Selective control of gait subtasks in robotic gait training: foot clearance support in stroke survivors with a powered exoskeleton
Journal of NeuroEngineering and Rehabilitation
Selective control of gait subtasks in robotic gait training: foot clearance support in stroke survivors with a powered exoskeleton
Bram Koopman 0
Edwin HF van Asseldonk 0
Herman van der Kooij 0 1
0 Institute for Biomedical Technology and Technical Medicine (MIRA), Department of Biomechanical Engineering, University of Twente , Enschede , The Netherlands
1 Department of Biomechanical Engineering, Delft University of Technology , Delft , The Netherlands
Background: Robot-aided gait training is an emerging clinical tool for gait rehabilitation of neurological patients. This paper deals with a novel method of offering gait assistance, using an impedance controlled exoskeleton (LOPES). The provided assistance is based on a recent finding that, in the control of walking, different modules can be discerned that are associated with different subtasks. In this study, a Virtual Model Controller (VMC) for supporting one of these subtasks, namely the foot clearance, is presented and evaluated. Methods: The developed VMC provides virtual support at the ankle, to increase foot clearance. Therefore, we first developed a new method to derive reference trajectories of the ankle position. These trajectories consist of splines between key events, which are dependent on walking speed and body height. Subsequently, the VMC was evaluated in twelve healthy subjects and six chronic stroke survivors. The impedance levels, of the support, were altered between trials to investigate whether the controller allowed gradual and selective support. Additionally, an adaptive algorithm was tested, that automatically shaped the amount of support to the subjects' needs. Catch trials were introduced to determine whether the subjects tended to rely on the support. We also assessed the additional value of providing visual feedback. Results: With the VMC, the step height could be selectively and gradually influenced. The adaptive algorithm clearly shaped the support level to the specific needs of every stroke survivor. The provided support did not result in reliance on the support for both groups. All healthy subjects and most patients were able to utilize the visual feedback to increase their active participation. Conclusion: The presented approach can provide selective control on one of the essential subtasks of walking. This module is the first in a set of modules to control all subtasks. This enables the therapist to focus the support on the subtasks that are impaired, and leave the other subtasks up to the patient, encouraging him to participate more actively in the training. Additionally, the speed-dependent reference patterns provide the therapist with the tools to easily adapt the treadmill speed to the capabilities and progress of the patient.
Robotic gait rehabilitation; Stroke; Reference trajectories; Virtual model control; Support of subtasks; Adaptive control; Impedance control; Reliance; Compensatory strategies; Visual feedback
Many patients with neurological injuries, like stroke or
spinal cord injury (SCI), suffer from muscle weakness,
loss of independent joint control, and spasticity, often
resulting in gait disorders. To regain functional mobility,
these patients require task-oriented, high-intensity, and
repetitive training [1-3]. Robotic gait-training devices are
increasingly being used to provide this kind of training.
They can provide highly repetitive, more frequent, and
intensive training sessions, while reducing the workload
of the therapist, compared to more conventional forms
of manual-assisted (and body-weight-supported) gait
training. Additionally, the assessment of the progress of
the patient becomes more objective with the integration
of different sensory systems, which can record
interaction forces and gait kinematics .
Despite the reduction in labor intensity, the
therapeutic effect of the different types of gait trainers is
inconsistent. Pohl, et al., and Mayr, et al., reported a
significant improvement in gait ability in subacute stroke
patients, compared to conventional physiotherapy [5,6].
Other studies found no significant difference between
robotic support and manual treadmill training [7,8], or
conventional physiotherapy , although robotic gait
training did show improvements in gait symmetry [7,9].
Some results even indicate that manual treadmill
training is superior to robotic assistance . Recently, a
large multicenter randomized clinical trial suggested that
the diversity of conventional gait training elicits greater
improvements in functional recovery than
roboticassisted gait training . These contradicting results
emphasize that robot-aided training needs to be further
optimized to increase therapeutic outcome.
One of the most important factors that promotes
therapeutic outcome is active participation. Active
patient participation has been proven to be beneficial for
motor learning in general [12-14] and is suggested to be
important for rehabilitation of gait disorders . The
first-generation devices, like the Lokomat (Hocoma
AG, Switzerland) or AutoAmbulator (HealthSouth, USA),
were initially developed based on the approach of
enforcing gait upon a patient by moving the legs through a
prescribed gait pattern. This diminishes the need for the
patients to actively contribute to the required motion.
Moving the legs in a rigid fashion is known to reduce 
and affect  voluntary muscle activity compared to
manual assistance, possibly making the patient reliant on
the support. Rigid trajectory control also limits the natural
gait variability and the possibility to make small movement
errors. These small errors have been suggested to promote
motor learning in mice  as well as humans [19,20].
To encourage active participation, and allow natural
gait variability, more and more robotic devices control
the interaction forces by using impedance or admittance
control algorithms [21-29]. They guide the leg by
applying a force rather than imposing a trajectory. Impedance
(or admittance) control can also make the robots
behavior more flexible and adaptive to the patients
capabilities, progress, and current participation. Depending on
the impedance levels, small errors are still possible,
promoting motor recovery. Patients might also increase
their motivation, since additional effort by the patient is
reflected in their gait pattern. Controllers based on this
principle are referred to as assist-as-needed (AAN),
cooperative, adaptive, or interactive controllers. In
mice, these AAN algorithms have been shown to be
more effective than position-controlled training .
Using impedance control instead of position control,
however, introduces new challenges. First, low impedance
levels increase the risk that the subject and robot start to
walk out of phase. Consequently, the robot will resist,
rather than support, the subject. Different algorithms have
been proposed to avoid synchronization problems. To
account for alterations in cadence, the reference pattern of
the robotic controller can be accelerated or decelerated,
based on the difference between the current gait phase of
the subject and the state of the robot. This can be done
continuously  or on a step-by-step basis .
Second, the impedance level needs to match the patients
capabilities and progress, which can vary widely due to
different levels of increased muscle tone, muscle weakness, or
loss of coordinated control. This makes choosing the
appropriate setting a priori a difficult process for the
operator. In most applications, the amount of support is set by
the operator on a trial-and-error basis. Setting the support
levels too low can result in a dangerous situation, whereas
too much assistance might reduce active participation of
the patient. Roughly two strategies can be distinguished to
automate the process of setting the support levels. The
support levels can be adjusted based on increased patient
effort (detected with force sensors) , or based on
kinematic errors . Emken, et al. , developed an
errorbased controller with a forgetting factor. The algorithm
systematically reduces the impedance levels when
kinematic errors are small, whereas it increases the impedance
when the errors are large. When the subject
(unconsciously) reduces his effort, he will experience no support.
Only when the subject fails to commit to the reference
pattern for a longer duration of time, the support will be
increased. This should prevent the patient from becoming
reliant on the support. In parallel, it allows normal gait
variability by lowering the impedance levels, when possible.
Others use a deadband or a non-linear stiffness to allow
normal variability, without causing the robot to increase its
assistive forces [25,29].
Third, even when the impedance levels are adaptive, the
whole movement is still potentially supported. This implies
that the patient receives support during gait phases where
his performance decreases, making no distinction between
the patients incapability, reliance, or fatigue. This also
limits the possibility to focus the therapy on specific aspects of
the walking pattern that require special attention.
Fourth, despite that impedance control does not rigidly
impose a fixed reference pattern, it still requires some sort
of reference pattern to determine the supportive force.
These patterns are mostly based on pre-recorded
trajectories from unimpaired volunteers. The major limitation
of these patterns is that they are not publicly available.
