-
Autonomous Robots 15, 3551, 2003c 2003 Kluwer Academic
Publishers. Manufactured in The Netherlands.
Upper Limb Robot Mediated Stroke TherapyGENTLE/s Approach
RUI LOUREIRO AND FARSHID AMIRABDOLLAHIANtHRILthe Human Robot
Interface Laboratory, Department of Cybernetics, School of Systems
Engineering,
The University of Reading, Whiteknights, PO Box 225, Reading,
RG6 6AY, [email protected]
MICHAEL TOPPINGStaffordshire University, School of Design,
College Road, Stoke on Trent, Staffordshire, ST4 2XN, UK
BART DRIESSENTNO-TPD Institute of Applied Physics, Control
Engineering, Delft, The Netherlands
WILLIAM HARWINtHRILthe Human Robot Interface Laboratory,
Department of Cybernetics, School of Systems Engineering,
The University of Reading, Whiteknights, PO Box 225, Reading,
RG6 6AY, UK
Abstract. Stroke is a leading cause of disability in particular
affecting older people. Although the causes of strokeare well known
and it is possible to reduce these risks, there is still a need to
improve rehabilitation techniques.Early studies in the literature
suggest that early intensive therapies can enhance a patients
recovery. According tophysiotherapy literature, attention and
motivation are key factors for motor relearning following stroke.
Machinemediated therapy offers the potential to improve the outcome
of stroke patients engaged on rehabilitation forupper limb motor
impairment. Haptic interfaces are a particular group of robots that
are attractive due to theirability to safely interact with humans.
They can enhance traditional therapy tools, provide therapy on
demandand can present accurate objective measurements of a patients
progression. Our recent studies suggest the useof tele-presence and
VR-based systems can potentially motivate patients to exercise for
longer periods of time.The creation of human-like trajectories is
essential for retraining upper limb movements of people that have
lostmanipulation functions following stroke. By coupling models for
human arm movement with haptic interfaces andVR technology it is
possible to create a new class of robot mediated neuro
rehabilitation tools. This paper providesan overview on different
approaches to robot mediated therapy and describes a system based
on haptics and virtualreality visualisation techniques, where
particular emphasis is given to different control strategies for
interactionderived from minimum jerk theory and the aid of virtual
and mixed reality based exercises.
Keywords: hemiplegia, motor relearning, robot mediated therapy,
virtual environments, assistive technology
1. Introduction
Cerebral Vascular Accidents (CVAs), often-calledstrokes occur
when the blood supply to the brainis interrupted either by a blood
clot or internal bleed-ing. Due to this interruption some parts of
the brain do
not receive fresh oxygenated blood and neurons in thatregion
die. Depending on the region of neuronal death,control, sensory or
cognitive functions may be lost orimpaired. The most common of all
strokes account-ing for 7080% are cerebral thrombosis and
cerebralembolism (Westcott, 2000). They are a leading cause
-
36 Loureiro et al.
of disability with estimates of the annual incidence ofstroke
ranging from 180 per 100,000 in the USA, 1251per 100,000 in Europe
(Stewart et al., 1999) to 200 per100,000 in England and 280 per
100,000 in Scotland(Isard and Forbes, 1992). England and Wales
statis-tics show that every year around 100,000 people havetheir
first stroke from which 10,000 are under retire-ment age.2 ,3
Approximately one-third of the peoplesurviving from a stroke are
left with severe disabili-ties (Abrams and Berkow, 1997). One
common con-sequence is movement and co-ordination of the UpperLimb
(UL). Almost 85% of people with stroke show aninitial deficiency in
the UL (Parker et al., 1986) fromwhom only 50% recover function of
their affected UL(Parker et al., 1986; Broeks et al., 1999).
Stroke is the third most common cause of deathin England and
Wales, after heart disease and cancer(Petersen et al., 2000).
Mortality statistics for the UKshows that in 1999 a total of 64,971
deaths4 were dueto stroke (ONS, 2000). The cost of stroke to the
Na-tional Health Service is estimated to be over 2.3 billion( 3.45
billion) per year. The total cost of stroke care isexpected to rise
in real terms by around 30 per cent bythe year 2023 (Westcott,
2000). The UK governmentis determined to reduce the death rate from
stroke andrelated diseases in people less than 75 years by at
leasttwo fifths by 2010saving up to 200,000 lives per yearin total
(Petersen et al., 2000).
Following the stroke the effects are almost immedi-ate and vary
according to the level of damage done tothe brain. Some of the most
important symptoms in-clude: unexpected insensibility, weakness or
paralysison one side of the body, or in a higher level both
sides.Signs of this might be a weak arm, leg or eyelid, ora
dribbling mouth, difficulty finding words or under-standing speech,
sudden blurring, disturbance or lossof vision, especially in one
eye, dizziness, confusion,unsteadiness and/or severe headache
(Westcott, 2000).
During the initial critical phase the aim is to stabilisethe
condition, control blood pressure and prevent com-plications. Once
the patient is stable, he/she is engagedin an individual
rehabilitation programme designed tohelp regain independence and
relearn the lost skills.
Several authors (Parker et al., 1986; Feys et al., 1998)have
shown that the reason that Upper Limb (UL) re-covery of function is
more difficult to achieve than withLower Limbs (LL) is due to the
UL complexity. UL areused for gesture and non-purposeful movements
suchas reflex perception and balance but their main func-tions are
task oriented. These tasks involve UL func-
tions such as locating a target, reaching (transport ofarm and
hand), grasping an object (grip formation andrelease) and postural
control. The recovery of such taskoriented function is of extreme
importance in every daymanipulative tasks (Ashburn, 1993).
1.1. Physiotherapy Practise
Physiotherapy is inconsistent, varying from one thera-pist to
another and from hospital to hospital. Many dif-ferent approaches
for treatment techniques have beenproposed (e.g. Bobath, 1978;
Cotton and Kinsman,1983; Knott and Voss, 1968; Rood, 1954). The
Carr &Shepherd method (Carr and Shepherd, 1987) focuseson motor
relearning where relearned movements arestructured to be task
specific. That is, motor controlis both anticipatory and ongoing
and postural controllimb activities are interrelated. The practice
of specificmotor skills leads to the ability to perform the
task.The task should be practised in the appropriate environ-ment
where sensory input modulates the performanceof motor tasks. The
model is based on normal motorlearning, the elimination of abnormal
movement, feed-back, practice and the relationship between posture
andmovement.
Physiotherapy practice is partially based on theo-ries but is
also heavily reliant on the therapists trainingand past experience.
Based on this many authors de-scribe that there is insufficient
evidence to prove thatone treatment is more effective than any
other (Sackleyand Lincoln, 1996). Recent reviews of Upper
Limbpost-stroke physiotherapy concluded that the best ther-apy has
not yet been found (Ernst, 1990; Coote andStokes, 2001).
Nonetheless, several studies (Kwakkelet al., 1997; Sunderland et
al., 1992; Lincoln and Parry,1999) point out that UL motor
re-learning and recov-ery levels tend to improve with intensive
physiotherapydelivery. The need for conclusive evidence
supportingone method over the other and the need to stimulate
thestroke patient (Sackley and Lincoln, 1996) clearly sug-gests
that traditional methods lack high motivationalcontent, as well as
objective standardised analyticalmethods for evaluating a patients
performance and as-sessment of therapy effectiveness.
