Effect of Arm Deweighting using End-effectorbased Robotic
Devices on Muscle Activity
Justin Fong1, Vincent Crocher1, Raneem Haddara1,David Ackland1,
Mary Galea2, Ying Tan1 and Denny Oetomo1
Abstract—Deweighting of the limb is commonly performedfor
patients with a neurological injury, such as stroke, as itallows
these patients with limited muscle activity to performmovements.
Deweighting has been implemented in exoskeletonsand other
multi-contact devices, but not on an end-effectorbased device with
single contact point between the assistingrobot and the human limb
being assisted. This study inves-tigates the effects of deweighting
using an end-effector baseddevice on healthy subjects. The muscle
activity of five subjectswas measured in both static postures and
dynamic movements.The results indicate a decrease in the activity
of muscles whichtypically act against gravity — such as the
anterior deltoid andthe biceps brachii — but also suggest an
increase in activity inmuscles which act with gravity — such as the
posterior deltoidand the lateral triceps. This can be explained by
both thechange in required muscle-generated torques and a
consciouschange in approach by the participants. These
observationshave implications for neurorehabilitation, particularly
withrespect to the muscle activation patterns which are
trainedthrough rehabilitation exercises.
Keywords—Deweighting, neurorehabilitation, robotics,
Each year, more than 15 million people experience theirfirst
stroke, with approximately two in three surviving.Unfortunately a
large proportion of stroke survivors remaindisabled after their
incident, with up to two in three beingpermanently disabled .
Providing adequate opportunitiesfor rehabilitative exercise to
these survivors remains a chal-lenge for health systems around the
Robotic devices have been viewed as tools which canbe used to
improve accessibility to upper limb training afterstroke , given
their ability to mechanically assist move-ments. Such devices are
commonly developed for those withmuscle weakness, which limits
their capacity to exerciseindependently. These devices take the
form of either anexoskeleton, which aims to provide a robotic joint
for eachcontrolled physiological joint (such as the ARMin ),
orend-effector based devices, which make contact with the armat
only a single location (such as the ADLER  or theEMU ).
A common practice in rehabilitation for people withmuscle
weakness in the upper limb is the mechanicaldeweighting of the arm
through providing external support.
1Melbourne School of Engineering, the University of
Melbourne2Faculty of Medicine, Dentistry and Health Sciences, the
This allows the patients to use their limited muscle functionto
initiate movements, without first having to overcome theeffects of
gravity –. Control strategies for achievingthis have been
developed for both exoskeleton-style roboticdevices , and
recently, in a previous work by the authors,for end-effector based
Although the goal of deweighting the arm is to reducethe muscle
activation levels required to hold or move thepatient’s arm, the
support itself is provided only at thejoint movement level (as
opposed to directly assisting indi-vidual muscles). Deweighting
therefore does not uniformlydecrease the activation levels across
all muscles or evenmuscle groups for a given movement. As
neurorehabiltiationoften focuses on retraining muscle recruitment
and muscleactivation patterns, it is important that when analysing
arobotic control strategy, the effects at the muscle level
Therefore, the goal of this work was to evaluate the effectof a
deweighting control algorithm on muscle recruitment.Previous
analyses have been conducted for the exoskeletondevices , ,
as well as passive deweighting devices,  but is yet to be
applied to the end-effector basedrobotic concept. The use of an
end-effector based devicemeans that the effects of gravity in the
joint space cannot becompletely compensated for, as the device
provides supportat a single point of the human arm. The work sought
tounderstand the consequences of such conditions through theuse of
an experimental protocol investigating both staticpostures and
II. DEWEIGHTING ALGORITHM
The deweighting algorithm evaluated within this workwas
introduced and validated using a mechanical arm in .However, a
brief overview is provided here.
The algorithm is derived by modelling the human armas a two-link
mechanism with 3 intersecting revolute jointsrepresenting the
shoulder (S) and a single revolute jointrepresenting the elbow (E)
(see Figure 1). It is assumedthat the robotic device is capable of
applying a generalisedforce (fr,mr) at its end-effector to the
human wrist (W ).
The equation of motion of the human arm, which can bewritten in
the following form, is then considered:
Mh(qh)q̈h + Ch(qh, q̇h)q̇h + gh(qh) = τh, (1)
where Mh(qh) ∈ R4×4, Ch(qh, q̇h) ∈ R4×4 and gh(qh) ∈R4 are the
mass, Coriolis matrix and gravity components of
Fig. 1: The two-link mechanism representing the arm. q1−3combine
to represent the shoulder joint, and q4 representsthe elbow joint.
lua, lfa,mua,mfa represent the lengths andmasses of the upper and
the human arm, and τh ∈ R4 is the torque at each jointgenerated
by the human through their muscles.
The objective of the deweighting algorithm is for therobot to
apply a generalised force (fr,mr) to compensatefor the gh(qh)
component of the equation of motion of thehuman arm, such that τh,
the force required to be producedby the human muscles, is reduced.
This can be calculatedusing information about the posture of the
The robotic device (EMU, ) utilised in this experimentis
underactuated, with only 3 actuated degrees of freedom,applying fr
but no moment (mr = 0) through its end-effector to the human wrist.
