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Abstract—The idea of using robots to assist a therapist with a
rehabilitation exercise has led to the development of several
rehabilitation robotic devices. The scope of robotic devices in
rehabilitation is rapidly advancing based on the developments
in robotics, haptic interfaces and virtual reality. GENTLE/S
was a rehabilitation system that utilized haptic and virtual
reality technologies to deliver challenging and meaningful
therapies to upper limb impaired stroke subjects. The current
research is working towards designing the GENTLE/A system
with a better adaptive human-robot interface. This paper
presents the results from an exploratory study conducted with
twenty healthy subjects. The aim of the study was to identify
the contribution by subject/robot during different human-robot
interaction modes. Our results show that it is possible to
identify the leading or lagging role of a subject during a
human-robot interaction session where a reference trajectory is
used to drive the arm along a path. The final goal is to use these
observations to probe various ways in which the adaptability of
the GENTLE/A system can be improved as a therapeutic device
and an assessment tool.
Keywords- stroke rehabilitation, robotic therapy, adaptable
system, lead-lag interactive behaviour
I. INTRODUCTION
Stroke [1] is a major cause of chronic impaired arm
function and may affect many activities of daily living.
Restoration of motor function has been a key objective of
stroke rehabilitation. The use of robotic devices for
rehabilitation purposes is a relatively new field within the
area of robotics in health care and emerged from the concept
of using robots to assist people with disabilities. The focus
of our research is robotic assistance in stroke rehabilitation.
Research on robotic assistance in stroke rehabilitation is
rapidly advancing based on the recent developments in
robotics, haptic interfaces and virtual reality.
The basis of all stroke rehabilitation is the assumption that
patients can re-gain lost motor skills by re-learning and
practice. Studies suggest that retention of motor learning is
best accomplished with variable training schedules and, for
optimal results, rehabilitation techniques need to be geared
toward patients’ specific motor deficits [2]. Robot-assisted
therapy facilitates individualized training exercises for
patients where the patients can train independently and also
has the scope of offering ‘Tele-rehabilitation’.
The work being carried out in this research is based on the
GENTLE/S [3]-[5] rehabilitation system. GENTLE/S
Manuscript received January 31, 2012.
Radhika Chemuturi, Farshid Amirabdollahian and Kerstin Dautenhahn
are with Adaptive Systems Research Group, Science and Technology
Research Institute, University of Hertfordshire, Hatfield, AL10 9AB, UK Contact author: Radhika Chemuturi (e-mail: [email protected])
utilised haptic and virtual reality technologies to deliver
challenging and meaningful therapies to upper limb impaired
stroke subjects. The clinical trial results with the GENTLE/S
[6], [7] system and other systematic reviews [8]-[10] in the
area of rehabilitation robotics bring out the need for robotic
therapy to be highly 'adaptable' according to the specific
needs and performance of the patient. Research [11], [12]
also highlights the opportunity for using robotic technology
to quantitatively 'assess' the underlying recovery process.
The current research is therefore working towards designing
the GENTLE/A rehabilitation system that can provide
individualised adaptability and assessment during stroke
rehabilitation.
Our approach to make the system adaptable is based on
studying the contribution by the subject or the robot during
different specifically designed human-robot interaction
modes of the system. We use the term ‘leading’ for cases
where the position of the subject’s arm is further on the
desired path when compared to the reference position. The
term ‘lagging’ refers to cases where the position of the
subject’s arm follows the positions given by the reference
trajectory. Our experiment aimed to investigate whether it is
possible to reliably identify the role of the subject during an
interaction session where this role is a known condition.
The HapticMaster (HM) robot (Moog BV [14]), the main
component of the GENTLE/A system, was programmed to
implement the minimum jerk trajectory (MJT) model. This
was one of the models used to create smooth and human-like
trajectories [13], [16]. Similar to its earlier incarnations, the
GENTLE/A system uses this model to create a reference
trajectory between two points in the work space. Different
modes were designed in the system to allow the robot or the
subject to take charge of the movement. Data was recorded
in both the situations, i.e. when the robot led the movement
while the subject remained passive as well as when the
subject took the lead by overtaking the robot. Although the
MJT was used as a reference trajectory for pulling or
tracking the subject's hand, any other human arm trajectory
model can be used in a similar way as presented in the
following sections.