1.2. Robot Mediated Therapy Approaches
Several authors have already proposed the use ofrobotics for the
delivery of Upper Limb post-stroke
-
Upper Limb Robot Mediated Stroke Therapy 37
physiotherapy. The first far-reaching study on the ac-ceptance
of robot technology in occupational therapyfor both patients and
therapists was done by Dijkerset al. (1991) using a simple therapy
robot. Dijkersstudy reported a wide acceptance from both
groups,together with a large number of valuable suggestionsfor
improvements. Advantages of Dijkers therapy in-cluded the
availability of the robot to successively re-peat movements, as
well as the ability to record move-ments. However, there was no
measure of movementquality, and patient cooperation was not
monitored.
At the VA Palo Alto Rehabilitation R&D center andStanford
University, Johnson et al. (1999) have devel-oped the SEAT:
simulation environment for arm ther-apy to test the principle of
the mirrored-image bythe provision of a bimanual, patient
controlled thera-peutic exercise based on a driving simulator. The
devicecomprises a customised design of a car steering wheelequipped
with sensors to measure the forces appliedby a patients limbs, and
an electrical motor to providepre-programmed assistance and
resistance torques tothe wheel. Visual cues were given to the
patient viaa commercially available low cost PC-based
drivingsimulator that provided graphical road scenes. The
in-terface allowed the participation of the patient in thetask and
the involvement of the paretic limbs in theexercise. The SEAT
system implemented 3 differenttherapy modes: passive, active and
normal mode. Inthe passive mode, the servomechanism compensatedfor
the weight of the paretic limb which was guided bythe non-paretic
limb. Active mode is used when the sub-jects demonstrated some
level of voluntary control ofthe paretic limb. In this mode, the
servomechanism onthe steering wheel encouraged the participation of
theparetic limb when performing the steering task withthe paretic
limb while relaxing the non-paretic limb.The normal mode was
designed to assess the force dis-tribution and analyse the
participation of the pareticlimb by the participation of both limbs
in the steeringtask and assess the limbs co-ordination. Recent
resultssuggest that SEAT system increased the interest of sub-jects
in using the impaired limb in the steering task andthe use of the
automated constraint discouraged com-pensatory use of the stronger
limb (Johnson et al., 2001,2002).
Based on the same mirror image concept, Lum et al.(1999) at the
VA Palo Alto Rehabilitation R&D centerproduced the MIME:
Mirror-image motion enabler.A Puma 260 robot was used for the
initial prototype,which was attached via a force-torque sensor to
the
arm splint. In the current prototype a Puma-560 robotreplaces
the original Puma-260 and its paretic limb mo-bile arm support
while a 6DOF digitiser replaces thenon-paretic arm support. The
MIME system can workin pre-programmed position and orientation
trajecto-ries or in a slave configuration where it mirrors
themotions of the non-paretic limb. A computer controlsmovement of
the robot, with specific pre-programmedtasks tailored to the
subjects level of recovery and ther-apeutic goals. Clinical trials
with 27 chronic strokesubjects (>6 months post stroke) based on
the Fugl-Meyer exam, have shown no negative effects. Resultsalso
suggested that robot-aided therapies are safe andeffective for
neuro development treatment (Shor et al.,2001).
Rao et al. (1999) have used the Puma-260 with apassive and
active mode. In the passive mode the robotguided the patients arm
through a specified path and inthe latter the patient lead the
robot along a predefinedpath based on graphical interface
resembling a tunnel.If the patient collided with the wall of the
tunnel, therobot would take control and bring the patients armback
to the normal path. One of the advantages of thisimplementation is
that the tunnel constraints could bechanged according to each
individuals need over therecovery time slot. The results from the
test bed imple-mentation of the Puma-260 suggested that the
subjectslearned to minimise deviations from the centre line
inrepeated trials and the torque applied to the end-effectorbecame
smoother over exercise time.
Work done at MIT by Krebs et al. (1999) on thedevelopment of a
manipulandum that allowed the sub-jects to exercise against
therapist nominated stiffnessand damping parameters uses a
different approach fromthe systems described so far. Their project
defines anew class of interactive, user-friendly clinical devicefor
evaluating and delivering therapies via the use ofvideo games. They
have designed and used the com-mercialised MIT-MANUS (Hogan et al.,
1995) a 3DOF(2DOF active 1DOF passive) planar manipulator toperform
a series of clinical trials. Reports on initialresults with 20
subjects with stroke, where 10 usedthe MIT-MANUS in addition to
normal therapy foran additional 45 hours per week suggests that
theyhad improved substantially compared to the ones un-dertaking
normal therapy. Recent results were reported(Krebs et al., 1999)
from a total of 76 subjects. It wasshown that the manipulation of
the impaired limb influ-ences recovery, the improved outcome was
sustainedafter 3 years, the neuro-recovery process continued
far
-
38 Loureiro et al.
beyond the commonly accepted 3 months post-strokeinterval, and
the neuro-recovery was dependent on thelesion location. The
MIT-MANUS mechanism how-ever limits the range of possible
therapies, has limiteddata collection facilities and does not allow
bimanualtherapies as the SEAT and MIME systems.
Reinkensmeyer et al. (2001) introduce a differentapproach with
their web-based force feedback telere-habilitator called Java
Therapy. Java Therapy is aninexpensive robotic telerehabilitation
system for armand hand therapy. It consists of a web site with
alibrary of evaluation and therapy activities that canbe performed
with a commercial force feedback joy-stick, which can physically
assist or resist movementas the user performs therapy. It also
allows for somelevel of quantitative feedback of movement
perfor-mance, allowing users and their caregivers to
assessrehabilitation progress via the web. The MicrosoftSidewinder
Force Feedback Joystick was used to movethe patients arm while the
subject interacted withsimple 2D games and perform speed,
co-ordinationand strength tests. Initial comments on this new
ther-apy indicate that while the subject gains concentra-tion,
there is no significant improvement in motorcontrol of the upper
arm mainly due to the verysmall workspace and force feedback
provided by thejoystick.
The systems and research reviewed above focused onthe
rehabilitation of the upper arm. Work in the con-text of haptic
feedback in the rehabilitation of the handwas done at Dartmouth
College (Brown et al., 1993)on an exoskeleton used as a prosthetic
device by sub-jects who had lost muscular control of their hand.
Thedevice consists of an instrumented aluminium struc-ture attached
to the back of the hand via a Lycra glove.Position is measured by
potentiometers and five cablesrouted to the palmar side are used to
close the index andthumb fingers in a pinch grasp. DC motors
located onthe forearm caused finger flexion, and restoring
springsin the exoskeleton pull the fingers to a neutral
positiononce the actuators are de-energised. Initial tests
showedgood range of motion, good repeatability but calibra-tion was
needed for every new patient, and cable staticfriction and
exoskeleton weight were judged to be toolarge.