Additionally, most patterns are recorded at a limited
number of speeds, while the progress of the patients preferred
walking speed can be as small as 0.1 km/h.
In this paper, we extend the support strategy that we
currently use in out gait trainer LOPES . Within this
strategy, patients are supported based on the execution of their
gait subtasks, rather than their complete leg movement.
Recent simulation and experimental studies [32,33]
showed that the muscle activity during walking can be
decomposed in different modules. Each of these modules
can be associated with a specific subtask of walking
(e.g., body weight support, forward propulsion or foot
clearance). In stroke survivors, each of these subtasks can
be impaired to some degree without automatically affecting
others. Selectively supporting these subtasks, based on the
capabilities and progress of the patient, can be seen as an
extension of the assist-as-needed principle. Also, the
subtasks of both legs can be regarded separately, since in most
stroke survivors the paretic leg will be more affected than
the non-paretic leg. Controlling gait subtasks, rather than
joint angles, also implies that compensatory strategies, like
hip circumduction to create more foot clearance, can still
be used. Imposing a symmetrical joint-angular reference
pattern also limits the possibility of the non-paretic leg to
compensate for the deficiencies of the paretic leg.
For the foot-clearance subtask, we developed a
controller based on the Virtual Model Control framework
. This kind of control provides an elegant way to
prevent synchronization problems by only controlling a
specific subtask during the corresponding phase of the
gait cycle. Using Virtual Models for different subtasks
also allows straight-forward adaptation of the support
to the subjects specific needs by only turning on the
controllers for impaired subtasks. A pilot study on a
small number of healthy subjects already showed that
this method allows selective control of foot clearance,
while leaving the remaining walking pattern largely
unaffected . However, also within a specific subtask,
the amount of support needs to match the specific
needs of the patient. The support should be such that 1)
large errors are prevented, 2) safe walking is guaranteed,
3) small errors and variations over steps are allowed and
4) reliance is minimized. In another pilot study we
incorporated the adaptive algorithm, that shaped the
impedance as a function of tracking performance, and
that was introduced by Emken, et al. . With that
pilot study we showed that the stiffness profile
converged to a subject-specific pattern, that varied over the
gait cycle and matched the subjects needs . During
the various pilot experiments, we also experienced that
visual feedback, based on basic gait parameters like foot
clearance, is easier to interpret for patients and
therapists than feedback in terms of joint angles or
The main contribution of this paper is to show the
effectiveness of selective-subtask-support, in conjunction
with adaptive support levels, in stroke survivors. Young
healthy subjects will be used as a control group. Secondly,
a new method to quantify reliance will be tested. Since
reliance, or slacking, is known to be present in upper-limb
robotic support , and is considered to be an undesired
effect, we try to investigate this phenomena using catch
trials. Catch trials are often used in motor learning
experiments to evaluate human behavior during prolonged
exposure to external stimuli. To our knowledge, this type of
methodology, to quantify reliance in lower-limb robotic
gait training for stroke survivors, has not been used before.
Because reliance is closely related to the feedback that the
patient receives, we also developed a system to provide
the patient with visual feedback about his performance.
Thirdly, we will investigate the use of compensatory
strategies in the robotic gait trainer. Since LOPES  allows
hip abduction, patients are allowed to employ their
compensatory strategies in the device. This puts us in the
unique position to evaluate whether patients reduce their
compensatory strategies when they receive robotic
support. Before testing the VMC framework, we will also
present a new method, and results, of constructing
reference trajectories for the ankle movement at different
speeds. First, the pattern is parameterized by defining
different key events (minima, maxima etc.), which are
extracted from the individual patterns. Next, the walking
speed and body-height dependency of the parameters are
determined by regression models. These regression
models can be used to reconstruct patient-specific ankle
movement patterns at any speed.
Eleven healthy elderly subjects (five male, six female, age
57.3 5.9, weight 74.9 kg 11.9, length 1.70 m 0.11)
volunteered to participate in an experiment that was setup
to collect the reference patterns that are required for VMC
of the step height. All subjects had no symptoms of
orthopedic or neurological disorders and gave informed consent
before participating in the experiments.
Gait kinematics were recorded using an optical tracking
system (Vicon Oxford Metrics, Oxford, UK) at a
frequency of 120 Hz. To track the motion of the subject,
twenty-one passive reflective markers were attached to
bony landmarks on the legs and trunk. The subjects
were asked to walk on a treadmill at seven different
speeds: 0.5, 1, 1.5, 2, 3, 4 and 5 km/h. After a general
familiarization period of three minutes, the subjects
walked for three more minutes at each selected speed.
During each trial, the subjects did not receive any
specific instructions about how to walk on the treadmill.
After each trial, the subject had a one-minute break.
Only the last minute of each trial was used for data
analysis. The recorded marker positions were processed
using custom-written MATLAB software . Since the
proposed VMC approach is end-point based, we do not
require the hip and knee angular reference patterns, but
only the ankle pattern in Cartesian space.
Key events and predictor variables
The kinematic data was split up into individual strides of
the right ankle, based on a phase-detection method
developed by Zeni, et al. , that used the local
maxima in the anterior-posterior position of the heel marker.
Each individual stride was parameterized by defining
points that corresponded to key events in the gait cycle.
For the vertical ankle position, from now on referred to
as ankle height, these key events included the ankle
height at the heel-contact of the contralateral leg (start
of the double stance), and a selection of extreme values
in position and velocity data. Each key event was
parameterized by an index, representing the percentage of
the gait cycle at which the key event occurred, and its
position and velocity. The median index, position and
velocity of the key events were computed for each
subject at each walking speed. Figure 1 shows the selected
key events for the reference ankle-height pattern.
The median index, position, and velocity of the key
events were used to construct the regression models.
These regression models require a set of predictor
variables. We used the following regression formula.
Where v represents the walking speed and l the body
height. y represents the index, position or velocity of a
Figure 1 Selection of key events for the reference ankle-height
pattern. The key events are a selection of extreme values in
position and velocity. HC CL represents the key event that is located
at Heel Contact of the Contralateral Leg.
particular key event. Stepwise regression  was used to
test the statistical significance of the predictor variables,
using entrance/exit tolerances of 0.05 on the p-values.
After selecting the appropriate predictor variables for each
regression model, robust regression  is used to retrieve
the final set of regression coefficients (x). Robust
regression is an iterative linear regression procedure that uses a
tuning function to downweight observations with large
residuals. Figure 2 shows an example of how the index,
position, and velocity of one key event changes for
different walking speeds. The relative position of the key event
(the index) decreases at higher walking speeds, whereas
the position and velocity of the key event increase. It also
shows that these effects are nonlinear. Therefore, the
regression models for the index, position, and velocity of this
particular key event contain coefficients for the walking
speed and walking speed squared. The stepwise regression
showed that the body height has no significant
contribution to the predictability of the index and position of the
key event, whereas for the velocity of this key event it did
contribute to the predictability. This indicates that some
of the variability in the third figure could be attributed to
differences in body height. The lists with the actual values
for 0, 1, 2,and 3 can be found in the results section.
