, when considering step width, i.e. the ML
the distance between the feet, no significant differences
were found between the legs used in the first recovery
step (F1,123 = 2.086, p = 0.151). For the AP distance
between the COM and the leading foot no significant
effects of factor leg were found (F1,123 < 0.001, p = 0.999).
However the step length, i.e. the AP distance between
the feet, was larger when stepping with the paretic leg
compared to stepping with the non-paretic leg (F1,123 =
9.294, p = 0.003). The latter effects are therefore caused
by the trailing leg, and are likely related to the time
spent standing on that leg.
Mediolateral adjustments with the leading foot mostly
occur in the direction of the perturbation. Especially
for outward perturbations the foot appears to be placed
further in the direction of the perturbation with
increasing perturbation magnitude. Significant effects of
factor perturbation were found for the ML distance
between the COM and the leading foot (F6,123 = 7.215,
p < 0.001), as well as for the AP distance between the
COM and the leading foot (F6,123 = 24.799, p < 0.001).
Post hoc analysis revealed that this AP distance
decreased significantly for all outward perturbation
compared to unperturbed walking (p < 0.002), but did not
change significantly for all inward perturbations
(p > 0.160).
Relation between ML foot placement and ML COM velocity
We investigated the mediolateral stepping location in
relation to the ML COM velocity at the instant of heel
strike, specifically in relation to the location of the
XCOM at heel strike, see Fig. 3. If the ML distance
between the COM and the leading foot (y-axis) would
increase with increasing mediolateral velocity (x-axis) at
the same rate (ω0−1) as that of the pink XCOM line, then
the foot would be placed at a fixed distance from the
]. This would provide a simple explanation for
lateral foot placement, based on a concept derived from
a linear inverted pendulum model.
Factors perturbation and leg had a significant
interaction effect on the ML COM velocity following the
perturbations (F6,114.068 = 2.587, p = 0.022). As a result, steps
made with the non-paretic leg show more variability in
the COM velocity between the various perturbations, as
compared to that of steps with the paretic leg. That is,
for steps with the non-paretic leg outward perturbations
lead to more positive COM velocities, whereas for
inward perturbations it leads to smaller, or even a slightly
negative COM velocity. Factor leg also provided a
significant main effect on the ML COM velocity, with a
higher velocity for steps made with the non-paretic leg
(F1,114.058 = 7.011, p = 0.009). Furthermore, the various
perturbations significantly affected the ML COM
velocity at heel strike (F6,114.068 = 82.830, p < 0.001),
increasing in the direction of the perturbation with increasing
perturbation magnitude (post hoc: step with paretic: p ≤
0.027 for all perturbations except for 0.04, 0.07 outward,
step with non-paretic: p ≤ 0.007 for all perturbations
except for 0.04 outward). While at heel strike the ML
distance between the COM and the leading foot showed
modulation with the COM velocity, the average
distances do not appear to modulate with the COM
velocity in the same way as the XCOM does. That is, the
ML distance of the foot relative to the COM does not
relate to the ML COM velocity through a factor ω0−1.
Instead, both the paretic and the non-paretic leg show an
increase in foot-COM distance with increasing outward
perturbation magnitude, but remain approximately the
same with increasing inward perturbation magnitude.
Therefore, for the presented results the XCOM alone is
not a sufficient predictor of the lateral stepping location,
though this might be different in a more homogeneous
Gait phase durations
For all presented gait phase durations, modulation with
the perturbations is invariant of the leg with which the
step is made. No significant interaction effects were found
between factors perturbation and leg on the duration of
the swing phase in which the perturbation was applied
(F6,117 = 0.163, p = 0.986), nor on the subsequent double
support phase duration (F6,115.993 = 1.045, p = 0.400).