A different approach was used by Popescu et al.(2000) for
orthopaedic rehabilitation. It consisted ofa PC based
rehabilitation station with a Polhemustracker, and used the Rutgers
Master II glove. Theyhave developed three different 3D graphical
exercises
and two functional games (Pegboard and Ball game).Data is
collected into an Oracle database and sent viathe Internet to a
remote site for analysis. The system isat present undergoing
clinical trials at Stanford MedicalSchool.
2. GENTLE/s Neuro-Rehabilitation Approach
The GENTLE/s project is financed by the EuropeanCommission under
the Quality of Life initiative ofFramework 5, which aims to
evaluate robot-mediatedtherapy in stroke rehabilitation. The
project takes awide group of users to include patients, family
mem-bers, physicians, physiotherapists, and healthcare man-agers.
GENTLE/s is focused on neuro and physicalrehabilitation and
particularly concentrates on devel-oping new, challenging and
motivating therapies to aidthe increase of sensory input,
relearning stimulation inthe brain, and achieve functional goals
that improve in-dependence and coordination. A more detailed
descrip-tion of the different project phases is given in Harwinet
al. (2001).
The GENTLE/s approach utilises haptic and VirtualReality (VR)
technologies. Haptics is the study of in-tegrating tactile,
proprioceptors and other sensors intomeaningful information
(Gibson, 1966) and a hapticinterface uses constrained motion
devices to replicatesome of these sensory modalities. An initial
user needsstudy along with brainstorming sessions and input
frommembers of the Young Stroke Association at Stoke-on-Trent in
the UK, encouraged the group to develop theidea of using haptic and
VR technologies to delivertherapy. It was postulated from group
discussions thatbetter functional and motor recovery outcomes
couldbe achieved when patients receive a challenging
andmotivational machine mediated therapy in a contextthat allows
the stroke patient to feel comfortable andin control.
2.1. Assumptions
Some studies have shown that repetitive task-orientedmovements
are of therapeutic benefit. With the use ofhaptics and VR
technology, patient attention and moti-vation can be enhanced by
means of Active Feedbackthat will further facilitate motor recovery
through brainplasticity (Butefisch et al., 1995; Hesse et al.,
1995).Four different levels for Active Feedback have been
-
Upper Limb Robot Mediated Stroke Therapy 39
identified: visual, haptic, auditory and performancecues. The
creation of active agents and biofeedbackcan be a way of
implementing and integrating activefeedback in a
neuro-rehabilitation robotic system.
Visual cues: In some cases, following a stroke, hemi-plegic
subjects tend to be confused about what theysee (Westcott, 2000).
The brain needs to be re-educated to associate (for example)
colours and ob-jects. As a result of the need of cognitive
re-learningit is important that visual cues be simple, yet
stim-ulating. Visual cues can be represented using realtasks based
on the ones used in occupational ther-apy sessions, to realistic
and accurate goal oriented3D computer environments. This can be
anythingfrom a virtual room (for example a virtual kitchen
ormuseum) to an interactive game.
Haptic cues: Kinaesthetic feedback can help to dis-criminate
physical properties of virtual objects, suchas geometry. It can
also be used to deliver physicaltherapy to a human subject using
haptic interfaces.The force delivered in this way can be very
ther-apeutic dependent on the way we apply this forceto human
muscular and skeletal systems. It will un-doubtedly play an
important role when manipulatingobjects, either virtual or real. In
conjunction withinteractive virtual and augmented tasks, it can
sim-ulate the shape of a virtual pen, bingo card or
thefriction/drag when writing on the virtual bingo card.
Auditory cues: In some cases it may be appropriateto give
encouraging words and sounds when the per-son is trying to perform
a task and congratulatory orconsolatory words on task
completion.
Performance cues: In a haptic stroke rehabilitationsystem,
results of the previous tasks can be displayedindicating the errors
committed and the level ofhelp given when completing the task.
These perfor-mance cues should be designed to give
constructivefeedback.
A robotic/haptic rehabilitation system should be er-gonomically
comfortable. The therapy should be en-joyable and the system should
be considered trust wor-thy by the patient. Such a concept can be
achieved bythe introduction of a personality to the system, such
asa character (wizard) that interacts with the patient byusing
different identified cues. Different wizards canbe implemented for
different personalities. These canbe defined and assigned to the
patient by the therapist.
An example could be in the case where the patientis performing a
simple exercise, such as reaching for
an object in a virtual world. In this case, with the aidof a
good sensor system, analytical measures can beobtained in order to
identify if the patient is strugglingin reaching the target. Taking
this into account, thewizard could then offer encouragement to the
patientto finish the movement. Another scenario is to presentthe
score at the end of the session.
2.2. Current Prototype
The current prototype system (Fig. 1) consists of aframe, a
chair, a shoulder support mechanism, a wristconnection mechanism,
an elbow orthosis, two embed-ded computers, a large computer screen
with speakers,an exercise table, a keypad and a 3DOF haptic
inter-face (HapticMaster from FCS Robotics, see
www.fcs-robotics.com). Current generations of haptic technolo-gies
allow relative large reaching movements in threeactive degrees of
freedom. This couples to three pas-sive degrees of freedom to allow
arbitrary positioningof the persons hand. The patient is seated on
the chairwith his/her arm positioned in an elbow orthosis
sus-pended from the overhead frame (Fig. 2). This is toeliminate
the effects of gravity and address the prob-lem of shoulder
subluxation. The wrist is placed in awrist-orthosis connected to
the haptic interface usinga quick release magnetic mechanism (Fig.
2). At thispoint, the physiotherapist can select the patient
profilefrom the database, select an exercise or create a
newexercise using a 3D graphical user interface (Fig. 3).The setup
exercise (Figs. 4 and 5) allows the therapistto define the exercise
path, amount of help needed foreach segment of the exercise,
duration of the move-ment for each segment, the 3D context of the
virtualexercise environment. When the exercise definition hasbeen
saved and the exercise is selected, the system isready to perform
that exercise with the patient.
2.3. Exercises & Movement Guidance
In the current prototype, three different virtual environ-ments
can be used (Fig. 6):
1. Empty roomA simple environment that repre-sents the haptic
interface workspace and intends toprovide early post-stroke
subjects with awarenessof physical space and movement (Fig.
6(A)).
2. Real roomAn environment that resembles whatthe patient sees
on the table in the real world.
-
40 Loureiro et al.
Figure 1. Initial concept design for GENTLE/s Robot mediated
therapy.
The mat with 4 different shapes on the table(Figs. 2 and 3) is
represented in the 3D graph-ical environment (Fig. 6(B)). This
environmentwas developed to help discriminating the third
di-mension that is represented on the Monitor 2Dscreen.
3. Detail roomA high detail 3D environment of aroom comprising
of a table, several objects (a book,can of soft drink), portrait of
a baby, window, cur-tains, etc (Fig. 6(C)).
