The effect of impedance-controlled robotic gait training on walking ability and quality in individuals with chronic incomplete spinal cord injury: an explorative study
Journal of NeuroEngineering and Rehabilitation
The effect of impedance-controlled robotic gait training on walking ability and quality in individuals with chronic incomplete spinal cord injury: an explorative study
Bertine M Fleerkotte 2
Bram Koopman 1
Jaap H Buurke 2
Edwin H F van Asseldonk 1
Herman van der Kooij 0 1
Johan S Rietman 1 2
0 Department of Biomechanical Engineering, Delft University
1 Institute for Biomedical Technology and Technical Medicine (MIRA), Department of Biomechanical Engineering, University of Twente , Enschede , The Netherlands
2 Roessingh Research and Development , Enschede , The Netherlands
Background: There is increasing interest in the use of robotic gait-training devices in walking rehabilitation of incomplete spinal cord injured (iSCI) individuals. These devices provide promising opportunities to increase the intensity of training and reduce physical demands on therapists. Despite these potential benefits, robotic gait-training devices have not yet demonstrated clear advantages over conventional gait-training approaches, in terms of functional outcomes. This might be due to the reduced active participation and step-to-step variability in most robotic gait-training strategies, when compared to manually assisted therapy. Impedance-controlled devices can increase active participation and step-to-step variability. The aim of this study was to assess the effect of impedance-controlled robotic gait training on walking ability and quality in chronic iSCI individuals. Methods: A group of 10 individuals with chronic iSCI participated in an explorative clinical trial. Participants trained three times a week for eight weeks using an impedance-controlled robotic gait trainer (LOPES: LOwer extremity Powered Exo Skeleton). Primary outcomes were the 10-meter walking test (10MWT), the Walking Index for Spinal Cord Injury (WISCI II), the six-meter walking test (6MWT), the Timed Up and Go test (TUG) and the Lower Extremity Motor Scores (LEMS). Secondary outcomes were spatiotemporal and kinematics measures. All participants were tested before, during, and after training and at 8 weeks follow-up. Results: Participants experienced significant improvements in walking speed (0.06 m/s, p = 0.008), distance (29 m, p = 0.005), TUG (3.4 s, p = 0.012), LEMS (3.4, p = 0.017) and WISCI after eight weeks of training with LOPES. At the eight-week follow-up, participants retained the improvements measured at the end of the training period. Significant improvements were also found in spatiotemporal measures and hip range of motion. Conclusion: Robotic gait training using an impedance-controlled robot is feasible in gait rehabilitation of chronic iSCI individuals. It leads to improvements in walking ability, muscle strength, and quality of walking. Improvements observed at the end of the training period persisted at the eight-week follow-up. Slower walkers benefit the most from the training protocol and achieve the greatest relative improvement in speed and walking distance.
Spinal cord injury; Robotic gait rehabilitation; Locomotor training; Impedance control
Spinal cord injury (SCI) affects 10.4  to 83  per
million individuals per year (in developed countries), leading
to an estimated prevalence that ranges between 223 and
755 per million inhabitants . Learning to walk again is a
major goal during SCI rehabilitation [4,5]. Generally, more
than 50 percent of patients have motor incomplete lesions
(iSCI) , of which around 75 percent regain some
ambulatory function . Still, many iSCI individuals experience
limited hip flexion during swing phase and insufficient
knee stability during the stance phase. Consequently, these
individuals walk slower and often remain reliant on
assistive devices. Over the last decades, many rehabilitation
strategies have been explored to improve functional
outcomes. Most are based on evidence suggesting that
taskspecific and intensive training, consisting of repetitive
active movements and providing appropriate afferent
feedback, engages spinal and supraspinal circuits, promoting
neural plasticity (cortical reorganization) and increasing
functional improvement [7-14].
Robotic gait-training devices have the potential to provide
training sessions that support these key components. These
devices reduce the labour-intensive demands on therapists
and their discomfort, compared to manually assisted
bodyweight-supported treadmill training (BWSTT) . They
also enable objective monitoring of a patients performance
and progress and reduce the between-trainer variability in
terms of the applied supportive forces . In the last
decade, different robotic gait-training devices have been
developed that are also used for other motor impairments, like
stroke or multiple sclerosis. These robotic devices consist of
a driven exoskeleton orthosis, like the Lokomat (Hocoma
AG, Switzerland) or Auto/ReoAmbulator (HealthSouth/
Motorika, USA) that drives the hip and knee joint, or driven
footplates that facilitate a stepping motion like the Gait
Trainer (Reha-Stim, Germany), G-EO (RehaTechnologies)
or LokoHelp (LokoHelp Group, Germany).