The most prominent gait phase duration
modulation occurred in the swing phase during which
the perturbation was applied. The duration of this
swing was significantly affected by the perturbations
(F6,123 = 26.035, p ≤ 0.001), significantly increasing with
increasing inward perturbation magnitude (p ≤ 0.045),
and decreasing with increasing outward magnitude
(p ≤ 0.002, except 0.04 outward), see Fig. 4. In
contrast, the subsequent double support durations were
not significantly affected by factor perturbation
(F6,121.993 = 1.864, p = 0.092).
The gait phase durations also reflect the differences
between steps made with the paretic and the non-paretic leg.
Subjects preferred to spend more time on the non-paretic
leg than on the paretic leg during the single support phase,
regardless of which perturbation was applied (F1,123 =
16.448, p ≤ 0.001). Furthermore, the double support phase
during which the weight is shifted from the paretic leg
toward the non-paretic leg also last longer compared to the
opposite shift (F1,121.993 = 23.365, p ≤ 0.001).
Gluteus medius activity
Subjects modulated the gluteus medius activity in the
swing leg with the various perturbations, for both the
paretic and non-paretic leg. Though more varied modulation
appears to occur in the non-paretic leg over all
perturbations, no interaction effects were found between factor leg
and perturbation magnitude (F6,88.978, p = 0.452).
However, a significant effect of the perturbations was
found on the gluteus medius activity levels (F6,94.917 =
18.993, p < 0.001), increasing on average. Especially for
outward perturbations where the gluteus medius
participates in foot placement adjustments through leg
abduction, increased activity levels can be observed with
increasing perturbation magnitude (p < 0.001, except for
0.04 outward), see Fig. 5. For inward perturbations the
post hoc comparison provides no significant effects,
though the non-paretic swing leg appears to show more
EMG modulation than the paretic swing leg. Differences
between both legs were not tested as activity levels of
both legs were scaled to baseline values of each leg.
We investigated the first recovery step following
mediolateral pelvis perturbations in chronic-stage stroke
survivors who walked at a self-selected speed. The transient
perturbation were randomly applied at the instant of
toe-off of the paretic and non-paretic leg, such that
every subject had to make recovery steps with both legs.
The aim was to evaluate and compare stepping
responses regarding the use of the paretic and non-paretic
leg. In accordance to our hypothesis, we found various
differences in gait characteristics between steps made
with the paretic and non-paretic leg. However, foot
placement modulation in response to the various
perturbations did not differ between the legs within the
included group of stroke survivors.
Foot placement with respect to the COM
Our findings in perturbed walking support the analysis
of foot placement measures with respect to the COM,
similar as previously applied in unperturbed walking [
As in the work by Balasubramanian et al., in the current
results the mediolateral distance between the feet is
independent of the leg with which the step is made.
However, this does not imply that there are no differences
between both legs in terms of foot placement, as the
paretic leading leg is placed at a consistent increased
lateral distance from the body’s COM. It has been
suggested that this might be related to strength limitations
in the paretic leg [
], leading to the typical weight
bearing asymmetry during stance . These limitations
could in turn lead to limited control over the COM
motion with the paretic leg [
], especially during the
single support phase in which the paretic leg is the
stance leg. This might also explain the larger variability
in ML COM velocity following the perturbations applied
during the paretic single support phase compared to
those during the non-paretic single support phase. The
results suggest that classical gait parameters such as step
length and width might not fully capture balance and
walking impairments in stroke survivors. Analysis of the
feet locations relative to the COM might identify leg
specific aspects of the impairment, and help specify
gaitphase specific support in robotic gait training and
Leg-independent step modulation in response to
The results imply that subjects attempt to execute
similar stepping strategies with both legs, and that the
modulation with the various perturbations is not
hampered by the effects of the stroke. Though the paretic leg
is laterally placed further away from the COM than the
non-paretic leg when making a step, the way in which
the foot placement location alters with the various
perturbations does not statistically differ between both legs.
Consequently, subjects do not modulate their stepping
location worse with the paretic leg than with the
nonparetic leg, in response to the various perturbations.