In order to allow the user to navigate and interact witha
virtual/real task, several mathematical models havebeen implemented
(further explained in Section 3) asa control strategy capable of
correcting the patientsmovement. An operation button on the keypad
must bepressed continuously by the user (Fig. 3) for the du-
ration of the movement. Since movement control wasdefined to be
in between two points, a new concept wasintroduced. The Bead
Pathway concept assumes thatmovement takes place in between a start
point and anend point. It is assumed that its behaviour is similar
tothe behaviour of beads on a wire, they can only movealong their
pathway. To achieve this behaviour the en-deffector is connected to
a virtual spring and damper(Fig. 7) where the bead is constrained
to move alonga wire-pathway that defines both the path and
thevelocity profile of the movement.
Deviations from the movement profile are permittedbut
constrained depending on the restoring force of thespring and
associated damper. Different levels of guid-ance and correction can
be programmed for differentsubjects with different recovery levels.
For a patient inearly days after stroke, more help is needed to
move
-
Upper Limb Robot Mediated Stroke Therapy 41
Figure 2. Subject using the GENTLE/s system. Arm is positionedin
an elbow orthosis suspended from the overhead frame and con-nected
to the robot using a wrist-orthosis that is secured using a
quickrelease magnetic mechanism.
Figure 3. Therapist setting therapy tasks and instructing
subject.
along the pathway, and this behaviour can be achievedby
implementing a velocity profile for the bead on thepathway and
proper spring-damper combination formore assistance. For a patient
who has recovered moremotor function, we may need a different
velocity pro-file along the pathway and a different setting
definedfor the spring-damper behaviour.
Figure 8 shows the representation of the pathway,where in part
(A) of the figure can be seen the yel-low start point (light
shaded), the blue end point (darkshaded) and the desired trajectory
between these twopoints (pathway). The choice these of colours
addressesthe problem of colour blindness with some primarycolours,
such as green and red that was noticed withsome subjects in earlier
studies (Loureiro et al., 2001).In part (B) of Fig. 8, shadows and
lines connectingshadow to object are used to help the user to
perceive thedepth and height of the positioned points with
respectto the table on the screen.
3. Implementation of Different Therapies
Several studies have agreed that the importance of aclear
understanding of how the human arm moves isachieved to supplement
interaction between a machineand a human subject. The urge to
understand humandynamics with emphasis on explaining how
humansbrain plans for reaching movements was first studiedby
Bernstein (1967) and later by Bizzi et al. (1984).This lead to the
study of several kinematic (Hogan,1984), dynamic (Uno et al.,
1989), and neural features(Georgopoulos et al., 1981) of human arm
reachingmovements. In most of these studies, one characteri-sation
of multi-joint planar reaching movements wasfound to be a straight
path with a bell shaped veloc-ity profile (Flash and Hogan, 1985;
Uno et al., 1989;Abend et al., 1982; Atkenson and Hollerbach,
1985).These studies suggest different optimisation modelsbased on
kinematic, dynamic or neural terms. Anoverview of these
optimisation models and techniquescan be found in Wolpert et al.
(1995) and Nakano et al.(1999).
The empirical minimum jerk approach is the sim-plest to
implement in a real-time system and such amodel was first proposed
for a single joint by Hogan(1984) and further developed for
multi-joint move-ments by Flash and Hogan (1985). The later
concludesthat humans by nature tend to minimise the jerk pa-rameter
over the duration of the reaching movement ofthe arm. Jerk is the
rate of the change of accelerationwith respect to time, (third time
derivative of the posi-tion). Minimum jerk theory states that any
movementwill have maximum smoothness when the magnitudeof the J
parameter given by Eq. (1) is minimised overthe duration of the
movement.
J = d
0| d3x/dt3 |2 dt (1)
-
42 Loureiro et al.
Figure 4. Setup exercise allows the therapist to define the
exercise path. Here a fork exercise is shown (further explained in
Section 3.1.4).
Figure 5. Users view of a reaching exercise.
-
Upper Limb Robot Mediated Stroke Therapy 43
Figure 6. (A) Empty room, (B) real room, and (C) detail
room.
Figure 7. Spring and damper combinationBead pathway.
3.1. Theoretical Models
The models presented in this paper are based on theuse of
polynomials to control position, velocity andacceleration
parameters encountered in a human basedmovement profile. The use of
polynomials has com-putational advantages for use in real-time
applications,particularly in rehabilitation. Using this
methodology,control of human trajectory is enhanced by the
flexi-bility of being able to redefine polynomials or
super-imposing a new trajectory over the previous one
inreal-time.
Figure 8. Representation of a movement trajectory in the virtual
environment.
Figure 9. Point-to-point movement definition.
3.1.1. Minimum Jerk Point-to-Point Model. Thefirst model is
based on the assumption that every move-ment happens in between a
start point and an endpoint, from which a straight-line path is
generated(Fig. 9).
In this case it is advantageous to have accelerationsthat are
zero at the start and end of the movement. Forthis a parameter is
chosen such that:
1 1
-
44 Loureiro et al.
This parameter ( ) in a later stage can be scaledto the movement
exact time. Symmetrical movementshave a mid range position,
velocity and accelerationoccurring when = 0 which in turn
simplifies the cal-culation of the polynomial coefficients at a
later stage.To ensure that the acceleration at the start and end
iszero, a polynomial with odd power is used. In the liter-ature
(Flash and Hogan, 1985) a 5th order polynomialhas already been
used. However, in order to allow fornon-symmetric, non-minimum jerk
polynomials to co-exist with symmetric minimum jerk polynomials, a
7thorder polynomial is used:
p = a + b + c 2 + d 3 + e 4 + f 5 + g 6 + h 7(2)
The derivatives with respect to the parameter ( ) aredenoted as
the more familiar p, p, p. The followingidentities are constraints
applied to the start and end ofthe movement:
Start and end positions are defined:
p|=1 = pstart p|=1 = pend
Start and end velocities and accelerations are zero:
p|=1 = 0 p|=1 = 0p|=1 = 0 p|=1 = 0
Hence, the polynomial becomes:
p = a + b + d 3 + f 5 + h 7 (3)
We can then identify the coefficients of the polynomialin Eq.
(3) as:
a = (pstart + pend)2
(4)b = p|=0 = vmid (5)d = 35
16p 3b (6)
f = 3b 218
p (7)
h = 1516
p b (8)
Where
p = pend pstart (9)
Mid velocity (vmid) needs to be determined in orderto minimise
the integral given by Eq. (10) and achievea minimum jerk
movement.
J = 1
1|p|2d (10)
Thus, to achieve minimum jerk, the mid velocityshould be:
b = 1516
p (11)
At which point h = 0 and the polynomial becomes5th order. The
minimum jerk model and polynomialspresented in this section were
used to implement thetherapy modes explained in Section 3.2 and to
generateminimum jerk paths for the Bead-Pathway explainedin Section
2. Polynomial coefficients are calculated be-tween end-effectors
current position (P1 in Fig. 9) andtarget position (P2 in Fig.
9).
3.1.2. Up and Over Model. Often rehabilitation ex-ercises
involve the recovery of function for actionsinvolving lifting and
transporting an object from one lo-cation to another. Such
curvilinear non-minimum jerkmovement patterns require an even order
polynomial(Eq. (12)) for the vertical axis of the movement:
p = a + c 2 + e 4 + g 6 (12)
In order to elevate the trajectory by the amount of Hand go back
to the start point, these additional assump-tions are made:
Start and end positions are the same:
pstart = pend
Movement is symmetric:
a = p|=0 = pstart + H
Hence the polynomial coefficients become:
g = H (13)c = 3H (14)e = 3H (15)
-
Upper Limb Robot Mediated Stroke Therapy 45
Figure 10. Super-positioning model. Left: superimposed
positions, and Right: superimposed velocities.