Now that the regression models for the key events are
known, a reference pattern can be reconstructed for each
walking speed (in the range of 0.5-5.0 km/h). First, the
index, position, and velocity of the key events, for a certain
speed and body height, are calculated. Next, a cubic spline
is fitted between every pair of consecutive key events,
resulting in 6 (3rd order) polynomials describing the
ankle-height pattern. By definition the position and
Figure 2 Relation between walking speed and the index, position, and velocity of a particular key event. The figure shows the index (A),
position (B), and velocity (C) of the 1st local maximum velocity key event at different walking speeds. Each circle represents the median value at
a specified walking speed for one subject. The timing of the key event (the index) decreases at higher walking speeds, whereas the position and
velocity of the key event increase. The solid line represents the fitted regression model. Stepwise regression showed that the velocity (C) of this
key event is also dependent on the body height. Here the fitted regression model for the average body height (1,7 m) is shown.
velocity of the first key event (at 0 percent of the gait
cycle) and the end of the last spline (at 100 percent) are
equal. The resulting set of splines are merged to construct
the reference pattern over the complete gait cycle.
To determine the accurateness of the spline-fitting
procedure, we compared the constructed splines (based on
data from the right ankle of all subjects) with the left ankle
pattern of each subject. First, we reconstructed the
reference patterns for the set of walking speeds (0.5-5 km/h)
for each subject, taking into account the subjects
individual body height. Next, their average left ankle pattern,
during the last minute of walking at the different speeds, was
calculated and the Root Mean Square Error (RMSE)
between both signals was calculated. The resulting RMSE
was averaged across subjects, for each speed.
Additionally, the correlation coefficient was used to
quantify the similarity between the left ankle pattern and
the reconstructed pattern. Both comparisons were
performed for the ankle-height profile and the ankle-height
Selective support of subtasks
Six elderly stroke survivors (five male, one female, age
57.8 6.4, weight 88 kg 12.2, length 1.81 m 0.05)
volunteered to participate in an experiment that was setup to
validate the VMC for the step height. Table 1 lists the
clinical description of the stroke survivors in more detail.
As a control group, twelve healthy young subjects (six male,
six female, age 25.8 2.2, weight 70.3 kg 10.9, length
1.77 m 0.10) also volunteered to participate in the
experiments. All healthy subjects had no symptoms of orthopedic
or neurological disorders. Both groups gave informed
consent before participating in the experiments.
Experimental apparatus and recordings
For the VMC experiments, the prototype of the gait
rehabilitation robot LOPES was used (see Figure 3). The
system is comprised of a bilateral exoskeleton-type
rehabilitation robot above an instrumented treadmill. It
is lightweight and actuated by Bowden-cable-driven
series-elastic actuators . The robot is impedance
controlled, which implies that the actuators are used as
torque sources . The exoskeleton offers a freely
translatable (3D) pelvis, where the sideways and
forward/backward motion is actuated. Furthermore, it
contains two actuated rotation axes in the hip joints and
one at the knee (abduction/adduction of the hip and
flexion/extension of hip and knee). A more detailed
description of the exoskeleton design is provided in .
Linear and rotary potentiometers measured
translations and angular rotations of all degrees of freedom.
Kinematics were used to detect heel contact (HC) and
toe-off (TO) events. HC and TO were used as triggers
to switch the robotic support on and off, and used to
segment the data into individual strides.
xPC Target was used for real-time control at 1000 Hz.
From the measured exoskeleton joint angles, and the
human segment lengths, the ankle position is calculated at
each instant of time. Data is collected on the target
computer in real-time and then transferred to a host machine,
where it was sampled at 100 Hz and stored for off-line
analysis, using custom software (MATLAB, Mathworks
Inc., Natick, MA, USA). For all subjects we measured joint
kinematics (angles and Cartesian positions), the torques
applied by the LOPES, and the gait phases.
Virtual model control
Virtual Model Control was used to selectively support
the step-height subtask. The basis of this control method
is to define physical interactions with the patient that
Table 1 Clinical description of patient group
FAC = Functional ambulation categories (max = 5).
DE = Duncan Ely test (min = 0, indicating normal tonus, max = 4, indicating rigidity).
TBT = Timed balance test (max = 5).
Proprioception = Outcome of lower extremity proprioception part of Nottingham sensory assessment, paretic (P) vs. non-paretic (NP) (max = 8).
MI leg = Motricity Index score, of the lower extremity (max = 99).
would assist the gait subtasks. These interactions are
then translated into a set of Virtual physical Models
(VMs), such as springs and dampers, that can be
switched on and off at appropriate times in the gait
cycle. The virtual forces, that would be exerted by the
VMs, are translated into joint torque commands for the
joint actuators. Here we want to support the foot
clearance. Therefore, we define a virtual spring (with stiffness
Kz) between the actual ankle height and the reference
ankle height (see Figure 4). If the actual ankle height (z)
deviates from the reference ankle height (zref ), a virtual
force (Fz) is exerted at the ankle, which mimics a
therapist lifting the ankle.
Initial testing showed that no damping was needed,
since the human limbs provide a kind of natural
damping to the system. The reconstruction of the reference
ankle-height pattern is explained in the next section.
The required vertical force is delivered by applying a
combination of knee and hip joint torques to the human.
The forces of the virtual spring are mapped to joint
where represents the joint torques at the hip and knee,
that offers the virtual force in Cartesian coordinates, and
haJT is the transpose of the Jacobian that maps the hip
_ h and knee _ k angular velocities to the velocities of
the ankle in Cartesian coordinates.
Lu cosh Ll cosh
Lu sinh Ll sinh
For foot-clearance support, only support in vertical
direction is required, therefore Fx is zero. The symbols
are defined in Figure 4.
Reference pattern reconstruction
For the patient group, the reference pattern is
reconstructed in the way described before, using the obtained
regression models (see spline fitting). In order to
Figure 3 The prototype of the gait rehabilitation robot LOPES.
Figure 4 Schematic representation of the VMC approach. Z
represents the absolute ankle height and Zref the reference ankle
height. Lu and Ll represent the upper and lower leg length, and k
and h the knee and hip angle. Kz indicates the virtual spring
investigate the effectiveness of the VMC approach for
healthy subjects, we chose to increase the reference
pattern, since the subjects were expected to already walk
according to the pattern. The shape of the reference
pattern is calculated similarly as in the patient group, but
the obtained pattern was multiplied such that the
maximal ankle height of the reference pattern reached a
15percent increase with respect to their nominal maximum
ankle height. The nominal ankle height of each subject
was obtained from a walking trial in LOPES where no
support was provided.
To prevent synchronization problems a specific subtask
is only supported during the phases in which the subtask
should be performed. For the step-height support, this
indicates that the controller is only active during the
double stance (with the contralateral leg in front) and
the swing phase. Heel contact and toe-off events were
detected in real time based on a phase-detection method
developed by Zeni, et al. . To account for alterations
in cadence the speed at which the reference trajectory is
replayed is scaled to the previous cycle time, and the
timer is reset at the contralateral heel contact.
To adapt the level of support within the step-height
subtask, we adopted the error-driven adaptation algorithm
of Emken et al. . The algorithm modifies the virtual
spring stiffness, at each percentage of the gait cycle,
based on the recorded error in the previous steps.
Where the superscript i denotes the ith step cycle, f is
a forgetting factor set to 0.9, g is an error-based gain set
to 1800, Kz is the resulting stiffness profile for the ankle
height, and t indicates the percentage of the gait cycle,
which is estimated based on the previous cycle time.
The stiffness was constrained to positive values, since
the support is intended to lift the ankle, and not push
the ankle downwards, when the ankle is above the
Before positioning a subject in the LOPES, different
anthropometric measurements were taken to adjust the
exoskeleton segments lengths. Next, the subject was
positioned into the LOPES and the trunk, thigh, and
upperand lower shank were strapped to the exoskeleton.
