Proceedings of the 2013 IEEE/SICE International
Symposium on System Integration, Kobe International
Conference Center, Kobe, Japan, December 15-17,

SP2-I.5

Wearable Dummy to Simulate Joint Impairment:
Severity-based Assessment of Simulated Spasticity of Knee Joint
Shun Ishikawa, Shogo Okamoto, Kaoru Isogai, Yasuhiro Akiyama, Naomi Yanagihara, and Yoji Yamada
Abstract— Physical therapists master manual examination
techniques for testing impaired motor functions. We used awearable robotic dummy joint that simulated disordered joint
resistances to help physical therapists learn such techniques.
This study developed a resistance model for a spasticity joint,
and the dummy joint was used to present it. We assessed
the simulated spasticity model using Modified Ashworth Scale(MAS), which is an evaluation criterion for spasticity seriousness that is widely used by physical therapists. The resultsof experiments involving two physical therapists showed thatthe model accurately expressed mild-to-severe symptoms ofknee joint spasticity. It is expected that using the system in
educational institutions for physical therapists will help students
learn the typical levels of joint resistance caused by spasticity
with different degrees of severity.

I. INTRODUCTION
Appropriate rehabilitation not only improves a patient’s
quality of life but also reduces the cost of medical careby facilitating the early recovery of the patient. Physicaltherapists assume an important role in the rehabilitation ofpatients, assisting them to recover impaired motor functions
caused by diseases such as stroke and arthrosis. One of
their most important skills is a technique called manual
examination, which is used to determine the pathology
and the sites to be treated. When performing a manual
examination, a physical therapist moves and palpates apatient’s diseased part, and determines the level of pain andneurological symptoms. Physical therapists flex and extend
the joints of patients to classify the degree of decreased
ability to exercise and the patient’s recovery. This processdepends on the reaction force patterns, the joint’s range of
motion, and the patient’s complaints. To learn this technique,students need to experience a variety of symptoms because
the reaction force pattern generated by a disordered joint
is significantly different for each symptom. However, they
do not normally have enough opportunities to experience avariety of symptoms. Furthermore, using students to provide
treatment is considered ethically problematic because they
do not have the qualifications of a physical therapist, evenduring internship training.
To resolve these problems, the development of training
robots that help to train physical therapists has recently beenThis work was in part supported by the Naito Science and Engineering
Foundation.
S. Ishikawa, S. Okamoto, K. Isogai, Y. Akiyama, N. Yanagihara, and Y.
Yamada are with the Department of Mechanical Science and Engineering,
Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku,
Nagoya, Japan.
K. Isogai is with the Department of Physical Therapy, Faculty of Healthand Medical, Tokoha University, 1230, Miyakoda-cho Kita-ku, Hamamatsu,
Japan.

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started. Masutani et al. developed a robot that simulates
impaired knee joint motion. They simulated the limited
motion of an impaired joint, a jackknife phenomenon, alongwith the rigidity of the muscles and tendons [1]. Kikuchi etal. controlled the knee joint of a patient robot using an MR
clutch and DC motor to simulate the high and dynamic forcesof disordered joints [2]. Grow et al. compared two types ofmodels to simulate the spasticity of an elbow joint [3]. They
attempted to prove that the robot was effective at training
students to classify the spasticity.
Some researchers have attempted to realize complex human joint behaviors, which are intrinsically different from the
movements made by the uniaxial joints of robots. Takahashi
et al. simulated the stretching and shortening of a forearm
associated with the extension and flexion of the upper limb
by using an oval cam in the joint [4]. Furthermore, a training
robot that presented forces in six directions was reported [5].
Morita et al. developed a knee robot that had a rotational
joint as well as a joint for extension and flexion [6]. On theother hand, as described in Sec. II, the authors are developing
wearable dummy joints to realize a high degree of realism
while avoiding the difficulties associated with using a robot
to simulate complex human joints. In this framework, an
exoskeletal robotic joint is worn by a healthy human, and it
simulates the joint resistances caused by motor impairment.
Thus far, wearable knee [7] and ankle joint simulators [8]have been prototyped.
The objective of this study was to develop a joint resistance model for spasticity that could be used for patient
simulators and to assess the simulated symptoms quantitatively. Spasticity is often caused by stroke, as described inSec. III-A. Although prior studies have demonstrated thejoint resistances associated with spasticity, they have notquantitatively assessed their simulated symptoms in relation
to their similarity to those of real patients. This may be
because a comparison between the simulated resistances andthose obtained from real patients is not always meaningful.
The joint resistances of real patients are highly individual,
whereas training simulators have to present characteristic or
typical joint resistances that can be used as the bases forclassifying the symptoms. Such typical symptoms are morevaluable for assisting trainees to learn examination techniques. To this end, we used the Modified Ashworth Scale(MAS) [9], which is widely used by medical professionals,
including physicians and physical therapists to classify theseverity of spasticity on the basis of generalized characteristic
joint resistances. As described in Sec. III-A, the severity isevaluated in six stages by following multiple criteria. In this300