In a preliminary experiment [15], we investigated whether
just the error from the reference position could be used to
detect the leading/lagging role of the subject. This was
performed on two settings: single-axis as well as planar
point-to-point reaching movements. The results obtained
showed that it was possible to identify whether the robot, or
the subject were leading the interaction modelled by the
MJT, but the sign of the error depended on moving away or
Impact of lead-lag contributions of subject on adaptability of the
GENTLE/A system: an exploratory study
Radhika Chemuturi, Farshid Amirabdollahian, Member, IEEE,
and Kerstin Dautenhahn, Member, IEEE
The Fourth IEEE RAS/EMBS International Conferenceon Biomedical Robotics and BiomechatronicsRoma, Italy. June 24-27, 2012
978-1-4577-1198-5/12/$26.00 ©2012 IEEE 1404
towards the reference coordinates. Next we investigated
whether a 3-dimensional movement (movement with
components on each of the three axes X, Y and Z) would
result in similar effects when a person purposely lags or
leads in performing a trajectory segment. This paper presents
the results obtained from a second study formulated to
answer this question.
II. METHODS
A. Experimental Set-up
The GENTLE/A experimental set-up used the hardware
and software components of the GENTLE/S rehabilitation
system [5] with some specific modifications. The
HapticMaster robot with its gimbal attachment formed the
vital component of the GENTLE/A system. The 24” wide
LCD screen for display stands on a rotary arm, which can be
turned from one side to the other side of the exercise table
and thus can be adjusted based on the dominant side of the
subject. Due to the participation of healthy volunteers, the
overhead frame support mechanism, elbow orthosis and
magnetic wrist attachment of the GENTLE/S system were
excluded.
Figure 1. The GENTLE/A experimental set-up
The current setting uses Window 7 (64 bit) and programmed
using Visual Studio 2009, with the C++ programming
language. Data during interaction was captured using
comma de-limited files. The graphical user interface was
programmed under OpenGL. The HapticMaster robot was
programmed to operate in two modes:
Passive Mode: The subject remained passive holding the
gimbal while the robot executed the movement from
source to target in its workspace. During this mode, the
subject’s arm was moved along the predefined reference
trajectory using a 2nd
order spring-damper system and a set
duration.
Active-Assisted Mode: The subject had to initiate the
activity, and the robot assisted the subject for the rest of
the activity. Thus in this mode, the subject and the robot
worked in collaboration to reach the target. The active-
assisted mode also utilised the set duration to propel the
arm along the predefined trajectory.
The new design of the Virtual Reality (VR) environment
allowed the experimenter to insert the target points that were
displayed as numbered spheres in green and rendered a pipe
(presented as a cylinder graphically) connecting these points
(Fig. 2). This connector pipe acted as a guide to the desired
straight-line path between the source and the target points.
The end-effector position was displayed as a small yellow
ball moving in the workspace of the robot. The subject could
initiate and execute movement between targets in the active-
assisted mode while in the passive mode the robot cycled by
itself through these defined points. The movement between a
source and a target point was termed as a ‘segment’ and a
duration of 4 seconds was set to execute each segment. All
numbered points were visited sequentially, ending with a
segment connecting the last pair (points 5 and 6 in Figure 2).
When the subject was due to start the movement from the
source of a segment, the target point glowed in pink and
once the subject reached the target point, it turned green
becoming the source for the next segment and the target for
the subsequent segment glowed in pink and so on. The
progress along the desired MJT path for the segment was
displayed as a grey cylinder and the actual path achieved by
the subject was displayed as a red cylinder. The angular
deviation from the desired path was calculated as θ and
when θ>10˚, a green arrow was displayed informing the
subject of the direction in which movement was deviating.
The break between one segment to the next was
characterized by a delay of 3 seconds when the target point
for the next segment gradually grew in size and popped (like
a balloon), serving as both an audio and a visual cue for the
subject to start the movement towards the target.
Figure 2. VR environment showing the execution of Segment-2,
target point in pink, progressing grey and red cylinders and
deviating green arrow. Points 1, 3 and 5 were located closer to the
subject’s body and points 2 and 4 were located farther away from
the subject’s body.
1405
B. Experimental Protocol
The protocol for the experiment was approved by the
ethics committee at University of Hertfordshire. All
participants provided informed consent prior to the
experiment. Twenty healthy volunteers took part in the
experiment aged between 23 and 60 (mean 36.9±11.3
standard deviation) and including 15 male and 5 female
subjects. The experiment was conducted in two phases:
Training Phase: The subject was instructed to hold the
ring (gimbal) attached to the end of the robotic arm and
move the ring along the guiding pipe joining the target
points shown on the screen. The subject was encouraged to
understand the operation of the system and was advised to
try the passive and the active-assisted modes at least once to
become familiar with the experimental procedure.