After a general familiarization period, the preferred
walking speed was determined for each stroke survivor
individually. During this familiarization period, LOPES
was operated in the zero-impedance mode. In this mode
the impedance of every joint is set to zero, so the robot
provides minimal resistance/assistance to the stroke
survivor . All patient trials were performed at the same
predefined preferred walking speed. All healthy control
subjects walked at 3 km/h.
Next, the stroke survivors, and healthy control subjects,
were exposed to selective control of the step height with a
compliant (600 N/m), stiff (1200 N/m) and adaptive
virtual spring (see impedance shaping). The stiffest of these
springs was chosen as having the maximum stiffness that
was comfortable for subjects during pilot experiments. For
the healthy control subjects all conditions were tested on
the right leg only, while the left leg was operated in zero
impedance. The stroke survivors were supported on their
For the patient group, we intended to use visual
feedback to maximally motivate the subjects in taking higher
steps. To investigate if subjects are capable of translating
the information from a simple visual feedback system
into the appropriate action, the visual feedback system
was first tested on the healthy control group. The visual
feedback system consisted of a screen, showing bars that
represented the maximum ankle height of their most
recent step. Also, the target height was displayed.
Preliminary results showed that healthy subjects were able
to use this visual feedback to reach the target height very
accurately. Therefore, it was decided to provide this kind
of visual feedback to the patient group in almost all
All conditions were randomized to minimize the
effects of fatigue or motorlearning effects. Table 2 lists
the different conditions. To evaluate if the robot is
influencing the steps without anticipation of the subject, we
decided to use catch blocks, where the subject did not
receive any support. 7 catch blocks were randomly
interspersed among the first 115 steps of support. Each catch
block consisted of three steps. Some patients could not
walk for 115 consecutive steps because of the severity of
Table 2 List of the tested conditions
their stroke. For these patients the last 10 steps of their
trial are discarded, and only fully accomplished catch
blocks and exposure blocks are included in the data
analysis. To evaluate the effect of prolonged exposure, the
trials in the healthy subjects were concluded with 50
steps of continuous exposure.
In general, the effectiveness of the step-height controller
was assessed by determining how well the set reference
values were attained, and how the support affected other
aspects of walking. First, the data was segmented into
separate steps based on the heel contact events .
Next, different spatiotemporal gait parameters were
extracted from the ankle trajectories: the maximal ankle
height (step height), the step length, and the cycle time.
The maximum ankle height is the maximum vertical
displacement of the ankle during swing. The step length is
the relative horizontal displacement of one ankle with
respect to the opposite ankle at the moment of heel
contact. All gait parameters were normalized with respect to
their nominal values. This allowed for comparison across
subjects and conditions. For the healthy subjects, as well
as the stroke survivors, the nominal values were
recorded during a trial in which they walked in the zero
impedance mode. Additionally, the relative duration of
the different gait phases was calculated. All parameters
were obtained for the exposure, as well as the catch
blocks. Group averages were calculated for the stroke
survivor and healthy control group.
To investigate the reduction of compensatory
strategies, we also determined the maximum knee flexion,
maximum hip abduction, and maximum pelvic height
for the stroke survivors during the different conditions.
Stroke survivors, with stiff-knee gait, for example, often
fail to reach enough toe clearance and use different
compensatory strategies to overcome their reduced knee
flexion. Common strategies are a circumduction
strategy, pelvic hiking, and vaulting. Vaulting is caused by an
increase of the plantar flexion of the non-paretic leg,
pushing the pelvis upward and creating more foot
clearance on the paretic size. We hypothesize that, when
stroke survivors experience step-height support, they
reduce their compensatory strategies. Thus, assisting one
subtask might automatically correct gait kinematics
To investigate the selectivity of the VMC support, we
first used a one sample t-test to determine whether the
percentage change in the spatiotemporal parameters
differed from 0 percent. If the step-height support
significantly influenced one of the defined spatiotemporal
parameters, we used a paired t-test to assess whether
there was a statistically significant difference between
the conditions with the compliant and stiff virtual
spring. All statistical tests were performed with SPSS
Statistics (IBM Corporation, Armonk, NY, USA). The
level of significance was defined at 5 percent.
Regression models for the reference patterns
The timing, position and velocity of the key events were
highly dependent on the walking speed (see Figure 5).
Generally, the different subjects showed the same
dependencies. However, there was considerable variation between
the subjects in the timing, position and velocity of the key
events at a specific walking speed (see Figure 2). The
stepwise regression, and subsequent robust regression, showed
that most key events were linearly and/or quadratically
dependent on speed (see Table 3). The body height did
not influence the timing (index) of the key events. Of all
positions it only influenced the maximal height key
event and it influenced the velocity at three of the six key
events. The Root Mean Square Error (RMSE), in the
prediction of the timing of the key events, was <2 percent of
the gait cycle (except for the timing of the minimal
height). The RMSE, in the prediction of the position, was
maximally 1.44 cm and was maximally 0.11 cm/%gait
cycle for the velocity. Figure 5 also shows that the key
events could be predicted well using the regression
From the predicted key events, a reference ankle-height
pattern was reconstructed for every subject and walking
speed. We validated these patterns by comparing them with
the measured patterns of the left leg (NB the regression
equations were fitted on data of the right leg). The
reconstructed patterns fitted the measured data well (see
Figure 6). The RMSE, averaged across subjects, was around
1 cm for all walking speeds and the average correlation
coefficient was larger than 0.95, for the low speeds, and
showed even larger values for higher walking speeds. Since
the error in predicting the key events is reflected in the
reconstructed patterns, these RMSE values were close to
the average RMSE in the prediction of the position of the
key events (see Table 3, average position RMSE = 0.79 cm).
The large correlation coefficients are in line with the small
RMSE in the prediction of the timing of the key events.
Also, the reconstructed velocity profiles matched the
measured velocity profiles well (see Figure 6), though the
correlations were a bit lower, especially for the lower velocities.
As a reference, we also calculated the RMSE and
correlation coefficient between the measured right and left ankle
patterns. These values provide an indication of the
achievable fitting quality (see Figure 6). The correlations between
the reconstructed spline and left leg data were very close to
the correlations between the left and right leg data, whereas
the RMSE values were approximately twice as large.
Figure 5 Typical example of the reconstructed ankle-height patterns. Graph A shows the individual steps (gray lines), together with the
detected key events (gray circles), at different walking speeds, for a specific subject. The black filled circles represent the predicted key events for
this particular subject, based on the obtained regression models. The black line represents the spline, which is fitted through the predicted key
events. Graph B shows the velocity profile.
Healthy control group
Selective and gradual support
One of the goals of this study was to show the feasibility
of selectively and gradually supporting step height
during gait training. Providing step-height support resulted
in a selective support of this specific subtask. It
significantly increased the right step height, whereas it did not
significantly affect the other basic gait parameters, like
the left step height, step length, cycle time, or the
relative duration of the different gait phases (see Figure 7).
Analyzing the gait kinematics showed that the increase
in step height was primarily caused by an increase in
knee flexion. For the stiff controller, the average knee
angle increased with 4.9 degrees at the moment of
maximum ankle height, whereas the hip angle at that
moment increased with only 1 degree (see Figure 8). The
average maximum joint torques, that causes these
changes, were 10 Nm hip extension and 9.6 Nm knee
flexion. The support was also gradual, since the use of
the stiff controller resulted in a significant increase in
step height compared to the compliant controller.
Non-adaptive support does not induce reliance
We did not find any evidence for reliance of the subjects
on the provided support, when they are exposed to
continuous non-adaptive support. No significant difference
between the initial exposure (first steps of the exposure
blocks) and prolonged exposure (last steps of the exposure
block) was found (see Figure 9). The step height during
the first step of the catch block also revealed no signs of
reliance. It shows that the subjects drop back to their
baseline, without any significant undershoot, which was to
be expected when reliance would occur (see Figure 9).