Similar effects were observed in modulation of the gait
phase duration following the perturbations. Although
subjects spent more time standing on the non-paretic
leg, they showed comparable changes in gait phase
durations with both the paretic and the non-paretic legs in
response to the different perturbations. One potential
cause might be that the subjects were only mildly
affected by the stroke, as classified by their clinical scales.
Subjects with lesser scores on clinical outcome measures
might have shown larger deviations. For instance, in a
study by Dean et al. high fall risk subjects showed
different stepping strategies compared to low fall risk subjects
]. Another potential reason might be that the
modulation is partly automatic. For example, Nashner et al.
showed that balance perturbations during standing can
trigger rapid postural adjustments that take precedence
over voluntary movements [
]. It might be that these
responses involve neural pathways that bypass the brain
areas damaged by the stroke.
Gluteus medius activity modulation
It has previously been suggested that swing leg gluteus
medius activation for ML foot placement might be based
on sensory information from the stance leg [
outward perturbations move the COM further away
from the stance foot with increasing perturbation
magnitude. Therefore, as in Dean et al., gluteus medius activity
increased with increasing ML distance between the
COM and the stance foot . These effects occurred in
both the paretic and the non-paretic swing legs, without
significant interaction effects between factors leg and
perturbation. These findings therefore match those for
step location and step time modulation in this study,
suggesting that modulation with the perturbations is not
different between both legs in the current subject group.
With the current setup in stroke survivors, it is not
possible to confirm whether this relation between the
stance leg and the gluteus medius activity of the swing
leg is a causal one. As stroke survivors often suffer from
sensory impairments in the paretic leg [
], one would
expect information from the stance leg to be less reliable
if that leg is paretic. Consequently, if gluteus medius
activity modulation would primarily depend on the state of
the stance leg, one would expect the paretic swing leg to
receive the best information. However, in Dean et al. the
gluteus medius of the paretic swing leg showed poorer
modulation with the state of the stance leg than that in
the non-paretic swing leg [
]. It is therefore likely that
effects of the stroke on the paretic leg dominate the
relations in Dean et al., rather than how well information is
conveyed from one leg to the other.
Absence of cross-stepping
In a similar study with healthy subjects, inward
perturbations of sufficient magnitude lead to cross-steps [
These were not observed in the current study. Though
the maximum perturbation magnitudes were not as high
as in Vlutters et al. [
], subjects visually appeared to
explicitly avoid crossing the legs. In case of larger
magnitude inward perturbations, subjects tended to move the
arms, or abduct the swing leg, possibly using angular
momentum strategies [
] to recover from the
perturbations. Similar leg abduction responses were
reported in Dean et al. for unperturbed high fall-risk
subjects . Subjects might be reluctant to make
crosssteps, as entangling the legs would hamper gait
progression. These effects might be amplified by walking on a
treadmill, as it requires the subjects to keep walking to
prevent falling off the belt.
Analysis from a model perspective
Evaluating foot placement in relation to the XCOM as
derived from a linear inverted pendulum model might
aid in understanding why a subject places the foot at a
certain location. It can be deducted how such a model
would move depending on where its foot is placed
relative to the XCOM. In general, subjects laterally placed
their foot beyond the XCOM, further away from the
COM than the XCOM itself. The foot was placed
further beyond the XCOM for steps made with the paretic
leg as compared to steps made with the non-paretic leg.
Doing so in the model would make the COM fall back
toward the other leg, but at a higher rate for steps made
with the paretic leg. This would require the subsequent
step with the non-paretic leg to be of shorter duration,
therefore spending less time standing on the paretic leg.