Actual Performance Phase: Once the subject was familiar
and comfortable with the activity, the actual performance
phase was executed. In order to create a situation where the
subject purposely led the activity, the active-assisted mode
was executed twice. The first run was termed Active
Assisted-1 (AA1) where the subject was instructed to initiate
the movement at the source point and then allow the robot to
take charge of the movement until the target point was
reached. The second run was termed Active Assisted-2
(AA2) and the subject was asked to execute the entire
movement from source to target points while trying to
overtake the robot using the virtual representation of the
grey and red cylinders. Thus the actual performance phase
involved executing the passive and two runs of the active-
assisted modes in that order.
A fixed set of points was used during the experiments
with all participants. The movement started at point 1,
progressed sequentially, and ended at point 6 during each
mode. The path with source at point 1 and target at point 2
was coded as segment-1 and so on. Data was recorded
during the interaction sessions, including Cartesian
positions, velocities and forces exerted at the robotic end-
effector.
The fixed set of points used was obtained from the
GENTLE/S database. The idea of using the same set of
points during this experiment was to facilitate the
comparison of results with stroke subjects with that of
healthy subjects in future.
III. RESULTS AND ANALYSIS
The data recorded from the ‘actual performance phase’
was used for data analysis purposes. Fig. 3 shows the
organisation of the raw data for analysis.
A. Parameters
Tau ( :
Parameter, τ, was calculated using the sample time (t), time
at the start and time at the end of each
trajectory segment:
This was a parameter of convenience used to map the
exercise time to a parameter between -1 and 1, which
allowed for considering all trajectories using the same
temporal window and also provided a chance to look at the
trajectory symmetry.
Figure 3. Organisation of raw data for analysis
parameter:
Cartesian positions, velocities and forces were sampled at a
time interval of 50 milliseconds. The line joining the source
point to the current position achieved by the subject was
termed ‘actual vector’ and the line joining the source point
to the reference MJT position that the robot was
programmed to follow was termed ‘MJT vector’.
Figure 4. Representation of the ‘Guiding’ and the ‘Actual’ vectors
and derivation of Effort and Error components
(
| || |)
Fig. 4 and the equations below the figure show the
derivation of Effort and Error components of the actual
vector. , derived by projecting the actual vector
12
tend tstart
t tend where
1 1
1406
onto the guiding vector (line joining the source and target
points) and , derived as the extent by which the
actual vector was deviating from the guiding vector.
and were similarly calculated using
the MJT vector and the guiding vector. Analysis of Error
calculations was left for future work. In order to compare the
progress achieved by the robot and the subject a new
parameter was calculated as follows:
(1)
B. Tau ( versus (Segment-specific analysis)
The passive mode was considered for testing the lagging
performance of the subject. The subjects were instructed to
remain passive and follow the robot which executed the
entire movement from the source point to the target point of
various segments. As a consequence of using second order
virtual spring-damper to propel the arm along the path (as
shown in [13]), the actual trajectory achieved by the subject
lags the MJT trajectory when the subject purposely remains
passive and hence according to (1), the parameter
during the passive mode always remains positive. Similarly
the subject was asked to overtake the robot during the AA2
mode and hence this mode was considered for testing the
leading performance of the subject. The actual trajectory
leads the MJT in the AA2 mode and according to (1), the
parameter remains negative. Therefore the
hypothesis for our data analysis was whether it is possible to
use the sign for the in order to identify subject’s
leading or lagging role.
Subject’s role Testing condition
Lagging
Leading
Our first step of data analysis was to check the spread of
the parameter during each segment performed
under different modes. Segment wise graphs of tau vs
were plotted with each plot showing a different
patterned- coloured line for different modes (Passive, AA1
and AA2). Fig. 5 shows the plots for SubID=15 during
various segments. Similar results were observed for other
subjects.
Fig. 5(a) shows that was satisfied for
all the five segments, while was satisfied
during segments 3 and 5 and the major part of segment-1,
but during segments 2 and 4, showed a varying
pattern as tau progressed from -1 to 1. To explore this
further, we computed the summation of
samples for each segment that could indicate if
remained negative for major part of the segment. Therefore
the new testing condition for leading performance of the
subject was formed as below,
Leading role ∑
Where n is the number of samples recorded during a
trajectory segment.