This holds for the compliant as well as the stiff controller.
Even when the subjects received continuous support for a
longer period of time (50 steps of continuous exposure),
the step height did not significantly differ from the
Visual feedback enhances performance and active
With visual feedback, the healthy subjects reached an
average increase of 14.5 percent in step height during
continuous exposure, while the reference was set to 15 percent.
This demonstrates that they can easily translate the simple
information displayed on the screen into the appropriate
hip and knee angular response. The visual feedback
resulted in an additional increase in hip and knee flexion
compared to the stiff controller (see Figure 8). The results
also demonstrate that the subjects use the feedback to
actively increase their step height within one step after the
support has switched off (see Figure 10). After the support
Table 3 Regression models for the index, position and velocity of the different key events
0. 207 x10-3
Speed is expressed in km/h and body height in m.
HC CL - Heel contact contralateral leg.
indicates that that particular predictor variables does not contribute to the predictability of the key event.
Figure 6 Validation of the reconstructed ankle-height patterns. A: RMSE between the left ankle-height pattern and the reconstructed spline
(black line), and the RMSE between the left ankle-height pattern and the right ankle-height pattern (gray). Both measures were averaged across
subjects for each walking speed. The error bars indicate the standard deviation. B: Correlation between the left ankle-height pattern and the
reconstructed spline (black line), and the correlation between the left ankle-height pattern and the right ankle-height pattern (gray line).
Graph C and D show similar figures for the validation of the velocity profile.
Figure 7 Selective and gradual foot-clearance support in the healthy control subjects and stroke survivors. A: Mean normalized gait
parameters for the trials in which the healthy subjects walked with the compliant (HC) and stiff (HS) step-height VMC. The mean parameters are
calculated during the last 10 steps of the last exposure block. C: Mean relative contribution of the different gait phases to the cycle time for both
tested conditions. As a reference, also the relative gait phases during walking in the zero impedance mode (HZ) are shown. B: Mean normalized
gait parameters for the trials in which stroke survivors walked with the compliant (PCV) and stiff (PSV) step-height VMC, in combination the visual
feedback. Mean parameters are calculated during the five steps of the longest exposure block. D: Mean relative gait phases for both tested
conditions. The error bars indicate the standard error of the mean. *p < 0.05. ++ indicates a significant difference between the compliant and
Figure 8 Increase in hip and knee angle during foot-clearance
support for the healthy control subjects. Mean absolute changes
in the hip and knee angle for the trials in which the healthy subjects
walked with the compliant step-height VMC (HC), stiff VMC (HS), and
with the stiff VMC in combination with the visual feedback (HSV).
Then mean parameters are calculated during the last 10 steps of the
last exposure block. The error bars indicate the standard error of the
mean. *p < 0.05.
is switched on, unexpectedly, they receive additional
support, which creates an overshoot. The subjects easily adapt
to the additional support and reach the target value again
within two steps.
Selective and gradual support
Providing step-height support to the stroke survivors
resulted in a selective increase in step height, without
significantly affecting the other basic gait parameters,
including the relative duration of the different gait phases
(see Figure 7). The support was also gradual, since the
use of the stiff controller resulted in a significant
increase in step height compared to the compliant
controller. The stroke survivors show a larger standard error of
the mean of the paretic step height, compared to the
right step height of the control group. For the compliant
controller the increase in nominal step height ranged
between 0 and 26 percent, whereas the increase in step
Figure 9 Effect of non-adaptive support on reliance. A: Mean
normalized step height during different steps of the trial in which
the healthy subjects walked with the compliant (HC) and stiff (HS)
step-height VMC. Mean parameters are calculated during the first
step of the exposure block, during the last step of the exposure
block, during the first step of the catch block and during continuous
exposure. Continuous exposure is based on the last 10 steps of the
last exposure block. B: Mean normalized step height during different
steps of the trial in which the stroke survivors walked with the
compliant (PCV) and stiff (PSV) step-height VMC, in combination
with visual feedback. Mean parameters are calculated during the first
step of the exposure block, during the last step of the exposure
block, and the first step in the catch block. The last exposure block,
with 50 steps of continuous exposure, was not included in the
protocol of the stroke survivors. The error bars indicate the standard
error of the mean. *p < 0.05.
height due to the stiff controller ranged between 0 and
44 Percent. For the stiff controller the average maximum
joint torques were 21.0 Nm hip extension and 17.4 Nm
knee flexion. The stroke survivors also show an
asymmetry in stance phase (see Figure 7), which is often
observed in stroke survivors .
Figure 10 Influence of adding visual feedback to the support.
Mean normalized step height during different steps of the trial in
which the healthy subjects walked with the stiff VMC in
combination with the visual feedback (HSV). The step height is
calculated during the first, second, and third step of the catch block,
during the first, second, and third step of the exposure block, and
during the last step of the exposure block. The error bars indicate
the standard error of the mean. *p < 0.05.
The experiments showed that the impedance-shaping
algorithm was effective in adapting the amount of support to
the stroke survivors individual capabilities on a
step-bystep basis (see Figure 11). Starting from the initial stiffness
(1200 N/m), the adaptive algorithm causes a gradual
increase in stiffness where a kinematic error persists, and a
clear reduction in stiffness where the ankle is above the
reference (i.e., a negative deviation from the reference in
Figure 11B). After 30 steps, the stiffness profile reached a
steady state, where the forgetting factor and the deviation
of the ankle from the reference pattern are in equilibrium.
Figure 11B demonstrates that the stiffness can be greatly
reduced without automatically compromising the overall
kinematic error. That is, the difference between
initial stiffness (1200 N/m) and final stiffness is much
clearer than the change in kinematic error.
With the impedance-shaping algorithm, the spring
stiffness was shaped such that it reflected the initial deviation
of the ankle from the reference trajectory for all patients
(see Figure 12). Although the stiffness converged to a
personal profile for each patient, the highest stiffness
occurred at the start of the swing phase for all patients.
Non-adaptive support does not induce reliance
Similar to the healthy control group, we did not find any
evidence that indicated that the stroke survivors started to
rely on the support. We did not find a significant
difference between the initial exposure and the end of the
exposure blocks (see Figure 10). This is also confirmed by
the catch blocks, which show that during support (with
the compliant or stiff VMC) the stroke survivors drop
back to their baseline, without any significant undershot.
Visual feedback enhances performance and active
To evaluate if stroke survivors can utilize the visual
feedback, we compared the trials where the stroke survivors
received adaptive support combined with visual feedback
(PAV), with the trials where the stroke survivors received
adaptive support only (PA). For this comparison, data from
only four patients was available, since the PA condition
could not be tested on two patients due to fatigue. In three
of the four patients, the mean stiffness over the last five
steps of the last exposure block was significantly lower
when the patients received visual feedback (see Figure 13).
This indicates that 1) these patients improved their
performance with the help of the visual feedback and 2) the
impedance-shaping algorithm lowered the impedance when
the patients improved their performance.