Although these robotic gait-training devices have been
on the market for more than a decade, research on their
efficacy is still at an early and rather inconclusive state.
On the one hand several studies showed improvements
in walking ability between pre- and post-training in
acute and chronic iSCI individuals who trained with the
Lokomat [14,17-20] or Gait Trainer [19,21]. On the
other hand, only very few randomized controlled trials
(RCTs) [22-25] or other study designs [19,26], were
performed to investigate whether these improvements are
superior to those obtained using conventional
approaches. Results from these studies, however, show
contradictory results. Recent reviews have also
concluded that robotic gait-training devices have not yet
demonstrated clear advantages over conventional
gaittraining approaches in terms of effectiveness of training
The limited effectiveness of the first-generation robotic
gait-training devices could be attributed to some
inherent limitations of these devices, which were mainly
position-controlled. This type of assistance may promote
slacking, where the user starts to rely on the robot to
perform the movement and reduces his muscular
activity [30,31]. In iSCI individuals, position-controlled
robotic guidance, especially in individuals with some
ability to walk, has been shown to actually reduce
volitional activity (EMG and VO2) compared to
therapistassisted BWSTT [25,32]. For motor learning in general,
active subject participation is considered a very
important factor [33,34]. If, conversely, participants are
encouraged to actively participate, they could be resisted by the
position-controlled robot, causing abnormal alternations
in muscle-activation patterns .
Another limiting factor of position-controlled robots is
that they reduce movement variability to a minimum
. Kinematic variability, and the possibility to make
movement errors is necessary to (re)learn any new task
[37,38]. In this respect, traditional robotic gait-training
devices only partly meet the requirement for
taskspecific, intensive, active, and variable training. In other
words, they do not resemble the manual assistance
provided by a therapist who is likely to be compliant,
motivational, and intuitively adaptive to the needs of the
individual and who inherently introduces a natural sense
This situation demonstrates the need to develop and
improve control approaches that increase active
participation and natural movement variability. This can be
achieved by only providing assistance when needed, and
not supporting the subjects movements that are
unimpaired. Technical implementation of this strategy often
consists of controlling the interaction forces between the
robot and the patient. Generally, these control strategies
use a healthy control spatial path to define the desired
motion, in combination with a virtual wall/force field
that determines the amount of supportive force when
the individual deviates from the template (impedance
control). In some cases a moving back wall is
introduced to assist the timing of the stepping pattern
This kind of control strategy can overcome the main
criticisms against robot-aided gait training by making
the robots behaviour more flexible and adaptive to the
users needs. That is, the stiffness of the virtual wall/
force field can be adapted to the capabilities, progress,
and current participation of the user. This allows
individuals to benefit from robot-aided treadmill training
throughout the different stages of their recovery. At the
initial stages of recovery, the robot can take charge (high
impedance), whereas at the concluding stages of
recovery, the user must contribute more to the prescribed
motion (low impedance). To reduce the chance of the
user becoming reliant on the support, some robotic
gaittraining devices use adaptive (impedance shaping)
algorithms that reduce the stiffness of the virtual wall
when kinematic errors are small [41,42]. Flexibility
between steps and the possibility of making small
movement errors can be increased by lowering the impedance
levels or by creating a virtual tunnel, dead band, or
nonlinear force-field around the healthy control
Lowering impedance levels might also increase
motivation during training sessions. At lower impedance
levels, the user has more control over his gait pattern,
and additional effort/voluntary movement is reflected in
the gait pattern. This way, individuals are aware of their
increased activity, a sensation that can positively
contribute to their active involvement. These types of
controllers that 1) provide more freedom of movement, 2) only
focus on the impaired aspects of gait, 3) promote active
participation and 4) allow online modification of the
amount of assistance (either manually or automatically),
are referred to as assist-as-needed (AAN),
cooperative, adaptive, or interactive controllers [39-47].
Despite the potential of AAN strategies, the superiority
of this approach for iSCI individuals has not been
demonstrated. In animal studies, Cai et al. and Ziegler et al.