3.1.3. Super-Positioning Model. Certain move-ments, however, are
not straight-line movements.These can be movements such as placing
cubes on topof each other or placing a book on a shelf.
For this, a technique comparable to the one usedby Flash and
Henis (1991) whereby movements thatare not straight-line or
non-symmetrical by nature areachieved can be used. It consists of
superimposing twodistinct trajectories to create a third one.
If two different polynomials are given:
p = a + b + d 3 + f 5 + h 7 (16)q = a + c 2 + e 4 + g 6 (17)
A different trajectory can be created by addingthe polynomials
in Eqs. (16) and (17) vectorially(Fig. 10):
h = p + q (18)
The super-positioning model allows for smooth tra-jectories to
be achieved when the minimum jerk param-eters for Eqs. (16) and
(17) are minimised separately.In this case the parameter ( ) for
the input polyno-mials can be mapped to time differently, therefore
thesecond trajectory (Eq. (17)) does not need to begin at = 1.
3.1.4. Fork Model. The Fork model was created withthe intention
of augmenting the existing therapy mod-els by allowing the user to
have the freedom to decidewhich target to choose. Comparing this
model to thepoint-to-point model, the only difference is that
move-ments are not generated sequentially (i.e., from P1 toP2, P2
to P3, etc.) but instead the user is able to decide
Figure 11. Fork model. (A) Target selection and (B) vector
dotproducts.
if it is more appropriate to move from P1 to P2 or fromP1 to P4
(Fig. 11(A)).
From a clinical point of view, apart from providingthe stroke
patient with repetitive challenge therapies,the ability to choose
can be motivational and be oftherapeutic benefit.
The fork model uses the force readings provided bythe force
sensor mounted on the end-effector of thehaptic interface to
identify the target that the patientwants to select. Once the user
attempts to move in thedirection of his/her chosen target, the
vector dot prod-ucts are used to detect the direction and the
amount offorce exerted by the user in the direction of the
targetpoint (Fig. 11(B)).
Vector dot product of the users force vector, ontovectors V1 and
V2, can be used to detect which targetis selected by the user. We
know that:
V1 = p2 p1 (19)V2 = p3 p1 (20)
-
46 Loureiro et al.
If the user is exerting a force F on the haptic inter-face, then
1 and 2 can be estimated as:
cos 1 =F V1| F || V1|
(21)
cos 2 =F V2| F || V2|
(22)
It is also known that, if both vectors are on the sameside of
the plane normal to the force applied, then cos1 0, and 0 1 . Thus
the algorithm fordetecting the target becomes:
Step 1. Calculate V1 and V2 using Eqs. (19) and (20)Step 2.
Calculate cos 1 0 and cos 2 0 using
Eqs. (21) and (22)Step 3. IF cos 1 0 OR cos 2 0 AND F
FActivation
(a defined threshold value) then:
Step 3.1. IF cos 1 cos 2 then assume 2 1which means target P2 is
selectedELSE IF cos 2 cos 1 then assume 1 2which means target P3 is
selected
Step 3.2. Initiate minimum jerk trajectory to appro-priate
target.
In order to determine the intended target from morethan two
choices, the algorithm becomes to find themaximum positive value
for cos ( i ) where i is the in-dex number assigned to targets from
the fork junction.
3.1.5. Time Mapping Model. The parameter ( ) pro-vides a useful
scaling mechanism for the polynomialimplementation. This parameter
can be scaled to time
Figure 12. Time mapping model. Left: time mapped positions and
Right: time mapped velocities.
using a linear or quadratic scaling, which in turn al-lows for
different movements to be attained dependingon the scaling factor.
In this context, Eq. (23) can beused to scale time (t) to ( )
linearly with respect to tStart= 0 and tEnd = duration.
= 1 2(t tend)tstart tend (23)
Different time mappings are possible; for example aquadratic
mapping can be used:
= at2 + bt + c (24)
Where:
0 t duration (25)
Hence the coefficients for this quadratic become:
c = 1 (26)a =
(2d2
bd
)(27)
Figure 12 shows a comparison in between linear andnon-linear
mapping (b = 1) of parameter ( ) for bothpositions and
velocities.
3.2. Different Therapy Modes
Using the minimum jerk polynomials three differenttherapy modes
are implemented on the GENTLE/ssystem:
3.2.1. Patient Passive Mode. The Patient Passivemode was the
first therapy mode implemented. As
-
Upper Limb Robot Mediated Stroke Therapy 47
the patient lacks the power to initiate the movementand remains
passive, the haptic interface will move thearm along the
pre-defined path. When the patients armreaches the target,
depending on the exercise selected,the movement can be reversed
back to the start positionor continued towards the next defined
position.
3.2.2. Patient Active Assisted Mode. The secondmode is Patient
Active-Assisted mode. In this mode,the haptic interface starts
moving as soon as the pa-tient initiates a movement in the
direction of the path-way. The haptic interface initiates the
movement whenFUser U > FActivation, where U is the position
vectorbetween the start point and the end point. After the
ini-tiation is made, the haptic interface helps the user toreach to
the end point.
3.2.3. Patient Active Mode. The third mode is Bead-Pathway
(ratchet) mode or Active mode. The velocityprofile for this mode
was set to zero to provide un-limited time for the patient to
finish the correct task.This mode provides a unidirectional
movement, wherethe amount of deviation can be controlled by
chang-ing spring-damper coefficients. Similar to the previousmode,
the user initiates the right movement. The hap-tic interface stays
passive until the user deviates fromthe predefined path. In this
case, the spring-dampercombination encourages the patient to return
to thepathway. This operation can end by reaching the endpoint or
releasing the operation button. Upon arrivalat the end point, it is
up to the user to continue thesame movement back to the start
point, a new pointor end the whole session in this mode. To
implementthis mode, a ratchet or energy function was calculatedso
that the user can only move towards the movementgoal. The ratchet
function relies on the actual posi-tion of the robot pa and the
position of the bead pto calculate an energy. Thus at a particular
setting ofthe parameter t the energy would be E(t) = (p(t)
=pa)2.
When user moves along the path, if the movement in-volves less
energy, then that position is accepted at thecurrent position of
the bead on the pathway, otherwise,the virtual spring damper will
resist the users move-ment to higher energy states. This means, if
t = t1,then for t2 > t1, E(t1) and E(t2) are calculated, ifE(t2)
< E(t1) then t is adjusted to be the new value t2.This algorithm
has the effect of only allowing one-waymovement along the
pathway.
4. Clinical Trials
A pilot study was carried out in the spring of 2001 and
aprinciple study conducted from autumn of 2001 to au-tumn of 2002.
The choice of sites in both the UK andIreland gives the study
access to a greater number ofsubjects (31 in total) for inclusion
in the clinical trials.In Dublin the studies were conducted at the
Adelaide& Meath Hospital, a teaching hospital of Trinity
Col-lege, Dublin, and in the UK at the Battle Hospital
inReading.