This is in line with our findings, as well as with other
studies in unperturbed walking [
Previous studies have investigated the XCOM as a linear
predictor for a mediolateral stepping location in healthy
]. In Vlutters et al., the COM velocity related
linearly to the mediolateral stepping location after the
perturbation . While the ML stepping location in our
stroke subjects does modulate with COM velocity, and
therefore with the XCOM, the data shows substantial
variability. The strong linear trend observed in healthy
subjects does therefore not hold here. This implies that the
COM velocity holds some predictive power for a
mediolateral stepping location in this group of stroke survivors,
but not as apparent as in healthy subjects, or perhaps not
through a linear relation.
Effects of walking speed
The subjects in this study were allowed to walk at a
selfselected comfortable walking speed, resulting in a
treadmill speed range from 0.4 to 5.0 km/h among subjects.
This range leads to variability in spatio-temporal gait
parameters such as step length, width, and frequency,
which all alter with gait speed. However, our findings
focus on the differences between the paretic and the
non-paretic legs of the subject population as a whole.
The effects of walking speed are likely to be equal for
both legs within a single subject, allowing comparison
between both legs throughout the subject population.
The perturbation device was controlled such that the
interaction force between the subject and the motor was
as low as possible. The total reflected inertia from the
motor to the pelvis is about 1 kg. In a study by Meuleman
et al. it was concluded that up to 5.3 kg of inertia can be
added to mediolateral pelvis movement without
significantly affecting the gait [
]. Interaction with the motor is
therefore not expected to affect the subjects’ gait when no
perturbation is applied. However, the rod used to connect
the motor to the pelvis brace partly obstructed the right
arm sway. Subjects were not able to fully swing their arm
backward during the measurement trials, thereby possibly
affecting the gait kinematics. Several subjects indicated
that the rod was not a problem, or preferred walking with
their arms in front of their body to hold the paretic hand.
Little effects of this constraint on the lower limb responses
are therefore expected.
It has previously been shown that self-selected
overground walking speed as assessed by the 10MWT relates
to the degree of walking impairment [
]. Based on this
classification, all subjects in our study are within a group
of community walkers, and are mildly affected. The
subject data shows that the gait speed on the treadmill is
often lower than that recorded in the 10MWT. Even
though subjects were given a period of time to familiarize
with the measurement set-up, subjects might adapt their
gait while walking on a treadmill [
] due to being
unfamiliar with the measurement setup, or due to fear of
falling. However, in Zadravec et al. it was demonstrated that
responses of healthy subjects to horizontal pelvis
perturbations show high degrees of similarity between
overground and treadmill walking [
]. Hence, despite the
differences in treadmill and overground walking speed in
our subjects, the responses to perturbations are expected
to be similar in both scenario’s given a walking speed.
Finally, the variety between subject characteristics such
as the walking speed results in more variable data than
what would be obtained with a more homogeneous
subject group, or with more subjects. The variability in the
data could affect the interpretation of the results, such
as whether or not the XCOM provides predictive value
for lateral foot placement in stroke survivors. Studying
more consistent subject groups could reveal to what
extent these relations hold for reactive stepping in
response to perturbations.
The current work investigated paretic and non-paretic
mediolateral stepping responses following ML pelvis
perturbations in walking stroke survivors. Stepping responses
were investigated for the first step taken after the
perturbation. Following a perturbation, subjects preferred to
spend more time standing on their non-paretic leg.
Analysis in a body-referenced frame identified that the paretic
leading leg was placed at a consistently increased ML
distance from the body’s COM. Despite these differences in
gait characteristics, our results suggest that the
modulation of the paretic and non-paretic leg with the various
perturbations is not greatly hampered by the effects of the
stroke. Foot placement and gluteus medius EMG activity
imply that subjects attempted to execute similar stepping
strategies with both legs. This might be explained because
all subjects were classified as having low-fall risk, or
because the responses might be partially involuntary, with
the modulation not directly affected by the effects of
stroke. Though modulation occurred in both legs,
mediolateral foot placement in the presented group of stroke
survivors could not be predicted using a linear predictor
based on the COM velocity. If the subject is able to
modulate steps using the paretic leg one might refrain from
assisting leg swing during training using a robotic device
or exoskeleton, and only provide support after foot
contact in weight bearing.