Passive exercise

PT trainee

Cuff

Simulated resistance

Link
Healthy person
Fig. 1.

Motor and encoder

Conceptual diagram of wearable dummy joint

Cuff

Link
Fig. 2.

DC motor

Wearable dummy joint to simulate knee-related disorders

study, the developed spasticity model was assessed using the
MAS to examine whether the model could present a wide
variety of severity levels.

of the skin, and conversation with patients. The robot must
consider all of these issues.
Fig. 1 shows a conceptual rendering of the dummy knee
joint designed to be worn by a healthy student. At educational sites, students practice examination techniques in pairs.
One student assumes the role of the patient, and the otherpractices the manual examinations. The wearable dummy
joint was designed to assist in these actual practices. The
student who plays the patient wears the dummy joint. Then,the other student grasps their lower thigh and flexes andextends the knee joint. At that time, the “patient” does notvoluntarily move but relaxes their muscles, as in an actual
manual examination. When the knee joint is moved, theactuator mounted on the dummy applies a resistance force tothe lower thigh. The force depends on the simulated symptom
and movement of the dummy joint. Therefore, the dummy
joint mimics the behavior of a muscle tone abnormality of
the knee joint and a disordered knee joint. The learner canpractice the manual examination with various postures suchas prone and sitting positions, as shown in Figs. 1 and 2,
respectively.
Our dummy joints do not have the convolution, stretchingand shortening mechanisms possessed by a human knee joint.
However, these mechanical differences between a humanjoint and the dummy joint can be absorbed by human fat or
clothes. Therefore, the dummy joint does not prevent thesehuman joint motions. Hence, the trainee can experience theminor rotations and slides inherently possessed by humanknee joints. Furthermore, unlike previous dummy robots,human-to-human communication is maintained because thetrainee touches a real human leg.
Fig. 2 shows a developed dummy joint. This dummy joint
attached to the thigh of a healthy human using two cuffs,
with one cuff on the lower leg. As an actuator, a DC motor(RE35, Maxon, stalling torque 949 mN·m), gearhead (1/86),
and motor driver (Maxon, 4-Q-DC servo amplifier ADS
50/5, current control mode) were used. The kinetic frictionof the gear system was specified as 0.5 N · m, and it wascompensated for during the later experiments.
III. SIMULATED SPASTICITY
A. SPASTICITY & MODIFIED ASHWORTH SCALE

II. WEARABLE DUMMY JOINT
We use a wearable dummy robot to simulate the resistances of impaired joints. There are some technical issues toovercome when developing our training robot for rehabilitation. The first issue involves accurately simulating a variety
of symptoms. For example, the robot is required to simulate acomplex resistance force pattern and the large reaction force
caused by contracture. The robot also needs to simulate thetrajectory of complex human-joint motions. For example, ahuman knee joint stretches with extension. Thus, a humanjoint has a complex trajectory caused by various motions
such as the rotation, stretching and shortening associatedwith flexion and extension. Finally, human-like characteristics are necessary, including the overall appearance, texture978-1-4799-2625-1/13/$31.00 ©2013 IEEE

Spasticity is a well-known syndrome, most commonly
arising after stroke, multiple sclerosis, spinal cord injury,
some traumatic brain injuries, and other central nervous
system lesions [10]. It is a motor disorder characterized
by a velocity-dependent increase in the tonic stretch reflex
with exaggerated tendon jerks; the disorder results fromhyperexcitability of the stretch reflex, and it is one component of the upper motor neuron syndrome [11], [12].
A study showed that approximately 40% of patients withinitial central paresis developed spasticity within six monthsof suffering a stroke [13]. Disability due to spasticity is asignificant health issue for stroke patients. Physical therapists
need to precisely understand the severity of spasticity to treat
the disability.