Some of the initial results of the pilot study are pub-lished
(Loureiro et al., 2001) with more detailed resultsof the principal
clinical trial to be published (Cooteet al., 2003; Amirabdollahian
et al., 2003). Results fromthe initial pilot studies showed that
the majority of thesubjects were enthusiastic about the use of
visual andhaptic cues. The trial suggested that the system as
awhole was able to motivate people with hemiplegia asa result of
stroke and encourage them to participate andexercise more. The
pilot study was used to evaluate thelevel of forces that should be
imposed by the virtualsprings and dampers and the subjects
difficulties in in-teracting with the forces. It also assessed the
level ofinteraction with the three virtual rooms and the
conse-quent motivation. An encouraging data was that 7 ofthe 8
patients in the pilot stopped exercising becauseof fatigue rather
than boredom, confirming the designobjective of providing
motivating therapies.
The principle study was conducted in the Adelaide& Meath
Hospital (AMH) center (19 subjects) and inBattle Hospital (BH)
center (11 subjects). In both cen-tres subjects were divided into
two randomised groups,ABC (AMH = 10, BH = 6) and ACB (AMH = 9, BH
=5) and each group involved in three phases, each phasehaving
duration of 9 trial sessions in three weeks. Un-der phase one the
subject was assessed using validatedoutcome measures in order to
identify the underlyingbaseline. At the start of the next two
phases, the sub-jects paretic limb was first assessed using
selected out-come measures. The B phase was the Robot
MediatedTherapy (RMT) customised for each individual. Dur-ing phase
C the subjects paretic limb was suspendedusing sling suspension
techniques. The results in bothcentres have shown a positive
development towards theuse of RMT as a useful intervention late
after stroke inthe assistance of UL rehabilitation. The data from
theprincipal study is in accordance with findings of theMIT MANUS
studies (Krebs et al., 1999). The salientconclusion to arise from
this study is that this mode of
-
48 Loureiro et al.
therapy appears to be particularly appropriate for moreseverely
disabled patients. Further studies are neededto establish the
level, nature, onset and determinationof treatments that will
result in the best recovery for anindividual patient.
5. Conclusion
This paper introduces a new approach to machinemediated
neuro-rehabilitation, based on integrating ap-propriate haptic
technologies to high quality virtual en-vironments, so as to
deliver challenging and meaning-ful therapies to people with upper
limb impairment inconsequence of a stroke.
A mathematical framework is given to computenatural paths for
machine-assisted movements basedon polynomials that can be
constrained to provide socalled minimum jerk movements that have
been shownto characterise typical reaching movements. Oncethe
polynomial pathway is established these can betraversed in a number
of ways from patient passivewhere the haptic interface shapes the
movement, toproviding an errorless constraint such that, the
hapticdevice only corrects deviations from the ideal path.This
framework is ideal for providing the challengingand motivating
therapies to people with upper limbhemiplegia following a stroke,
as it can be adaptedto provide a variety of levels of patient
participationincluding the requirement for the patient to preplan
anddecide which of two or more movements he/she shouldcomplete.
Two identical prototypes have undergone extendedclinical trials
in the UK and Ireland with a cohort of31 stroke subjects. The
clinical outcomes for these stud-ies are in the process of
publication and are summarisedin this paper.
A second prototype is under development to improvethe current
prototype at hardware and software levels.
Although 3D visual environments are relatively easyto implement
the current generation requires wearingof glasses or a head-up
display and the therapist in-put considered this too distracting
for people who mayalso have visual and cognitive problems
associated withtheir stroke. It is clear also that high quality
therapydevices of this nature have a role in future deliveryof
stroke rehabilitation, and machine mediated thera-pies should be
available to patient and his/her clinicalteam from initial hospital
admission, through to longterm placement in the patients home
following hospitaldischarge.
Acknowledgments
The work presented in this paper has been car-ried out with
financial support from the Commis-sion of the European Union,
Framework 5, spe-cific RTD programme Quality of Life and
Man-agement of Living Resources, QLK6-1999-02282,GENTLE/SRobotic
assistance in neuro and motorrehabilitation. It does not
necessarily reflect its viewsand in no way anticipates the
Commissions futurepolicy in this area.
We are grateful to all our colleagues in theGENTLE/s consortium
(University of Reading,UK; Rehab Robotics, UK; Zenon, Greece;
Virgo,Greece; University of Stafordshire, UK; University
ofLjubljana, Slovenia; Trinity College Dublin, Ireland;TNO-TPD,
Netherlands; University of Newcastle, UK)for their ongoing
commitment to this work.
Special thanks to Mr. Paul Hawkins for providingthe drawing used
in Fig. 1.
GENTLE/s website: http://www.gentle.rdg.ac.uk
Notes
1. EASREuropean Age Standardised Rate.2. 55 years old.3. (2002).
StrokeFacts and figures statistics, UK National
Stroke Association; web resource:
http://www.stroke.org.uk/noticeboard/facts.htm (Accessed on
28-08-02).
4. Mortality rate: 109 per 100,000, 1999.
References
Abend, W., Bizzi, E., and Morasso, P. 1982. Human arm
trajectoryformation. Brain, 105:331348.
Abrams, W. and Berkow, R. (Ed.). 1997. Merk Manual of
Geriatrics,Merk Research Laboratories, Whithouse Station, NJ.
Amirabdollahian, F., Gradwell, E., Loureiro, R., Collin, C.,
andHarwin, W. 2003. Effects of the gentle/s robot mediated
therapyon the outcome of upper limb rehabilitation post-stroke:
Analysisof the battle hospital data. In Proc. 8th Int. Conf. Rehab.
Robotics,(ICORR 2003), Published by HWRS-ERC Human-friendlyWelfare
Robot System Engineering Center, KAIST, Republic ofKorea, pp. 5558,
April 2325 (ISBN 89-88366-09-3 93560).
Ashburn, A. 1993. Physiotherapy in the rehabilitation of stroke:
Areview. Clin Rehabil, 7:337345.
Atkenson, C.G. and Hollerbach, J.M. 1985. Kinematic features
ofunrestrained vertical arm movements. Journal of
Neuroscience,5:23182330.
Bernstein, N. 1967. The Coordination and Regualtion of
Movements,Pergamon, London.
Bizzi, E., Accornero, N., Chapple, W., and Hogan, N. 1984.
Posturecontrol and trajectory formation during arm movement.
Journalof Neuroscience, 4:27382744.
-
Upper Limb Robot Mediated Stroke Therapy 49
Bobath, B. 1978. Adult Hemiplegia: Evaluation and
Treatment,London, Heinemann.
Broeks, J.G., Lankhorst, G.J., Rumping, K., and Prevo, A.J.
1999.The long-term outcome of arm function after stroke: Results of
afollow up study. Disabil & Rehabil, 21(8):357364.
Brown, P., Jones, D., Singh, S., and Rosen, J. 1993. The
exoskeletonglove for control of paralysed hands. In Proc. IEEE Int.
Conf. onRobotics and Automation, Atlanta, GA, pp. 642647.