No reduction of compensatory strategies
During the experiments, the stroke survivors showed
different combinations, and degrees, of compensatory strategies
Figure 11 Typical example of the working principle of the impedance-shaping algorithm in a stroke survivor. Graph A demonstrates
how the stiffness shapes from a constant stiffness of 1200 N/m to a personal stiffness profile after around 30 steps for stroke survivor A3. Note
that the step-height VMC is only active during the double stance, with the non-paretic leg in front, and the paretic swing phase (approximately
50100 percent of the gait cycle), and that the stiffness has a lower limit of 0. Graph B shows the course of the deviation from the reference
pattern over multiple steps. The black area shows the error before the controller is switched on.
to overcome their reduced knee flexion. All patients
showed a larger paretic hip abduction range (hip
circumduction) and an increased pelvic height during the paretic
swing phase (vaulting). Figure 14 shows two typical
examples of stroke survivors with stiff-knee gait, who use a
vaulting strategy and/or a hip circumduction strategy. None of
the patients reduced their compensatory strategies during
the assistance. Although the use of the stiff controller
resulted in an average increase of 8.8 degrees in the
maximum paretic knee flexion, and all patients reported
that they felt the assistance in their paretic leg, we did not
find a significant reduction in the hip abduction of the
Figure 12 Shape of the stiffness profile after convergence. The
figure shows the initial deviation of the ankle from the reference
trajectory (gray) together with the converged stiffness profile (black).
For all patients, the stiffness shaped according to the initial deviation
from the reference.
The purpose of this study was to assess the effectiveness
of selectively supporting the step height during the
swing phase. First, we derived regression models for the
key events of the reference ankle-height pattern. These
models can be used to reconstruct patient-specific
reference patterns at any speed. The proposed step-height
VMC was tested on healthy subjects and chronic stroke
survivors, and proved effective in selectively influencing
the step height. Additionally, the step height could be
manipulated easily by changing the impedance levels.
Incorporation of an impedance-shaping algorithm resulted
in an adaptation of the impedance to the specific needs of
every individual stroke survivor. Catch trials were used to
investigate whether healthy subjects, or stroke survivors,
would start to rely on the robotic support, but revealed no
signs of reliance. The step height parameter was used to
provide intuitive visual feedback. Both groups were able to
utilize this feedback. We did not find evidence that the
stroke survivors reduced their compensatory strategies
when support was provided.
Reference pattern reconstruction
A large part of this paper concerns the reconstruction of
the reference patterns. Throughout the literature, different
strategies exist to determine these reference patterns.
Figure 13 Adding visual feedback to adaptive support. Mean stiffness over the course of the walking trial in which four stroke survivors (A1
(A), A4 (B), A8 (C), and A9 (D)) walked with the impedance-shaping algorithm with (PAV) and without visual feedback (PA). In three patients, the
mean stiffness over the last five steps of the last exposure block was significantly lower when the patients received visual feedback. This indicates
that 1) these patients improved their performance with the help of the visual feedback and 2) the impedance-shaping algorithm lowered the
impedance when the patients improved their performance. *p < 0.05.
Most reference patterns are based on pre-recorded
trajectories from unimpaired volunteers walking on a treadmill
[24,29,44,45], or based on walking in the device while it is
operated in a transparent mode [21,27], or with the
motors removed . Patient-specific patterns can be
obtained by recording the gait trajectories while the
patient walks with manual assistance [21,27], or by
defining joint patterns based on movements of the unimpaired
limb . Most methodologies, however, have certain
considerations that limit the use of the recorded trajectories
to a specific application.
A major limitation of most of these reference patterns is
that it is unknown how to correct for changes in speed.
Most pre-recorded trajectories are recorded at a limited
number of speeds, while the progress of the patients
preferred walking speed can be as small as 0.1 km/h. The
coupling between the right and left leg  will change at
different speeds and the recorded pattern, obtained during
manual assistance [21,27], will only be valid for that
specific speed. Scaling algorithms can be used to
compensate for changes in speed or cadence . Most scaling
algorithms, however, apply scaling in time, amplitude and
offset, whereas also the (relative) timing of the maximum
joint angles changes at different speeds.
For the patterns recorded in the gait trainer itself,
another limitation should be noted. Due to the mass and
inertia of the device, and/or imperfections of the
transparent mode, these patterns might not match with the
ones recorded during free walking. Emken, et al., found
that the added inertia resulted in a slightly higher
stepping pattern compared to free walking , while
others found a significant and relevant decrease in knee
angular range due to the device .
Most pre-recorded trajectories are obtained by
rescaling the gait pattern to a percentage of the gait cycle, and
taking the mean across subjects. This introduces another
issue. Averaging normalized data can result in an
underestimation of the extremes in the gait pattern, when the
subjects have a different distribution of the gait events
throughout the gait cycle .
Therefore, we developed a method where the gait
pattern is parameterized by defining different key events
(minima, maxima etc.), which all have a timing, position,
and velocity. Next, the walking speed and body-height
dependency of the parameters are determined by
regression models. This way, the extreme value in the
reconstructed pattern is actually based on the extreme values
of the individual patterns, even when the extremes occur
at another percentage in the gait cycle.
Another advantage of the proposed method, compared
to other available methods, is that it can be used to
construct a reference ankle-height pattern at each particular
walking speed between 0.5 and 5.0 km/h, for persons
with different body heights. This allows the physical
therapist to easily increase the training speed, even
within a single walking session. Speed-dependent
reference pattern adjustments are also essential when the
patient is in control of the walking speed, either
manually or with intuitive speed-adaptation algorithms .
The proposed method is also generally applicable and
can be applied to reconstruct speed-dependent reference
patterns for joint angles. Different studies have already
Figure 14 Two typical examples of gait adaptations seen in stroke survivors. Both patients (A4 (left) and A1 (right)) show one or more
compensatory strategies to overcome a reduced foot clearance due to stiff-knee gait (A and B). They show an increase in hip abduction (C and
D), and an increased pelvic height during the paretic swing phase, compared to the non-paretic swing phase (E and F).
shown that peak joint kinematics are dependent in a
linear and/or quadratic way on walking speed , and
that its occurrence (timing) is also speed dependent .
There were some limitations in deriving the regression
equations, which are related to the relatively low number
of subjects (11) in this study. Due to this small number,
we did not derive separate equations for male and
female subjects, whereas systematic effects of gender of
kinematics have been reported . The range of body
heights in this group of subjects was limited (1.52 m to
1.86). However, this range is expected to be sufficient for
the majority of the elderly population.
Selective and gradual support
The results from the stroke survivors and healthy
control group showed that the step-height VMC could
selectively influence the step height. Supporting the step
height did not significantly affect other spatiotemporal
gait parameters, like non-supported step height, step
length, or relative gait phase duration. Although the
subjects were free to adapt their cadence, no change in
cycle time was observed. As expected, the support was
also gradual, a higher stiffness resulted in a closer
approximation of the target values.
On average, the stroke survivors received more
supportive hip and knee torque. At baseline, the stroke
survivors walked more below the reference than the
healthy subjects, resulting in more supportive torque.
The stroke survivors also showed a larger standard
error of the mean of the paretic step height, compared
to the right step height of the control group. For the
healthy subjects, the reference ankle-height pattern was
scaled such that it reached a 15-percent increase with
respect to their nominal maximum ankle height. For the
stroke survivors, the reference pattern was purely based
on the regression formulas. The stroke survivors who
were less affected, and almost reached their target value
without support, showed a smaller (relative) increase in
step height, compared to the patients that performed
less without the support.
Selective-subtask-support already allowed us to focus
the robotic support on the subtasks that are impaired.
However, also within a subtask, the amount of support
needs to be minimized to the personal needs of the
patient. Aoyagi, et al., already suggested that by scheduling
the impedance as a function of the gait cycle, the
assistance can be further personalized . This, however, is
impossible for the operator to manually adjust.