showed that AAN control algorithms allow more variation
between steps and result in larger walking recovery than
position-control algorithms [48-50]. Despite numerous
experimental robotic gait-training devices that have been
developed , very few of the new compliant-control
strategies have been tested on iSCI individuals in
multisession training protocols. In single-session experiments,
Emken et al. showed that iSCI individuals trained with
more variability when they used their impedance-shaping
algorithm . Duschau-Wicke et al. evaluated their
patient-cooperative approach in a single training session
and showed that iSCI individuals trained with larger
kinematic variability, and with larger muscle activity,
compared to non-cooperative position-controlled training
. Schck et al. evaluated this approach in a
multisession training protocol. They used the Lokomat to train
two iSCI (and two stroke) individuals for four weeks, with
four training sessions of 45 minutes per week, However,
they did not find a relevant increase in gait speed for iSCI
Most studies on robotic gait training only assess walking
ability. They report functional outcome measures and
clinical scales, like walking speed (10-Meter Walking
Test), distance (Six-Minute Walking Test), or walking
ability (WISCI II). Only a few studies assess the effect of
robotic gait training on walking quality, in terms of
spatiotemporal and kinematic measures [23,25]. Assessment of
walking quality can provide useful insights into whether
gait training restores walking function by restoration of
function (using more normal movement patterns) or by
The aim of this study was to evaluate the feasibility and
effect of an eight-week, multi-session training protocol
using an impedance-controlled gait trainer. The effect of
training was assessed in terms of walking ability and
walking quality. Individual assessments were used to determine
which individuals were most likely to benefit from the
training protocol. To evaluate if training effects were
retained post-training, we performed follow-up testing
eight weeks after completion of the training protocol.
Subjects with chronic, motor-incomplete SCI (iSCI)
were recruited from Het Roessingh Centre for
Rehabilitation in Enschede, The Netherlands. Inclusion criteria
were iSCI sustained at least a half year prior to
enrolment, age above 18 years, a stable medical condition, a
physical condition that allows for three minutes of
supported walking, the ability to bear their own body weight
while standing, not currently enrolled in gait training
therapy, and a stable dose of anti-spastic medicine
during the study. Exclusion criteria were current orthopedic
issues causing problems in walking or balance, the
presence of other neurological disorders, a history of cardiac
conditions that interfere with physical load, the absence
of independent ambulation prior to SCI, chronic joint
pain and inappropriate/ unsafe fit of the robotic trainer
due to the participants body size (bodyweight > 100 kg)
and/or joint contractures. All subjects provided written
informed consent including permission for publication,
prior to admittance to the study. The study protocol was
approved by the local medical ethics committee, METC
Twente (Enschede, The Netherlands).
Gait training was done with the prototype of the LOPES
gait rehabilitation robot (Figure 1). LOPES consists of a
bilateral exoskeleton-type rehabilitation robot above an
instrumented treadmill. It is lightweight and impedance
controlled using Bowden-cable-driven series-elastic
actuators. 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).
Passive foot lifters can be added to induce ankle dorsal
flexion, if required. An external bodyweight-support
system can relieve a definable percentage of body weight via
a harness. A more detailed description of the exoskeleton
design is presented in .
Figure 1 LOPES robotic gait trainer.
In this study, the amount of assistance that the participant
receives is proportional to the deviation from a template
or reference walking pattern. This reference walking
pattern is derived from speed-dependent walking patterns in
healthy participants. Details about the derivation of these
reference patterns can be found in . This method was
implemented such that, when the therapist changed the
treadmill speed, the joint trajectories were automatically
adjusted to that specific walking speed (Figure 2).
The amount of robotic support was adjusted by
changing the stiffness of the impedance controller. The
impedance levels were set to a participant-specific percentage of
the maximum stiffness that could be controlled by the
LOPES (300 Nm/rad). In this study, the same percentage
was used for hip and knee joints and for the left and right
leg. To enable the participant to stay in control of his
cadence, the reference walking pattern is not replayed as a
function of time but is synchronized to the cadence of the
Subjects participated in an eight-week training program.
Participants trained three times per week, for a
maximum of 60 minutes per session. The training period
was divided in two four-week periods, with one week
scheduled for clinical tests in between. During training
sessions, rest intervals were introduced if required by
the participant or suggested by the therapist. The first
training session was used to 1) fit the LOPES to the
subject, 2) let participants get used to walking in the device
and 3) select their preferred walking speed.
To fit the LOPES to the subject, different
anthropometric measurements were taken to adjust the
exoskeleton segment lengths. Next, the subject was positioned
into the LOPES and the trunk and lower extremities
were secured. Three adjustable cuffs (one at the thigh,
two at the shank) attached the lower extremities to the
LOPES frame. Final adjustments were made to the cuffs
to align the subjects hip and knee joints with the axes of
the exoskeleton joints. Bodyweight support was set at a
minimal amount for each participant, preventing
excessive knee flexion during stance phase or toe dragging
during swing phase. Foot lifters were used in case of
insufficient ankle dorsal flexion during swing phase.