10MWT: 10-meter-walking-test; AP: Anterior-posterior; BBS: Berg balance
scale; BoS: Base of support; COM: Center of mass; DGI: Dynamic gait index;
FES-I: Fall efficacy scale international; ML: Mediolateral; MMSE: Mini-mental
scale examination; XCOM: Extrapolated center of mass
This study was supported by ZonMw, funded under grant number:
10–10,400–98-005, and by the BALANCE (Balance Augmentation in
Locomotion, through Anticipative, Natural and Cooperative control of
Exoskeletons) project, partially funded under grant number 601003 of the
Seventh Framework Programme (FP7) of the European Commission
(Information and Communication Technologies, ICT-2011.2.1). The funding
parties had no role in study design, data collection and analysis, decision to
publish, or preparation of the manuscript.
Availability of data and materials
The data used and analyzed during the current study are available from the
corresponding author on reasonable request.
JH and MV acquired the data, analyzed and interpreted the data and results,
and drafted the manuscript. ROK was involved in acquiring the data, and
interpretation of the results. EvA was involved in the design of the study,
and interpretation of the results. JB, JR, JSR, and HvdK were involved in the
interpretation of the results. All authors were involved in critically revising
the manuscript, and have approved of its content.
Ethics approval and consent to participate
This study was approved by the local Medical Ethical Institutional Review
Board (Medisch Ethische ToetsingsCommissie Twente (METC), Enschede, the
Netherlands) and methods conformed to the Declaration of Helsinki. All
subjects gave written informed consent prior to participation.
Consent for publication
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in
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1. Weerdesteyn V , De Niet M , Van Duijnhoven HJR , Geurts ACH . Falls in individuals with stroke . J Rehabil Res Dev . 2008 ; 45 : 1195 - 214 .
2. MacKinnon CD , Winter DA . Control of whole body balance in the frontal plane during human walking . J Biomech . 1993 ; 26 : 633 - 44 .
3. Balasubramanian CK , Neptune RR , Kautz SA . Foot placement in a body reference frame during walking and its relationship to hemiparetic walking performance . Clin Biomech . 2010 ; 25 : 483 - 90 .
4. Dean JC , Kautz SA . Foot placement control and gait instability among people with stroke . J Rehabil Res Dev . 2015 ; 52 : 577 .
5. Krasovsky T , Lamontagne A , Feldman AG , Levin MF . Reduced gait stability in high-functioning poststroke individuals . J Neurophysiol . 2013 ; 109 : 77 - 88 .
6. Hak L , Houdijk H , van der Wurff P , Prins MR , Mert A , Beek PJ , van Dieën JH. Stepping strategies used by post-stroke individuals to maintain margins of stability during walking . Clin Biomech . 2013 ; 28 : 1041 - 8 .
7. Hof AL . The 'extrapolated center of mass' concept suggests a simple control of balance in walking . Hum Mov Sci . 2008 ; 27 : 112 - 25 .
8. Vlutters M , van Asseldonk EHF , van der Kooij H. Center of mass velocitybased predictions in balance recovery following pelvis perturbations during human walking . J Exp Biol . 2016 ; 219 : 1514 - 23 .
9. McAndrew Young PM , Wilken JM , Dingwell JB . Dynamic margins of stability during human walking in destabilizing environments . J Biomech . 2012 ; 45 : 1053 - 9 .
10. Hof AL , Gazendam MGJ , Sinke WE . The condition for dynamic stability . J Biomech . 2005 ; 38 : 1 - 8 .
11. Pratt J , Carff J , Drakunov S , Goswami A . Capture point: a step toward humanoid push recovery . In: Humanoid robots, 2006 6th IEEE-RAS international conference on; 2006 . p. 200 - 7 .