Therefore, we chose to adopt an adaptive algorithm, that
shaped the impedance based on the tracking
performance, that was suggested by Emken, et al. .
Emken, et al., reported that the impedance converged
repeatedly over separate trials . Although we only
performed one trial with the adaptive algorithm per
patient, the impedance profile shaped according to the
initial error between ankle and the reference trajectory for
all patients. This indicates that the shaped impedance
was directly related to the patients incapabilities.
All stroke survivors converged to a stiffness profile
where the stiffness was highest at swing initiation. This
is in agreement with the trials where a constant stiffness
was used. There, most of the assistive torques were
exerted during swing initiation, indicating that that phase
requires most of the support. Because of the provided
torques during initial swing, the leg was propelled
upward with a higher velocity, and required less support
during the remainder of the swing phase. Anderson,
et al., already demonstrated the importance of knee
angular velocity at swing initiation in normal gait. They
showed that the knee angular velocity at heel off was the
main determinant for the maximal knee angle, and foot
clearance, during swing . Reduced angular velocity,
and foot clearance, in stiff-knee gait is suggested to be
caused by an abnormal knee flexion during swing
initiation. Kerrigan, et al., and Riley, et al., found an
inappropriate activity in at least one of the quadriceps muscles
during the pre-swing or initial swing phase [55,56],
which inhibit a normal knee flexion. Kerrigan, et al. ,
also reported that patients with delayed heel rise
achieved less peak knee flexion. The patients included in
this study also showed a delayed heel rise. So, providing
support during this phase seems like a natural, and the
most effective, way to increase the maximum knee angle,
and subsequently foot clearance.
Apart from shaping the impedance to the patients
individual needs, minimizing the impedance also allows more
variability within the stepping pattern, which has been
shown to promote motor learning in mice . Emken,
et al., reported an increase in variability in maximum step
height and step length, but could not verify whether
increasing the variability during gait training had a positive
effect on EMG activity levels . In our study, we did not
investigate the variability within the gait pattern. We did
see a clear reduction in the impedance levels where the
stroke survivors required less support, which allows them
to vary their steps in a more natural way compared to
walking with a stiff controller. The possibility to make
small gait variation was also promoted by using a
unidirectional spring that only provided support in taking a
higher step, thus not constraining the ankle when it
reached above the reference.
Based on previous pilot experiments , and
computational models of movement training , we hypothesized
that the stroke survivors and healthy controls would start
to rely on the support, such that when assistance is no
longer provided, their performance becomes worse.
Previous studies, that let subjects adapt to external force fields,
already showed that the human motor system can be
modeled as a process that greedily minimizes a cost function,
consisting of a weighted sum of kinematic error and effort
[58,59]. In these studies, a forgetting factor is introduced in
the human effort, which models that the human
continuously tries to accomplish the prescribed movement with
In this study, however, we did not find patients, or
healthy subjects, who started to rely on the support. A
likely explanation for the patient group could be the
visual feedback, which we did not use in the pilot
experiment . The visual feedback provided them with
information about their performance on a step-by-step
basis, increasing their motivation and reducing the
changes of reliance.
Also in the healthy control group, who did not receive
visual feedback in most trials, we did not observe
reliance. To evaluate the effects of prolonged exposure, the
trials were concluded with 50 steps of continuous
exposure. This block might have been too short for the
subjects to explore the benefits of the support and start to
rely on it. The relatively low impedance levels might
contribute to this effect.
Also the task instruction and type of support might
explain our findings. In most motor learning
experiments, a disturbing force field is applied and the subjects
are asked to reduce the error. To reduce the error, the
subjects have to produce additional effort to overcome
the disturbance. During this process, they continuously
try to minimize the trade-off between reducing their
effort and increasing the error [58,59]. In our experiments,
the subjects experience a force field that decreases,
rather than increases, the performance error. Here, the
subjects are not challenged to provide additional effort,
which might not elicit them to reduce their effort.
The fact that a relative small movement error can
cause the subjects to trip might also have contributed to
the fact that these subjects did not start to rely on the
support. This would indicate that the weight of the error
in the cost function increases compared to the reduction
in effort. Bays, et al., already suggested that humans can
change the weighting of different costs, according to the
task and type of the movement .
Although the chances that reliance will occur are
reduced by minimizing and localizing the support with
the impedance shaping algorithm, two issues remain.
First, the algorithm cannot distinguish between a
decrease in effort due to reliance or due to fatigue. In both
cases, the algorithm will increase its support. Second,
subjects might still, consciously or unconsciously, reduce
their effort over time and consequently receive more
support. Emken, et al., showed that, to effectively
assistas-needed, the robot must reduce its assistance at a rate
that is faster than that of the learning human . They
stated that reliance can be prevented by setting the
forgetting factor to a lower value than the learning rate of
the subjects. They also state that determining the
learning rate for neurological patients can be difficult because
of their impaired motor control due to spasticity, muscle
weakness, and synergies. Therefore, we chose to set the
forgetting factor based on a stable convergence of the
stiffness pattern within approximately 30 steps.
Finally, one might argue that to eliminate reliance one
should apply resistive forces rather than supportive forces.
In fact, error-enhancing therapy is suggested to be more
effective than assistive therapy [20,61]. For some training
exercises, where movement errors do not impose serious
safety issues, this might be true. For robotic treadmill
training, where small movement errors can have large
consequences, this strategy may be inappropriate.
In this study, very simple visual feedback was provided in
the form of the step-height parameter. We showed that
both the stroke survivors and the healthy controls were
capable of utilizing this information effectively. Providing
visual feedback to the healthy controls led to a very close
approximation of the reference values. Adding visual
feedback to the trials, in which the stroke survivors received
adaptive support, led to lower impedance levels in three of
the four patients, indicating that these patients are
additionally motivated by the visual feedback.
The key element of any form of feedback is that it
displays the subjects effort in an intuitive manner. Different
forms of feedback are available. A review performed by
Teasell, et al. , concluded that there is a positive
effect of EMG feedback in patients after stroke. Others
use the subjects kinematics to display their performance
[25,29], or the interaction force between user and robot,
like in the Lokomat .
A disadvantage of the latter approach is that it is only
applicable to position-controlled gait trainers. In these
type of gait trainers, the additional effort of the subject
is reflected on the screen, but is not reflected in their
gait pattern. This might decrease the motivation of the
subject. Thus, to optimize visual feedback, the gait
trainer needs to be compliant. In more recent versions
of the Lokomat, Duschau-Wicke, et al., introduced a
more patient-cooperative strategy, effectively making the
robot more compliant . In their study, they used
body kinematics as visual feedback.
To optimize the feedback, factors like the amount of
information and its frequency need to be investigated. Also
the complexity of the feedback is important - do we need
detailed information from every joint, or combined
information from several joints, like the ankle position? Banala,
et al. , only displayed the ankle position in the sagittal
plane. Our results suggest that only showing the
maximum ankle height of the last step is already sufficient to
control the hip and knee joint such that the subject takes
a higher step. Also, for the therapist himself, we expect
that feedback in the form of basic gait parameters will be
easier to interpret, compared to joint angles or ankle
The primary goal of visual feedback is, of course, to
contribute to the long-term changes in relearned gait
kinematics. Kim, et al. , used the ALEX to induce
gait modification in healthy adults. They reported that a
combination of visual and force guidance resulted in
larger modifications in step height that maintained longer,
persisting up to two hours, whereas only visual guidance
or only force guidance evoked changes that did not last
beyond the 10-min retention test. Although we did not
investigate retention, our experience with visual
feedback is encouraging, and can serve as a starting point in
the investigation about how to optimize gait training in
such a way that short-term gait adaptation can become
long-lasting gait modification.