During all training sessions, the LOPES operator was
paired with an experienced physical therapist. Over the
training period, different parameters were adjusted to
increase training intensity. Walking speed was the first
parameter to be increased when possible. Subsequently,
the total training time per session was increased and BWS
levels were decreased. To promote active patient
participation, the impedance levels of the LOPES were reduced
when possible. This controller could vary between very
stiff (robot-in-charge) to very flexible (patient-in-charge).
Lower impedance levels also allowed more variability in
the stepping trajectory (Figure 3) .
Adjustments of training parameters were done by the
physical therapist based on the quality of walking
(adequate step height during swing phase and adequate knee
stability during stance phase), current physical condition
(observation of breathing rate and degree of transpiration),
and motivation (as verbally indicated by the participant).
All changes were made in agreement with the participant.
All training parameters were stored for later analysis.
Primary outcome measures
To assess changes in muscle strength and walking
ability, clinical tests were performed before (pre), during
(mid), and after (post) eight weeks of training. To
examine whether the training effects were retained, we also
performed a follow-up eight weeks after the completion
of the training protocol.
Walking speed was measured using the 10-Meter
Walk Test (10MWT). Participants were instructed to
walk in a straight line at their own comfortable speed.
Distance/endurance was tested with the Six-Minute
Walk Test (6MWT), where participants ambulated for
six minutes at their self-selected speed. The Timed Up
and Go (TUG) test assessed the combination of balance
during walking, gait speed, and sit-to-stand transitions.
In this composite test, the patient must get up from a
chair, walk 3 meters, return, and sit down again. For
these three tests, participants were permitted to use
braces and walking devices. The Walking Index for
Spinal Cord Injury II (WISCI-II) was used to quantify
the amount of assistance required during over-ground
ambulation and to assess the use of assistive devices
and/or orthoses. Category 0 indicates the participant
could not walk or stand and category 20 indicates the
Figure 3 Typical example of hip and knee reference trajectories and actual joint trajectories for a healthy subject walking at 2 km/h
using different impedance levels. Increasing the impedance levels results in a closer approximation of the reference trajectory and a reduction
in the movement variability between steps. Here, the reference knee angle is enlarged by 10 percent to ensure that the robot provides support
(since the healthy subject is expected to walk according to the healthy reference trajectory).
participant could walk at least 10 m without assistance
or use of assistive devices. All of these measures were
taken according to van Hedel et al. . Muscle strength
was determined by the Lower Extremity Motor Scores
(LEMS), utilized by the American Spinal Injury
Association (ASIA). The strength of five key muscles are
graded from 0 to 5 (0 indicates absence of muscle
contraction and 5 is a normative active movement with full
range of motion against full resistance). The cumulative
score for lower extremities is between 0 and 50 . All
measures were recorded by an experienced physical
therapist, not involved in the training.
Secondary outcome measures
To assess changes in gait quality, kinematic data and
spatiotemporal measures were taken pre- and post-training.
Gait kinematics were recorded using an optical tracking
system, consisting of six infrared cameras (Vicon PlugIn
Gait Model, VICON, Oxford Metrics, Oxford, UK) and
reflective markers. Participants walked at their preferred
speed across a 7-meter walkway approximately 10 times
and were allowed to rest between rounds. Kinematic data
from right and left limbs of each participant were
extracted and averaged over at least 10 steps, using
customwritten software (MATLAB, Mathworks Inc., Natick, MA,
USA). The use of assistive walking devices and orthotic
devices for safe over-ground walking was allowed (and
kept constant during the pre- and post-measurements).
A total of nine parameters were extracted from the
kinematic data: walking speed, cycle time, step symmetry index,
step length, step width, relative stance phase duration,
maximum knee flexion during the swing phase, range of motion
(ROM) of the knee during the stance phase (initial- and
mid-stance) and hip ROM. These parameters were used for
comparison between pre- and post-training.
Cycle time was defined as the time between two
consecutive heel strikes of the same leg. Range of motion of
the knee during the stance phase was used to assess
knee stability during the stance phase. Step width was
determined as a measure of gait stability . The step
symmetry index was calculated according to equation 1.
SLs represents the step length of the stronger leg and
SLw the step length of the weaker leg. Here, a symmetry
index of zero indicates perfect symmetry between the
two legs. Similarly to Nooijen et al. , the stronger leg
was defined as the leg that, on average, made the largest
steps during the pre-test. In all participants, the weak leg
during the pre- and post-training remained the same.
The step length, relative stance phase duration,
maximum knee flexion during the swing phase, ROM of the
knee during the stance phase and the hip ROM were also
separately calculated for the weaker and stronger leg.