12. Holden MK , Gill KM , Magliozzi MR , Nathan J , Piehl-Baker L . Clinical gait assessment in the neurologically impaired. Reliability and meaningfulness . Phys Ther . 1984 ; 64 : 35 - 40 .
13. Cappozzo A , Catani F , Della Croce U , Leardini A . Position and orientation in space of bones during movement: anatomical frame definition and determination . Clin Biomech . 1995 ; 10 : 171 - 8 .
14. Dumas R , Cheze L , Verriest J-P . Adjustments to McConville et al . and young et al. body segment inertial parameters . J Biomech . 2007 ; 40 : 543 - 53 .
15. Zeni J Jr, Richards J , Higginson J . Two simple methods for determining gait events during treadmill and overground walking using kinematic data . Gait Posture . 2008 ; 27 : 710 - 4 .
16. Hall AL , Peterson CL , Kautz SA , Neptune RR . Relationships between muscle contributions to walking subtasks and functional walking status in persons with post-stroke hemiparesis . Clin Biomech . 2011 ; 26 : 509 - 15 .
17. Bujanda ED , Nadeau S , Bourbonnais D , Dickstein R . Associations between lower limb impairments, locomotor capacities and kinematic variables in the frontal plane during walking in adults with chronic stroke . J Rehabil Med . 2003 ; 35 : 259 - 64 .
18. Genthon N , Rougier P , Gissot A-S , Froger J , Pélissier J , Pérennou D. Contribution of each lower limb to upright standing in stroke patients . Stroke . 2008 ; 39 : 1793 - 9 .
19. Roerdink M , Geurts AC , de Haart M , Beek PJ . On the relative contribution of the paretic leg to the control of posture after stroke . Neurorehabil Neural Repair . 2009 ; 23 : 267 - 74 .
20. Nashner LM , Cordo PJ . Relation of automatic postural responses and reaction-time voluntary movements of human leg muscles . Exp Brain Res . 1981 ; 43 : 395 - 405 .
21. Rankin BL , Buffo SK , Dean JC . A neuromechanical strategy for mediolateral foot placement in walking humans . J Neurophysiol . 2014 ; 112 : 374 - 83 .
22. Carey LM . Somatosensory loss after stroke . Crit Rev™ Phys Rehabil Med . 1995 ; 7 ( 1 ): 51 - 91 .
23. Herr H , Popovic M. Angular momentum in human walking . J Exp Biol . 2008 ; 211 : 467 - 81 .
24. Hof AL . The equations of motion for a standing human reveal three mechanisms for balance . J Biomech . 2007 ; 40 : 451 - 7 .
25. Jeon H-J , Kim M-Y , Lee J-U , Kim J. Differences in leg length discrepancy and weight distribution between the healthy and unhealthy sides of hemiplegic stroke patients . Toxicol Environ Health Sci . 2013 ; 5 : 221 - 6 .
26. van Meulen FB , Weenk D , Buurke JH , van Beijnum B-JF , Veltink PH . Ambulatory assessment of walking balance after stroke using instrumented shoes . J Neuroeng Rehabil . 2016 ; 13 : 48 .
27. Meuleman JH , Van Asseldonk EH , Van der Kooij H. The effect of directional inertias added to pelvis and ankle on gait . J Neuroeng Rehabil . 2013 ; 10 : 40 .
28. Perry J , Garrett M , Gronley JK , Mulroy SJ . Classification of walking handicap in the stroke population . Stroke . 1995 ; 26 : 982 - 9 .
29. Bayat R , Barbeau H , Lamontagne A . Speed and temporal-distance adaptations during treadmill and overground walking following stroke . Neurorehabil Neural Repair . 2005 ; 19 : 115 - 24 .
30. Zadravec M , Olenšek A , Matjačić Z. The comparison of stepping responses following perturbations applied to pelvis during overground and treadmill walking . Technol Health Care . 2016 : 1 - 10 .