The force applied at the end-effector is therefore of the form:
fr = JT#h (qh)g(qh), (2)
where JT#h (qh) is the posture-dependent generalised inverseof
the Jacobian of the human’s arm.
This study investigated the effects of the deweightingalgorithm
on human subjects, through the implementationon the EMU upper limb
rehabilitation robotic device andthe measurement of
electromyographic (EMG) data in staticand dynamic conditions. Five
healthy male subjects (age27.4±4.7 years, weight 62.6±9.1 kg) were
included in thisstudy, which was approved by The University of
MelbourneHuman Ethics Advisory Group (ID: 1749444).
A. Apparatus and Parameters
The proposed method requires an estimation of anthro-pomorphic
parameters of the subject — namely the masses(mua and mfa), lengths
(lua and lfa) and locations of thecentres of mass (lcua and lcfa)
of the upper-arm and forearm.Lengths were directly measured prior
to the experiment andsegment masses were estimated based on the
body mass ofeach subject according to Harless’ ratios reported in
The EMG data of 6 muscles used in activities of dailyliving: the
biceps brachii (BB), lateral triceps (LT), posteriordeltoid (PD),
anterior deltoid (AD), pectoralis major (PM)and upper trapezius
(UT) were recorded simultaneously bypre-amplified EMG surface
electrodes (Cometa, Bareggio,
Italy) at 2000Hz. All electrodes were placed in correspon-dence
to  except for the PM which was placed centrallyin the
sternocostalis area .
The posture of the subjects was measured using TrakStarmagnetic
sensors (Ascension Technologies, USA), whichwere used to measure
the orientations of the upper arm andforearm. It was assumed that
the subject remained seatedupright throughout the experiment.
The protocol investigated both static and dynamic con-ditions.
At the start of each session, the EMG sensorswere placed on the 6
areas of interest. The MaximumVoluntary Contraction (MVC) of each
muscle was measured.Following this, the subject was seated,
attached to the EMUrobot and magnetic sensors used to measure the
subject’sposture were attached. Data were captured for the static
anddynamic conditions as follows:
1) Static Condition: The static condition tested the abil-ity of
the device to reduce muscle activity when a subject’sarm was not
moving. Four postures were tested, as detailedin Table I, and
visualised in Figure 2. Postures 1 to 3 werechosen as they
demonstrate progressively increasing amountof joint torques to
compensate for the gravity of the arm— through progressive
increases of both the shoulder ele-vation and elbow extension —
whilst remaining functionalpostures. Posture 4 was chosen
explicitly to investigate theeffects of the unactuated wrist joint
of the EMU robot — that is the robot cannot produce a torque
around theswivel angle .
TABLE I: Static Postures
Posture q1 q2 q3 q4P1 10 20 0 10P2 10 40 0 90P3 10 80 0 30P4 25
80 75 45
*Angles are reported in degrees, with reference to Figure 1
For each posture, the subject’s arm was positioned basedon the
measurement feedback given on a computer monitor,as measured by the
magnetic sensors. The subject wasconsidered ‘in posture’ if the
angle error of each joint wasless than 5◦.
Once the subject was at the desired posture, the roboticdevice
was set one of two modes — the Transparent mode,where no force was
applied onto the human subject’s armand the Deweighting mode, with
the deweighting algorithmactivated. In each mode, the subject was
asked to maintainthat posture and the EMG data were measured for
10seconds, although the subject was not made aware of thestart of
the recording until the data capture had finished.
2) Dynamic Condition: Two movements were studied.The first
movement — the “reaching movement” originatedwith the hand at the
subject’s upper thigh in a seatedposition, with a physical target
placed at 80% of the subject’smaximum reach, directly forward, and
at shoulder height.
Fig. 2: The four postures used in the Static Condition
The second movement — the “nose touch movement” —originated with
the hand at the subject’s knee, and endedat the subject’s nose.
These movements were chosen due totheir functional relevance.
A metronome was set to one beat every 1.5 seconds, andthe
subjects were asked to move back and forward along themovement path
every 1.5 seconds (i.e. 1.5 seconds frominitial to final position
and 1.5 seconds to return to theinitial position). This was
repeated for both “transparent”and ”deweighted” robot conditions,
with EMG data capturedfor 3 complete cycles — once again, the
subjects were notadvised of when this recording had started.
The EMG signals of all muscles were filtered witha 2nd order
Butterworth band-pass filter at 20Hz-400Hz.Following this, the
static and dynamic conditions wereprocessed through the following
1) Static Condition: For each subject s, posture p,condition c
(transparent and deweighting) and musclegroup m, the Root Mean
Square (RMS) value of a3 seconds period starting at 2.5 seconds was
computed(RMS(es,c,p,m)) and normalised against the
subject-specificMVC (RMS(MVCs,m)) as follows:
2) Dynamic Condition: The captured data for eachmovement was
segmented by manually identifying the timeat which the subject
first started moving (based on thelocation of the wrist as recorded
by the robotic device). Thefollowing 1.5 seconds was taken as the
forward motion, andthe subsequent 1.5 seconds as the backwards
The analysis was conducted in a similar way to the
staticcondition — a normalised RMS value over the duration ofthe
movement o. For this analysis, the forward and returndirections (d)
were analysed separately:
It is noted that due to the limited number of subjects(five), no
results were statistically significant according to
the Paired Wilcoxon Signed-Rank Test (p < 0.05). As such,the
results are presented and discussed as trends.