Measurements of walking ability were assessed pre-,
mid-, and post-training and at follow-up. Because of
the lack of normally distributed data (determined by
Shapiro-Wilk test) and the relatively small number of
participants, nonparametric statistical tests were used to
detect changes throughout the training period. Statistical
analysis was done on the absolute values for all
measurements. To assess the effect of the training protocol on
functional outcome (10MWT, 6MWT, WISCI II, TUG
and LEMS), the Friedman analysis of variance by ranks
was used, with P < 0.05. Post-hoc comparisons were
performed using the Wilcoxon signed-rank test and a
Bonferroni correction to account for multiple
comparisons (P < 0.017). To assess retention of the functional
level at follow-up, a Wilcoxon signed-rank test was
performed to detect changes between post-training and
follow-up with significance P < 0.05. Spearman
correlation coefficients were calculated to identify possible
correlations between the initial performance on the
walking ability tests and the absolute change in these
measures (P < 0.05). Measurements of walking quality
(kinematic and spatiotemporal measures) were only
assessed pre- and post-training. Changes in walking
quality between pre- and post-training were determined
with the Wilcoxon signed-rank test (P < 0.05). All
statistical tests were performed with SPSS Statistics (IBM
Corp., Armonk, NY, USA).
A total of 12 participants with iSCI were included.
Participant characteristics are listed in Table 1. Two
participants dropped out (subjects 6 and 12). They did not
complete the training due to medical reasons not related
to the gait training.
Over the eight-week period, a mean number of 20.2
(range, 18- 24) training sessions were completed by the
10 participants. Due to reasons unrelated to the gait
training, some participants had to cancel some training
sessions. The average time ambulated during a session
increased from 14.5 ( 6.1) minutes at the start of the
training protocol to 22.7 ( 18.2) minutes at the end.
Gait speed increased from 0.43 to 0.58 m/s. BWS was
only used in five participants, and decreased from 8.5
percent to 7.4 percent. The average impedance levels/
support levels decreased from 56.9 percent to 37.4
percent. Individual changes in the training parameters over
the course of the training period are shown in Figure 4.
Table 1 Descriptive information of participants
Motor level ASIA Post- injury
of injury* class time (months)
Primary outcome measures
The Friedman analysis showed a significant training
effect in all walking ability and strength scores (Table 2).
Subsequent, post-hoc pairwise comparisons between the
different evaluation periods showed that significant
improvements were primarily found between pre- and
post-training. The post-hoc test between pre- and
posttraining revealed that eight weeks of training with
LOPES resulted in significant improvements in walking
speed (10MWT), distance (6MTW), TUG score, and
LEMS (Table 2). No significant difference was found for
the WISCI II score between pre- and post-training.
Figure 5 shows the individual changes in the
primaryoutcome measures at the different evaluation periods.
All participants retained the functional level reached at
completion of their training. No significant differences
were found between follow-up and post-training in any
of the primary outcome measures (Table 2).
Relationship between initial impairment levels and
There were no significant correlations between the
initial performance on walking ability tests and the
absolute increase in test performance. Still, for walking speed
and distance, for example, assuming an equal increase in
absolute performance suggests that slower ambulators
experience the greatest relative improvement. Indeed,
the relative improvement in 10 MWT ( = -0.68, p =
0.04) and 6 MWT ( = -0.79, p = 0.01) showed a
significant negative correlation with the initial score on these
tests. The initial score on the TUG, WISI-II and LEMS
Figure 4 Training parameters as a function of the training duration. Training sessions are normalized to the total training time (0 percent
start of training, 100 percent completion of training). Training duration refers to the actual total training time per session (excluding setup time
and rest periods). Support levels are expressed as a percentage of the maximum stiffness that could be controlled by the LOPES (300 Nm/rad).
BWS was only required in five of the 10 participants. The bars indicate the mean training parameters, averaged across participants, at the start of
the training (0-10 percent) and at the end of the training period (90-100 percent). The error bars indicate the standard deviation.
Table 2 Statistical results primary outcome measures
Pre-mid p Mid-post p Pre-post p Post-follow
0.411 0.023 0.008* 0.797
Walking distance (m) 100
184.4 (184) 212.9 (216)
1The TUG was assessed in eight of 10 participants. Participants 7 and 9 were unable to stand up from the chair independently during the entire study.
did not prove to be an indicator of the relative increase
in the corresponding score.
Secondary outcome measures
Significant changes were observed in most
spatiotemporal parameters (Table 3). The maximum knee flexion
during swing, the knee ROM during the stance phase,
and the step width did not show significant changes. For
the step length and hip ROM, the mean changes in
the weak leg exceeded the changes observed in the
Participant 7 was excluded for analysis of kinematic
and spatiotemporal data because of the use of orthotic
devices, limiting accurate 3D kinematic data collection.