Butefisch, C., Hummelsheim, H., Denzler, P., and Mauritz,
K.H.1995. Repetitive training of isolated movements improves the
out-come of motor rehabilitation in the centrally paretic hand.
JournalNeurological Sciences, 13:5968.
Carr, J.H. and Shepherd, R.B. 1987. A Motor Relearning
Programmefor Stroke, 2nd ed., Oxford, Butterworth Heinemann.
Coote, S. and Stokes, E. 2001. Physiotherapy for upper
extremitydysfunction following stroke. Physical Therapy Reviews,
6:6369(W.S. Maney & Son Ltd).
Coote, S., Stokes, E., Murphy, B., and Harwin, W. 2003. The
ef-fect of GENTLE/s robot mediated therapy on upper
extremitydysfunction post stroke. In Proc. 8th Int. Conf. Rehab.
Robotics,(ICORR 2003), Published by HWRS-ERC Human-friendlyWelfare
Robot System Engineering Research Center, KAIST,Republic of Korea,
pp. 5961, April 2325 (ISBN 89-88366-09-393560).
Cotton, E. and Kinsman, R. 1983. Conductive Education for
AdultHemiplegia, Edinburgh, Churchill Livingstone.
Dijkers, M.P., deBear, P.C., Erlandson, R.F., Kristy, K., Geer,
D.M.,and Nichols, A. 1991. Patient and staff acceptance of robot
tech-nology in occupational therapy: A pilot study. Journal of
Rehabil-itation Research and Development, 28(2):3344.
Ernst, E. 1990. A review of stroke rehabilitation and
physiotherapy.Stroke, 21:10811085.
Feys, H.M., de Weert, W.J., Selz, B.E., Cox Steck, G.A.,
Spichiger,R., Vereeck, L.E., Putman, K.D., and van Hoydonk, G.A.
1998.Effect of a therapeutic intervention for the hemiplegic upper
limbin the acute phase after stroke: A single-blind, randomised,
con-trolled multicentre trial. Stroke, 29(4):785792.
Flash, T. and Henis, E. 1991. Arm trajectory modification
duringreaching toward visual targets. Journal of Cognitive
Neuroscience,3:220230.
Flash, T. and Hogan, N. 1985. The coordination of arm
movements:An experimentally confirmed mathematical model. Journal
ofNeuroscience, 5(7):16881703.
Georgopoulos, A.P., Kalaska, J.F., and Massey, J.T. 1981.
Spatialtrajectories and reaction times of aimed movements: Effects
ofpractice, uncertainity and change in target location. Journal
ofNeurophysiology, 46:725743.
Gibson, J.J. 1966. The Senses Considered as Perceptual
Systems,Houghton Mifflin Company.
Harwin, W., Loureiro, R., Amirabdollahian, F., Taylor, M.,
Johnson,G., Stokes, E., Coote, S., Topping, M., Collin, C.,
Tamparis, S.,Kontoulis, J., Munih, M., Hawkins, P., and Driessen,
B. (2001).The GENTLES/S project: A new method of delivering
neuro-rehabilitation. In Proc. Association for the Advancement of
Assi-stive Technology in Europe (AAATE 2001), Assistive
Technology-Added Value to the Quality of Life, Assistive Technology
ResearchSeries, Crt Marincek, C. Buhler, H. Knops, and R. Andrich
(Eds.),IOS Press, 10:3641.
Hesse, S., Bertelt, C., Jahnke, M.T., Schaffrin, A., Baake, P.,
Malezic,M., and Mauritz, K.H. 1995. Treadmill training with partial
body
weight support compared with physiotherapy in
non-ambulatoryhemiparetic patients. Arch Phys Med Rehabil,
75(10):10871093.
Hogan, N. 1984. An organizing principle for a class of
voluntarymovements. Journal of Neuroscience, 4:27452754.
Hogan, N., Krebs, H.I., Sharon, A., and Charnnarong, J.1995.
Interactive Robotic Therapist, U.S. Patent #5,466,213,MIT.
Isard, P.A. and Forbes, J.F. 1992. The cost of stroke to the
National-Health-Service in Scotland. Cerebrovascular Diseases,
2:4750.
Johnson, M.J., Van der Loos, H.F.M., Burgar, C.G., and Leifer,
L.J.1999. Drivers SEAT: Simulation environment for arm therapy.
InProc. 6th Int. Conf. Rehab. Robotics, ICORR99, Stanford, CA,USA,
pp. 227234.
Johnson, M.J., Van der Loos, H.F.M., Burgar, C.G., Shor, P.,
andLeifer, L.J. 2001. Designing a robotic stroke therapy deviceto
motivate use of the impaired limb. In Proc. 7th Int. Conf.Rehab.
Robotics, ICORR 2001, Integration of Assistive Technol-ogy in the
Information Age, Evry, France, Vol. 9, M. Mokhtari(Ed.), IOS Press,
pp. 123132.
Johnson, M.J., Van der Loos, H.F.M., Burgar, C.G., Shor, P.,
andLeifer, L.J. 2002. Design of drivers SEAT: A car steering
sim-ulation environment for upper limb stroke therapy. In
Robotica,to appear.
Knott, M. and Voss, D.E. 1968. Proprioceptive Neuromuscular
Stim-ulation, New York, Harper & Row.
Krebs, H.I., Hogan, N., Volpe, B.T., Aisen, M.L., Edelstein, L.,
andDiels, C. 1999. Robot-aided neuro-rehabilitation in stroke:
Three-year follow-up. In Proc. 6th Int. Conf. Rehab. Robotics,
ICORR99,Stanford, CA, USA.
Kwakkel, G., Wagenaar, R.C., Koelman, T.W., Lankhorst, G.J.,
andKoetsier, J.C. 1997. Effects of intensity of rehabilitation
afterstroke, a research synthesis. Stroke, 28:15501556.
Lincoln, N.B. and Parry, R.H. 1999. Randomised, controlled
trialto evaluate increased intensity of physiotherapy treatment of
armfunction after stroke. Stroke, 30(10):22422243.
Loureiro, R., Amirabdollahian, F., Coote, S., Stokes, E., and
Harwin,W. 2001. Using haptics technology to deliver motivational
ther-apies in stroke patients: Concepts and initial pilot studies.
InProc. 1st European Conference on Haptics, EuroHaptics
2001,Educational Technology Research Paper Series, University
ofBirmingham, UK, pp. 16, ISSN 14639394.
Lum, P.S., Burgar, C.G., Kenney, D.E., and Van der Loos,
H.F.M.1999. Quantification of force abnormalities during passive
andactive-assisted upper-limb reaching movements in
post-strokehemiparesis. IEEE Transactions on Biomedical
Engineering,46(6):652662.
Nakano, E., Imamizu, H., Osu, R., Uno, Y., Gomi, H., Yoshioka,
T.,and Kawato, M. 1999. Quantitative examinations of internal
rep-resentations for arm trajectory planning: Minimum
commandedtorque change model. Journal of Neurophysiology,
81(5):21402155.
ONS 2000. Mortality Statistics: Cause, Office for National
Statistics,276.