Figure 5(a). Segment specific plots of Tau (τ) vs ∆Effort during
different modes for SubID=15
Figure 5(b). Segment specific plots of Tau (τ) vs Velocity during
different modes for SubID=15
The number of subjects (out of 20 participating subjects)
satisfying the leading performance condition during various
segments of the AA2 mode can be summarized as: Segment-
1 (13/20), Segement-2 (8/20), Segment-3 (13/20), Segment-
4 (5/20) and Segment-5 (10/20). These identified a visible
difference between segments 1, 3 and 5 versus segments 2
and 4. To examine if this difference was dependent on the
length of the segment, we conducted a correlation test
between magnitude of segments (summarized in a table at
-1 -0.5 0 0.5 1-0.1
-0.05
0
0.05
0.1Segment-1
E
ffort
(m
)
-1 -0.5 0 0.5 1-0.1
-0.05
0
0.05
0.1Segment-2
E
ffort
(m
)
-1 -0.5 0 0.5 1-0.1
-0.05
0
0.05
0.1Segment-3
E
ffort
(m
)
-1 -0.5 0 0.5 1-0.1
-0.05
0
0.05
0.1Segment-4
E
ffort
(m
)
-1 -0.5 0 0.5 1-0.1
-0.05
0
0.05
0.1Segment-5
E
ffort
(m
)
PassiveAA1AA2
Segment Magnitude
Segment-1 0.236205
Segment-2 0.252054
Segment-3 0.253221
Segment-4 0.238556
Segment-5 0.315249
-1 -0.5 0 0.5 10
0.05
0.1
0.15
0.2Segment- 1
Velo
city (
m/s
ec)
-1 -0.5 0 0.5 10
0.05
0.1
0.15
0.2Segment- 2
Velo
city (
m/s
ec)
-1 -0.5 0 0.5 10
0.05
0.1
0.15
0.2Segment- 3
Velo
city (
m/s
ec)
-1 -0.5 0 0.5 10
0.05
0.1
0.15
0.2Segment- 4
Velo
city (
m/s
ec)
-1 -0.5 0 0.5 10
0.05
0.1
0.15
0.2Segment- 5
Velo
city (
m/s
ec)
Passive
AA1
AA2
1407
the bottom right of Fig.5(a)) and the number of subjects that
managed to lead the performance during those segments, but
no significant correlations were found. The velocity plots in
Fig.5(b) show a smooth pattern during the Passive and the
AA1 modes compared to a visibly multi-peak velocity
during the AA2 mode. This indicated that subjects actively
contributed to the AA2 mode, yet did not manage to lead the
robot in achieving the task goals.
It was notable from Fig.1 and Fig.2 that segments 1, 3 and
5 were reaching segments where the movement started at a
source point located closer to the subject’s body and ended
at the target point away from the body. Segments 2 and 4
were returning segments where the movement started at a
source farther away from the subject’s body towards a target
closer to the body. Our observation here indicated that in
cases where the robot moved towards the subject’s body, the
subject played a leading role (as reflected by ) for a
smaller proportion of the time when compared to cases
where the robot moved away from the subject’s body.
C. Tau (τ) versus ∆Effort (Quadrant-specific analysis)
As discussed earlier, a large number of subjects could not
play a leading role during segments 2 and 4 of the AA2
mode when they were instructed to do so. Also, plots of
segments 1, 2 and 4 in Fig. 5 showed that the sign of did not remain constant as tau progressed from -1 to 1. A
possible cause was linked to the set duration of the
movement and whether there has been enough time allocated
to perform a segment comfortably. The segments were
therefore fragmented in to four equal quadrants (based on
tau) to carry out a closer observation of lead/lag role of the
subject during various segments of the AA2 mode. The four
quadrants were formed as follows: Quadrant-1 (Q1, ), Quadrant-2 (Q2, ), Quadrant-3
(Q3, ) and Quadrant-4 (Q4, ). The
decision to divide the segments into four equal quadrants
was solely based on the literature and the expectation of a
bell-shaped velocity profile during these segments. Our
anticipation was that if duration is an influencing parameter,
there will be a gradual reduction of subject numbers
managing to lead the robot as one progressed from Q1-Q4.
The condition for the leading role was then applied to all
four quadrants of each segment and the number of qualifying
subjects that led the interaction during that quadrant was
counted.
The results were presented by the bar chart (Fig. 6).