The aim of the present study was to examine the effects
of an eight-week training program on the walking ability
Figure 5 Primary outcomes. Measurements of walking ability were assessed pre-, mid-, and post-training and at follow-up. TUG could not be
measured for subject 7 and 9. The bars indicate the mean clinical measures, averaged across participants, at each period. The error bars indicate
the standard deviation.
Table 3 Statistical results secondary outcome measures
Walking speed (m/s)
Step symmetry index (%)
Rel. stance phase duration (%)
Maximum knee flexion (swing) (deg)
Knee ROM (initial and mid stance) (deg)
Increase in % of
and quality in iSCI individuals, using an
impedancecontrol strategy. In this study, we used a prototype of
the LOPES gait trainer. The training protocol was
tolerated well by all 10 participants and was performed
without difficulties for eight weeks. Participants improved
significantly on functional outcomes, muscle strength,
kinematics, and spatiotemporal measures after eight
weeks of LOPES training. Subsequent follow-up
evaluations revealed that participants retained their
traininginduced functional improvements. The main improvement
in kinematics occurred at the hip. The range of motion of
the hip joint increased, whereas the different measures for
the knee joint were unaffected by the training protocol.
Participants with the most limited initial walking function
showed the largest relative improvements.
Our main findings were a significant functional
improvement and an increased muscle strength. Comparing our
results with those of others is hampered because of
differences in robotic devices, protocols, patient
characteristics, outcome measures and the number of individuals.
Furthermore, most robotic gait-training devices are
rapidly evolving with increasing functionalities, making
robotic gait-training strategies hard to categorize.
We found significant changes in 10MWT, 6MWT, and
TUG performance that were relatively small compared
to other studies (Table 4). A likely explanation for this
difference is the included participants. Both Alcobendas
et al.  and Benito-Penalva et al.  included acute
iSCI individuals. Benito et al., who included a very wide
range of participants, showed that the greatest rate of
improvement was seen when training started early in
rehabilitation, defined as less than six months post-injury
. It is very likely that the improvements in these
participants are partly due to underlying spontaneous
recovery , rather than therapy effects. These findings
agree with other pilot studies, showing that individuals
with the smallest time since onset of injury show the
largest improvements in over-ground walking ability
[14,17,21]. Additionally, most studies that include
subacute iSCI individuals also allow their participants to
receive additional gait-related therapies [19,20,24], whereas
these therapies for chronic individuals have stopped,
effectively increasing the intensity of the training protocol.
From the studies including chronic iSCI individuals,
Nunen et al.  reported similar improvements in
walking speed. Wirz et al. , who also included only chronic
iSCI survivors, observed larger improvements in walking
speeds. Possible explanations for their higher gains are a
greater number of training sessions (26 vs. 20), longer
session durations (45 vs. 19 min), and lower initial walking
speeds (0.38 vs. 0.61 m/s). A lower initial walking speed
possibly allows more room for improvement. Although
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not discussed by Nunen et al., their results indicate that
participants with initial walking speeds around 0.4 m/s
show the largest improvements in walking speeds.
Therefore, it seems reasonable to assume that the greater effect
sizes found by Wirz et al., can be explained by the initial
functional level of their participants . Field-Fote et al.
, who trained chronic iSCI individuals with very low
initial walking speeds, did not find any significant effects
of robotic gait training on walking speed. Apparently, iSCI
individuals must have a certain level of initial walking
speed/function to benefit from robotic gait training.
Although the Friedman analysis showed a significant
training effect on the WISCI II score, there was no
significant improvement in WISCI II scores between
preand post-training. This is in line with other studies,
taking into account the types of participants. It is known
that the WISCI II is more sensitive in monitoring
recovery of walking capacity in iSCI subjects during the acute
stage of recovery rather than the chronic stage .
Similar to Wirz et al. , Nunen et al.  and others
[14,17,21], we did not find effect in the WISCI II score
in chronic individuals, whereas Alcobendas et al. 
and Benito-Penalva et al.  reported significant
increases in acute patients. Improvement in LEMS scores
are similar to the results found in other studies among
chronic iSCI individuals .