The VMC approach, used in this study, is an
end-pointbased-control strategy. This implies that within a certain
subtask there is more freedom to walk, or choose a
certain strategy. For example, different patients might
choose different strategies to accomplish appropriate
foot clearance. With the step-height VMC, the patients
are left free in the strategy they use to clear their foot
and will only receive support when this task is not
executed successfully. This means that compensatory
strategies [64,65], like pelvic hiking, hip circumduction, or
vaulting [66,67], which are seen in most stroke survivors,
can still be employed. Joint control limits the use of
these strategies. Additionally, imposing a symmetrical
joint-angle pattern limits the possibility of the
nonparetic leg to compensate for the deficiencies of the
paretic leg. Although these compensatory strategies do not
contribute to a more symmetric walking pattern, they do
increase basic gait function [68,69]. Some even advocate
teaching compensatory strategies because of time and
financial limitations . Thus, because it is still largely
debated whether the focus of robotic gait training should
be on restitution of a normal walking pattern or on these
compensatory strategies, they should not be overruled.
The use of these compensatory strategies might even
become redundant when support is provided on the
impaired subtask that evokes these compensatory
strategies. We hypothesized that providing support on one
subtask, i.e. foot clearance, would reduce the need for the
patient to employ his compensatory strategies. Although
all our stroke survivors showed compensatory strategies
without support, none of them reduced their compensatory
strategies with support. During the experiments, the stroke
survivors received no specific instructions about how to
walk on the treadmill. Therefore, they might not have been
triggered to reduce their compensatory strategies. Also, the
limited time that the stroke survivors walked in the LOPES
during the experiments, in combination with the amount
of time it would take to un-teach their adapted strategies,
might be a reason for the unchanged kinematics. In the
future, we might even develop special VMC modules that
suppress compensatory strategies to promote restitution of
a symmetrical walking pattern.
Different support methods have been suggested to correct
the gait pattern of neurological patients. However, none of
the compliant, or interactive, support methods has been
evaluated in large-scale clinical trials. To guide potential
clinical trials, the differences between our and other
approaches will be explained. The method presented in
this paper can be best compared to the virtual tunnel
approach. Banala, et al., implemented this virtual tunnel
approach, which was previously described by Chai, et al.,
, and trained two chronic stroke survivors with the
ALEX . Their tunnel consisted of a healthy-control
template and the assistance was composed of a normal
force, that simulated the virtual walls, and a tangential
force that helped the ankle move along the trajectory. A
similar virtual tunnel strategy is implemented in the
Lokomat to train iSCI patients. Duschau-Wicke, et al., also
implemented a moving window that limits free
movement to a region of the tunnel, similar to the tangential
force in the ALEX . In contrast to Banala, et al., they
defined a torque field in joint space rather than a force
field in Cartesian space. There are three main differences
between the above-mentioned control strategies and the
control strategy presented in this study.
First, both the ALEX and the Lokomat use some sort
of support that potentially helps the ankle, or joint,
move along the trajectory. The tangential force, used by
Banala, et al. , decreases when the ankle deviates
from the trajectory, thus the ankle is only pushed along
the path when the ankle is close to the desired
trajectory. Duschau-Wicke, et al.  use the moving window,
that is synchronized with the users cadence , to
assist the user. In our study, no tangential force, or moving
window, is used. Within the subtask-support strategy,
step timing and foot clearance are two separate subtasks.
Here, we only supported foot clearance. This allowed
the subjects to freely change their timing, if they wished
to do so. Still, subjects did not adapt their timing. For
bilateral affected iSCI patients, who experience difficulties
during swing initiation, or gait initiation in general,
gaittiming assistance might be useful. In that case an
additional VMC in the horizontal plane can be added. Our
experience with stroke survivors suggests that the
nonparetic leg can take care of the gait timing and the
paretic leg will follow.
Second, both studies use a virtual tunnel that lifts the
ankle , or increases joint angles , but can also do
the opposite when the subject performs above the
reference. In this study, a unidirectional spring was used,
because the support is intended to support the subject in
taking higher steps, and not push the ankle downwards,
when the ankle is above the reference.
Third, in contrast to the Lokomat, the support of
subtasks is an end-point-based-control strategy, rather than
a joint-angle-based-control strategy. As mentioned
before, joint-angle-based-control strategies exclude the use
of compensatory strategies.
Future applications of selective support
The key goal of future research is to expand the concept
of subtask support. Support in taking higher steps is an
important part of the rehabilitation process, but other
subtasks might also require assistance. A new VMC, that
assists patients in taking more symmetric steps, is
currently under development, and its interaction with other
subtask controllers is being investigated.
For severely affected patients, body weight support
systems (BWSS) are often used. Alternatively, VMC can
also be used to partially support the body weight by
attaching a vertical virtual spring to the hips. In that
case, the forces, required to bear your own body weight,
are provided in terms of hip and knee torques, rather
than lifting the body externally. This allows normal
sensory input from the foot soles, which is essential in order
to generate natural gait kinematics [72,73]. VMC for
body weight support also allows easy modulation of the
amount of support between the different legs, since
stroke patients primarily need support during the stance
phase of the affected leg. It also enables separate control
of body weight support and balance support, which can
be considered as two separate subtasks, either of which
can be impaired to a certain degree. BWSS, with an
overhead harness, not only provide a force in the pure
vertical direction, but also in the horizontal plane that
stabilizes the body. Pilot experiments have shown the
feasibility of body weight support with VMC . The
possibilities of VMC for balance support are now being
Finally, we started preliminary tests with an intuitive
speed-adaptation algorithm, in which the patient can
move freely over the treadmill and the speed is
automatically adapted when the patient deviates from the center of
the treadmill. In conjunction with the obtained
speeddependent reference patterns, this will provide the
therapist and patient with tools to easily adapt the treadmill
speed to the capabilities and progress of the patient,
without the need to manually change the control settings.
In this study we implemented, and evaluated, a VMC
strategy for selective and gradual support of gait subtasks. Here
we focused on one specific subtask, i.e. increasing foot
clearance. Initially, we derived and provided regression
models that can be used to reconstruct patient-specific
ankle movement patterns based on body height and
walking speed. The RMSE between the predicted and actual
trajectory was around 1 cm for all walking speeds. The
proposed method can also be applied to reconstruct
speeddependent reference patterns for joint angles. Experiments
with healthy subjects, and chronic stroke survivors, showed
that with the proposed VMC approach, the step height
could be selectively and gradually influenced, without
affecting other spatiotemporal gait variables. In
conjunction, we tested an impedance-shaping algorithm, which
shaped the impedance to the patients individual needs. The
provided support did not result in reliance on the support
for both the stroke survivors as well as the healthy control
groups. Providing visual feedback to the user resulted in an
increased active contribution in all healthy subjects and
three of the four stroke survivors. The presented VMC
approach, and impedance shaping, can be crucial for the
development of new rehabilitation strategies and robotic gait
trainers. It allows automatic localization and minimization
of the support, which increases active patient contribution
and promotes functional recovery.
BK and EA carried out the experiments, collected and processed the data,
and wrote the manuscript. HK participated in the design of the study and
contributed to the revision of the manuscript. All authors read and approved
the final manuscript.
This study was supported by a grant from Dutch Ministry of Economic affairs
and Province of Overijssel, the Netherlands (grant: PID082004). We would like
to thank Jaap Buurke and Martijn Postma, from the Roessingh Research and
Development, for their assistance during the patient experiments.
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