Reaching segments (1, 3 and 5) show similar patterns with
≥50% subjects satisfying the leading performance condition
during Q1, Q2 and Q4 and <50% during Q3. Similarity also
existed in returning segments (2 and 4) with ≥50% subjects
satisfying the leading performance condition during Q1 and
Q2 and <50% during Q3 and Q4. This showed that during
Q1 and Q2 the majority of the subjects could lead the
performance during all segments in the AA2 mode when
they were asked to do so, but the lead role was not consistent
during Q3 and Q4. It further highlighted that there is
potentially a link between the type of reaching task, its set
duration and the subject’s ability to lead during an active
assisted interaction.
Figure 6. Quadrant specific counts of subjects satisfying leading
condition in the AA2 mode during various segments. Segments
were re-ordered to allow for better comparison.
IV. DISCUSSION
During this exploratory study, the duration to execute
each segment was set to 4 seconds. Data analysis results
showed that the subjects did not always lead the robot when
they were asked to do so in the AA2 mode. When the
segment was further fragmented into quadrants, the results
showed the leading role in Q1 and Q2 and an inconsistent
role in Q3 and Q4 in majority of the cases. A likely
explanation for this behaviour is that subjects were restricted
from performing at their normal and natural pace by the pre-
set 4 seconds duration while segment lengths and arm
movement patterns varied. It could also be linked to the type
of movement (reaching away or returning towards the
subject’s body) required for executing each segment. Also,
Fig. 2 shows that reaching segments (1, 3 and 5) had a more
pronounced gravity component towards Q3 and Q4 while
returning segments (2 and 4) present the opposite (as the
arm-shoulder support gravity compensation unit was not
used during this experiment). In our next experiment we
plan to include trajectories towards and away from the body
with horizontal and vertical components, allowing us to test
whether gravity is also a contributing factor. This would also
give us an opportunity to study the influence of movement
direction on the performance of the subject.
Our findings presented in this paper further highlight the
need for adaptive interaction as it has magnified that
different movement patterns require different settings. An
interesting question here is whether a customised duration is
sufficient to pose a therapeutic challenge?
One logical approach was that the duration for performing
segments should be based on subjects’ pace in performing a
segment. In our on-going experiment we included a new
customisation phase to calculate the average reaching time
Seg-1 Seg-3 Seg-5 Seg-2 Seg-4
Q1 20 16 16 18 16
Q2 12 12 10 12 12
Q3 8 9 5 9 5
Q4 12 13 13 3 4
0
5
10
15
20
25
Co
un
t (o
ut
of
20
)
Leading role in AA2 (Quadrant-wise)
1408
for the participating subject and execute the rest of the
experiment based on the average reaching time for that
subject and type of movement, i.e. reaching away or
returning towards the body.
In our ongoing experiment modifications were made to
make the GENTLE/A system adaptive to a subject’s ability
to perform a segment based on observed duration values
during an ‘adaptive phase’. As ∑ is used for this
purpose, program code has been enhanced to compute the
∑ parameter for each segment as the experiment
progresses. The experiment begins with a constant value for
the duration set to execute each segment. During the AA2
mode once the execution of a segment finishes,
the duration to execute that segment in the next
cycles is altered using the following algorithm:
∑
( )
( )
∑
[ ]
The two main aims of the ongoing experiment are to test
the adaptability of the GENTLE/A system to segment
duration and to repeat the investigation on lead-lag role of
the subject interacting with the GENTLE/A system tuned to
operate with customized segment durations for that subject
on a more versatile set of points. The data recorded until this
date is showing some promising results where the duration
to execute each segment adjusts through the initial iterations
and settles down to an optimum value for each segment
displaying the characteristic of an adaptable system. This
data also shows that adaptive times vary between the
reaching away and returning towards the body durations,
further confirming the findings shown by Figure 6. We plan
to complete this experiment and present its findings in future
publications.
V. CONCLUSION
We described in this paper an approach to identify the
leading or lagging role of the subject interacting with the
system. We aim to use this information to improve the
adaptability of the GENTLE/A system by customizing the
exercise parameters, including segment duration or
increasing the assistance provided to the subject in case of
lagging performance and reducing the assistance in case of
leading performance. The results from this exploratory study
showed that vector projections of the position data achieved
by the subject when compared to the reference positions
driven using MJT could inform the lead-lag role of the
subject. The varying performance of the subjects during
different quadrants of reaching and returning trajectory
segments opened a new line of investigation into reaching
durations recorded. Studying these durations hinted further
on their links to the lagging or leading role of the subject.
Adapting the GENTLE/A system according to the
performance of the subject forms a vital part of our ongoing
experiment. Further research into exploring the potential of
average reaching times as an ‘assessment’ parameter is also
under progress.
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