A. Static Condition
The percentage of MVC for each posture and eachcondition
(statics,c,p,m), for all subjects, are presented inFigure 3. It can
be observed that in all postures in the
Trans. DW Trans. DW0
Trans. DW Trans. DW Trans. DW Trans. DW0
BB LT AD PD PM UT
Fig. 3: Static Condition Results: Normalised RMS EMG ineach
posture, for biceps brachii (BB), lateral triceps (LT),posterior
deltoid (PD), anteriordeltoid (AD), pectoralis major(PM), and upper
trapezius (UT), under Transparent andDeweighting conditions for all
transparent condition, the AD had the largest %MVC
value,indicating the importance of this muscle in sustaining thearm
against gravity. It is also noted that the utilisation of
this muscle is decreased in all postures by the deweighting.This
was also the case for the UT, to a smaller extent.
The muscles most significantly contributing to the con-trol of
the elbow — BB and LT — both demonstrated onlya small percentage of
utilisation in all postures and thedata indicated that activation
of the biceps decreased in allpostures.
B. Dynamic Condition
The normalised overall muscle recruitment can be seen inFigure
4. Movements against gravity saw a large decrease inthe recruitment
of muscles, which operate against gravity inthese movements —
particularly when the motions were alsoagainst gravity. However, it
is also noted that some muscleswhich operate in the same direction
as gravity — LT andPD — also increased their activations in these
Trans. DW Trans. DW0
Trans. DW Trans. DW Trans. DW Trans. DW0
BB LT AD PD PM UT
Fig. 4: Dynamic Condition Results: Normalised RMS EMGfor for all
subjects for biceps brachii (BB), lateral triceps(LT), posterior
deltoid (PD), anteriordeltoid (AD), pectoralismajor (PM), and upper
trapezius (UT), for each movementand direction, under Transparent
and Deweighting condi-tions
A. Deweighting with End-Effector Based Devices
The results suggest that end-effector based devices arecapable
of compensating for the effects of gravity at both
the elbow and the shoulder — as demonstrated by thereduction in
muscle activity in muscles normally recruitedto compensate for the
effects of gravity (the biceps brachii,upper trapezius, and
anterior deltoid). This indicates thatend-effector based devices
are capable of providing somedeweighting of the arm, and is of
interest particularly whencomparing such devices with exoskeletons
and multi-contactsling based devices — which by construction are
capable ofproviding this support.
B. Muscle Recruitment Changes
It is also important to note that for both the staticand dynamic
conditions, muscle activation change is notuniform over all
muscles. In particular, although activationsof the biceps brachii,
anterior deltoid and upper trapeziusare decreased across postures
and movements, activationsof the other muscles are maintained, or
even increased insome postures and movements. Most notably
activations ofthe lateral triceps and posterior deltoid were
increased inalmost all postures and movements. This indicates
mus-cle co-contraction, which leads to increased subject
jointimpedance , possibly due to a reaction of the subject
tothe unfamiliar environment. Moreover this increase is
morepronounced in movements in the direction of gravity (i.e.down).
This can be explained by the fact that subjects haveto counteract
the gravity support provided, which does notexist in transparent —
and everyday — conditions. Thissuggests that, in order to provide a
deweighting suitablefor movements in and against gravity, specific
compensa-tion strategies, relative to movement direction should
C. Implications for Neurorehabilitation
Deweighting is a commonly-employed technique
forneurorehabilitation, and typically allows a patient to
engagetheir limited muscle activity in movement of their
limb.However, it is important to consider the overall objectiveof
neurorehabilitation — to reconstruct the neuromotor loopto enable
patients to use their limbs in everyday life, that is,without the
assistance of machine or therapist.
This study suggests that, with an end-effector baseddevice — but
also more generally with rehabilitation robotsand even
therapist-supported deweighting — muscle recruit-ment may change
besides a simple reduction. As such, pos-sible negative effects of
these changes must be balanced withthe obvious benefits of
providing deweighting. These ef-fects should also be further
investigated with neurologicallydisabled subjects who present
different muscle recruitmentpatterns.
This study investigated the effects of a deweightingstrategy for
a 3D end-effector based rehabilitation devicefor the upper limb on
muscle recruitment in both staticand dynamic conditions. The
results indicate a decrease inthe muscle activity for muscles which
typically act againstgravity at both elbow and shoulder, but also
suggest thatthere is an increased activity of the antagonist
can be explained both by the adaptation to an
unfamiliarenvironment by the subject as well as by the
deweightingstrategy, making movements in the same direction as
gravitymore difficult. Thus, care should be taken in the use
ofrobotic devices and deweighting in neurorehabilitation andfurther
experiments conducted, to ensure that such differ-ences are in the
interests of the patients’ recovery.
This work is supported by the ARC Discovery
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