Follow-up measurements revealed that participants in
our study retained the level of functional improvement
measured at the end of the training period. Studies on
robotic gait training in iSCI individuals rarely include a
follow-up. FieldFote et al. did perform a follow-up
among 10 individuals whose improvements exceeded
0.05 m/s to assess their retention of relearned gait
abilities . They concluded that walking speeds declined
between the conclusion of the training and follow-up,
but remained above pre-training levels. However, their
follow-up group included only two chronic iSCI
participants who received robotic gait training, hampering a
It is important to note the timing of follow-up, which
was, on average 20.3 months in their study and only
eight weeks in ours. Although Field-Fote et al. did not
find a correlation between time since the conclusion of
training and the decline in walking speed, it seems likely
that participants lose some of their relearned walking
abilities over time, especially if they do not exploit their
relearned walking abilities in daily life [60,61].
Spatiotemporal and kinematic measures
To our knowledge, this is the first study reporting
significant changes in spatiotemporal and kinematic
measures associated with increased walking ability due to
robotic gait training. We found significant changes in
most spatiotemporal and kinematic measures after
robotic gait training. Previous studies showed small
increases in cadence, step and stride length, and
steplength symmetry  or sagittal plane excursions ,
but these were not significant. In this study, most of
these measures were significantly higher after training. It
is important to note that Nooijen et al.  and Hornby
et al.  used the LOKOMAT without the option to
decrease guidance forces, as this option was unavailable
on the device at the time of their study. Although both
studies encouraged participants to "walk with the
machine", they both state that constant guidance may
minimize the voluntary effort during training and
subsequently limit improvements in gait function.
In this study, improvements in spatiotemporal and
kinematic measures were greater in the weaker leg. The
larger improvements in step length and hip ROM in the
weaker leg resulted in significant increases in symmetry
between the two legs. This may indicate that gait
training restores walking function by restoration of function
using more normal movement patterns, rather than
Improvements in walking speed were caused by
improvements in step length as well as cadence. The
increased walking speed might explain some of the
observed changes in other spatiotemporal measures.
Here, the increase in walking speed probably explains
the decrease in stance phase duration  and the
decrease in step width . Whether the increased hip
flexion is enabled by an increased hip flexion strength
(mean increase in LEMS score of 3,4), or is simply a
consequence of the increased walking speed  cannot
Most current rehabilitation strategies focus on recovery
through intense practice of a specific task. In BWSTT
training, intensity depends on a combination of duration
(time or number of steps), speed, training frequency,
and the amount of BWS. In this study training intensity
was maximized by increasing training speed and
duration, and lowering the BWS levels when possible. With
the development of robotic gait trainers that can
potentially support the whole movement, the amount of
robotic support is also an important parameter that affects
training intensity. Often the precise setting of these
parameters is based on a therapists clinical judgment and
not on experimental evidence . For some
parameters, the effects on training outcome are known, but for
many they are not. Furthermore, the interaction among
the different parameters is not being investigated. For
example, reducing the amount of BWS and training at
higher treadmill speeds increases efferent input. This is
known to affect the neural control of stepping and is
suggested to promote functional recovery [11,65]. Still, the
interactive effect of BWS and walking speed in individuals
following SCI is unknown. Also the tradeoff between
training duration and frequency remains unknown.
For robotic gait training, the optimal amount of
support also remains unclear, although reducing the amount
of support according to the AAN principle seems most
suitable. Here, one might follow the concepts provided
in the Challenge-Point Framework . This
framework states that, for each skill level, there exists an
optimal level of task difficulty. When skill levels increase,
further learning will be best facilitated by increasing task
difficulty. In this study, task difficulty was increased by
lowering impedance levels.
To gain a better understanding of the combined effect
of training duration, speed, frequency, the amount of
BWS and robotic support levels, these intensity
parameters should be carefully reported in future studies .
For example, average walking speed and BWS levels in
the robotic device are rarely reported in robotic
gaittraining studies. Additionally, often only the total session
duration is reported, which does not represent the actual
training time (excluding setup time and rest periods).
With the increasing interest in robotic gait-training
devices that have (adaptive) impedance levels, it is also
advised to report impedance levels of the robot.
Among the different robotic gait-training studies, there
is a great diversity in training frequency, ranging
between three to five sessions per week, and duration,
ranging from 30-45 minutes per session (Table 4). These
parameters are often based on financial and practical
reasons . In this study, training frequency fell within
this range but the mean training duration (19 minutes)
was considerably lower. The relatively low training
duration is thought to be the result of the use of the
impedance control. By lowering the impedance levels when
possible, the active contribution required from
participants was relatively high. As a result, some participants,
especially the slowest walkers, could not train for the
same duration as seen in other position-controlled
gaittraining studies. Still, we showed that similar gains in
walking ability can be accomplished with less training
time. Actually, the biggest gains in walking ability were
observed in slow walkers with the lowest training
duration, suggesting that active participation is equally
important as training duration.