Parker, V.M., Wade, D.T., and Langton-Hewer, R. 1986. Loss of
armfunction after stroke: Measurement, frequency, and recovery.
IntRehabil Med, 8:6973.
Petersen, S., Rayner, M., and Press, V. 2000. Coronary Heart
Dis-ease Statistics, 2000 ed., The British Heart Foundation
StatisticsDatabase.
-
50 Loureiro et al.
Popescu, V.G., Burdea, G.C., Bouzit, M., and Hentz, V.R. 2000.A
virtual-reality-based telerehabilitation system with force
feed-back. IEEE Trans. on Information Technology in
Biomedicine,4(1):4551.
Rao, R., Agrawal, S.K., and Scholz, J.P. 1999. A robot test bed
forassistance and assessment in physical therapy. In Proc. 6th
Int.Conf. Rehab. Robotics, ICORR99, Stanford, CA, USA, pp.
187200.
Reinkensmeyer, D.J., Pang, C.T., Nessler, J.A., and Painter,
C.C.2001. Java therapy: Web-based robotic rehabilitation. In
Proc.7th Int. Conf. Rehab. Robotics, ICORR 2001, Integration of
As-sistive Technology in the Information Age, Evry, France, Vol.
9,M. Mokhtari (Ed.), IOS Press, pp. 6671.
Rood, M. 1954. Neurophysiological reactions as a basis for
physicaltherapy. Phys Ther Rev., 34:444449.
Sackley, C.M. and Lincoln, N.B. 1996. Physiotherapy for
strokepatients: A survey of current practice. Physiotherapy Theory
&Practice, 12:8796.
Shor, P.C., Lum, P.S., Burgar, C.G., Van der Loos,
H.F.M.,Majmundar, M., and Yap, R. 2001. The effect of
robotic-aidedtherapy on upper extremity joint passive range of
motion pain. InProc. 7th Int. Conf. Rehab. Robotics, ICORR 2001,
Integration ofAssistive Technology in the Information Age, Evry,
France, Vol. 9,M. Mokhtari (Ed.), IOS Press, pp. 7983.
Stewart, J.A., Dundas, R., Howard, R.S., Rudd, A.G., and
Wolfe,C.D.A. 1999. Ethnic differences in incidence of stroke:
Prospec-tive study with stroke register. British Medical Journal
(BMJ),318:967971.
Sunderland, A., Tinson, D.J., Fletcher, D., Langton-Hewer, R.,
andWade, D.T. 1992. Enhanced physical therapy improves arm
func-tion after stroke, a randomised controlled trial. J. Neurol
NeurosurgPsychiatry, 55:530535.
Uno, Y., Kawato, M., and Suzui, R. 1989. Formation and control
ofoptimal control trajectories in human multijoint arm
movements:Minimum torque change model. Biological Cybernetics,
61:89101.
Westcott, P. 2000. StrokeQuestions and Answers, The
StrokeAssociation, Stroke House, Whitecross Street, London.
Wolpert, D.M. et al. 1995. Are arm trajectories planned in
kinematicor dynamic coordinatesAn adaptation study. Exp Brain
Res,103(3):460470.
Rui Loureiro is a research officer working in the field of
Med-ical Robotics in the department of Cybernetics at Reading
Uni-versity, a Member of the Institution of Electrical Engineers
(IEE)and a student Member of the Institution of the Electrical and
Elec-tronics Engineers (IEEE). He is also pursuing a Ph.D. in
Neuro-Haptics Rehabilitation Systems on a part-time basis. He
received the
B.Eng. (HONS) degree in Electronic & Computer Engineering
fromBolton Institute, Manchester in 1998 and a M.Sc. degree in
AdvancedRobotics from Salford University, Manchester in 2000. From
1997to 1999 he worked in industry as a technical consultant in the
fieldof Electronic Process Control. His research interests are in
Neuro-Rehabilitation Robotics, Haptic Interfaces, Novel-Actuator
Systemsand Real Time Simulation & Processing. He was involved
in theGENTLE/s project since its start and is currently working on
a Euro-pean project looking at Upper Limb Dynamically Responsive
TremorSuppression.
Farshid Amirabdollahian is a research student and a research
offi-cer in the department of Cybernetics, Reading University. He
is alsoa student member of Institute of Electrical and Electronics
Engineers(IEEE). He received his B.Sc. in Mathematics majoring
ComputerScience in Azad University, Tehran, Iran in 1995. He is
currentlyfinishing his Ph.D. in Upper Limb Stroke Rehabilitation
Robotics.From 1995 to 1997 he worked as a software engineer, until
1999he worked as senior Engineer and database administrator for
sev-eral companies. His current research interests include human
armtrajectories for neuro-rehabilitation, arm movement
quantification,control strategies and real-time control issues in
man-machine in-teraction. He has been involved in the GENTLE/s
project since itsstart.
Michael Topping received a BA, Cert Ed. From Keele Uni-versity
in 1988 and is currently a professor at StaffordshireUniversity;
Research Development Manager at the North StaffsHealth Authority,
Stoke-on-Trent, and a Clinical Scientist at Re-hab Robotics,
Birmingham. Professor Topping has extensive experi-ence with
assistive technologies for people with motor impairments,where in
19881992 designed and developed the Handy 1 RoboticAid to Eating
and Drinking when Research Manager at Keele Uni-versity. He his
responsible for the assessment, placing and after careof over 120
Handy 1 eating and drinking systems in the UK todate. Project
Leader for several university projects now underway todetermine the
benefits gained from using eating and drinking aids toacquire
independence at mealtimes. Participant and leader of severalpast
and current European Union projects.
-
Upper Limb Robot Mediated Stroke Therapy 51
Ir. Bart J.F. Driessen is with TNO TPD in the Netherlands. He
stud-ied electrical engineering in Delft from 19841990. His
graduationsubject was the design of a control architecture for the
MANUSwheelchair mounted manipulator. At TNO he is senior project
man-ager with a focus on intelligent autonomous systems. Main
applica-tion areas are i) service robotics (MANUS manipulator,
GENTLE/ssystem), ii) transportation systems (autonomous container
transport,obstacle detection), and iii) agricultural systems
(automatic mush-room harvesting, automatic flower picking). Current
research area ison visual servoing combined with force control.
William Harwin received a BA in engineering from
CambridgeUniversity in 1982, and MSc in Bioengineering from
Strathclyde
University in 1983. Following a period as a research assistant
atthe Engineering Department of Cambridge University he began
aPh.D. on computer recognition of the head gestures made by peo-ple
with cerebral palsy, work he completed in 1991. His
researchinterest is in machines and the human system. In 1983 he
workedat the Cambridge University Engineering Department on
rehabili-tation robotics research. Between 1990 and 1995 he
directed reha-bilitation robotics research at the Applied Science
and EngineeringLaboratories in Delaware, USA. He moved to the
Department ofCybernetics at the University of Reading, England in
1996 wherehe directs work at the Human-Robot Interface Laboratory
(tHRILwww.cyber.rdg.ac.uk/W.Harwin). Current research work
includesrobot assistance of human movements, information content of
hap-tic interfaces and lumped parameter modeling of human
interactionsinvolving power exchange.