In this study, 90 percent of participants increased their
walking speed on the 10MWT, 100 percent increased
their distance on the 6MWT and 100 percent reduced
their time on the TUG. Although this resulted in an
average significant change of 0.6 m/s, 29 m and 3.4 s
(Figure 5), it should be noted that there was
considerable variation among subjects. Whether these
improvements represent a detectable (and clinically relevant)
change is debatable. The minimally detectable change
(MDC), which defines the minimal amount of change
required to distinguish (with 95 percent confidence) a
true performance change from a change due to
variability in performance or measurement error, is reported
to be around 0.13 m/s, 45.8 m, and 10.8 s . Criteria
for what clinicians define as clinically relevant or
meaningful can be even higher. Although MDC
criteria for detecting true improvements are conservative,
according to them, only one participant showed a true
improvement on the 10MWT, 6MWT and TUG tests.
Still, small gains in functional improvement that can
lead to reduced reliance on assistive devices could be of
great personal relevance to these individuals .
Limitations and future perspectives
The major limitation of this study is the lack of a control
group. The rationale for not including a control group
was that at this stage a pilot study was set up to assess
the possible effect of impedance-controlled robotic gait
training, how well it can be applied, the utility of the
outcome measures chosen and the variability in patient
responses . It was not intended to afford a basis on
which to claim that this kind of training can produce
greater functional improvements than those achieved
through manually assisted gait training or other forms of
conventional therapy. It only shows that chronic iSCI
individuals still have the capacity to improve their walking
function when provided with an intensive robotic
Patients and therapists will probably benefit the most
from robotic gait-training devices during acute stages of
recovery . Still, in this study, all participants were
chronic individuals. We included chronic individuals
(>12 months) because they typically have reached a
stable level of recovery . The average time since
onset was 46 months, suggesting that observed
improvements can be attributed to the intervention rather than
spontaneous recovery. That the participants reached a
stable level of recovery was also confirmed by a lack of
correlation between the time since onset of the injury
and the relative (or absolute) increases in walking speed.
Thus, to investigate the true potential of
impedancecontrolled gait training, acute and sub-acute individuals
should also be included in future studies. However, these
trials will require larger patient numbers to reach
significance due to the potential for underlying spontaneous
Apart from time since injury [19,20,58,69,70], previous
studies also showed that ASIA levels [6,19,69,71], LEMS
scores [25,71,72] (for recent injuries, for chronic results
vary [18,20,22]), sensation [71,73], and age [6,71] are
distinguishing factors for the degree of ambulatory capacity
after gait rehabilitation. Several studies purely focus on
increased walking speed, which is considered to be closely
related to functional ambulation . Patients who start
rehabilitation programs early after injury, have higher
ASIA/LEMS/sensory scores, or are younger generally
show greater improvements. Factors like ethology, levels
of injury, or sex seem to be less predictive [19,71]. Because
of the relatively small number of participants in this study,
we did not perform an analysis to relate clinical
improvement to patient characteristics. Future studies should
carefully document these characteristics, or stratify study
participants, to determine which iSCI sub-population
responds better to robotic gait training. These predictors
might be different for robotic gait training where age or
sensation, for instance, do not seem to have a clear effect
on functional outcomes [19,70].
This first explorative study using an impedance-controlled
robotic gait trainer shows significant improvements in
functional and qualitative walking parameters after an
eight-week training program in chronic iSCI individuals.
We were able to provide task-specific and intensive
training sessions, even for severely affected individuals,
with a minimal workload on the therapist. Compared to
position-controlled robotic gait-training strategies, the
training duration was relatively short, whereas
improvements in functional outcomes were similar. Additionally,
improvements observed at the end of the training period
persisted at the eight-week follow-up. The most impaired
ambulators, based on their initial walking speed, benefitted
most from the training protocol in relative improvements
in walking speed and walking distance.
The authors declare they have no competing interests.
BF was involved in the study design, conducting the measurements and
experiments, the analysis, and the writing of the manuscript. BK carried out
the experiments, collected and analyzed data, and wrote the manuscript. JB
and EA contributed to the design and revision of the manuscript. HK and JR
participated in the revision of the manuscript. All authors read and approved
the final manuscript.
This study was supported by a grant from the Dutch Ministry of Economic
affairs and Province of Overijssel, the Netherlands (grant: PID082004). We
would like to thank Martijn Postma, Bram van Gemeren, and Leontien
Zonnevijlle, from Het Roessingh, Centre for Rehabilitation, for their assistance
during the experiments and their experience in treadmill training.
of Technology, Delft, The Netherlands. 4Department of Rehabilitation,
Medisch Spectrum Twente, Enschede, The Netherlands.
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