301

TABLE I
M ODIFIED A SHWORTH S CALES AS DESCRIBED IN SOURCE ARTICLE [9]
Score
0
1
1+
2
3
4

Modified Ashworth Scale
No increase in muscle tone
Slight increase in muscle tone, manifested by a catch and release or by minimal resistance at theend of the range of motion when the affected part(s) is moved in flexion or extension
Slight increase in muscle tone, manifested by a catch, followed by minimal resistance throughout
the remainder (less than half) of the range of movement (ROM)More marked increase in muscle tone through most of the ROM, but affected part(s) easily moved
Considerable increase in muscle tone, passive movement difficult
Affected part(s) rigid in flexion or extension

As listed in Table I, the Modified Ashworth Scale (MAS)classifies the severity of spasticity into six levels according to
the presented symptoms [9]. The MAS provides evaluation
criteria for use in clinical settings. These are based on theresistances that physical therapists experience when they
passively move patients’ joints. At level 1, there is mild
muscle tone and the presence of a catch and release if
the diseased part is moved passively. Additionally, a slightresistance manifests at the end of the range of motion.
When the severity increases to level 2, there is a marked
increase in muscle tone through most of the range of motion.
However, the affected part can be easily moved. At level 4,
the joint is rigid, and the affected part cannot be moved.
Medical professionals are in good agreement about these
MAS judgments [14].
As described in Table I, it is not only the tonic reflexes
but also the limited range of joint motion and the rigidityof the muscles and tendons that provide information about
the degree of spasticity. For practical purposes, limits forthe range of motion and the rigidity of the muscles andtendons are significant criteria when evaluating the extent
of the symptoms. Our dummy joint needed to collectively
simulate these criteria.
B. SIMULATED SPASTICITY
Using the MAS, physical therapists classify spasticity on
the basis of the degree of muscle tone and the resistance atthe end of the range of motion. In addition to joint tonic
reflex, our model of spasticity includes resistance due tomuscle tone and resistance at the end of the range of motion.
Physical therapists commonly apply the MAS on the basis ofthese symptoms. The following model is based on a uniaxial
joint, however, as we stated before, this does not restricta human joint’s motion with multiple degrees of freedom.
Subtle rotation or stretching of the human knee accompaniedwith its flexion are mostly realized because the compliance ofthe skin or clothes beneath the cuff absorbs minor differencesin mechanical structures between the dummy and humanlimb. We also assume that the knee joint of relaxed healthy
wearer is of little friction and its biomechanical factors
yield little resistance with the exception being at the endof the motion range where natural impedance of humanbody is significant. The following impairment model imitates
joint resistances mostly at the range where the healthy kneesmoothly moves.

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Typically, tonic reflexes are defined as resistances characterized by a velocity-dependent increase [11], [12]. Thus,
they are expressed as viscosity resistances against joint
angular velocity. The resistance torque due to the tonic
reflex is constant until an angular velocity ω0 . When the
angular velocity of the knee joint reaches ω0 , the torqueincreases in proportion to the angular velocity. Denoting
the joint angle by θ(t) and using a constant torque τ0 andcoefficient of viscosity c, the angular velocity-dependent
resistance torque τc (t) is expressed as{
τ0 + c(|θ̇(t)| − ω0 ) (|θ̇(t)| > ω0 )(1)
τc (t) =τ0(|θ̇(t)| ≤ ω0 ).
Here, τ0 indicates the constant resistance present throughout
the range of motion, i.e., rigidity.
According to the MAS, a limit for the range of motion
manifests from level 1. At level 1+, there is a resistance overnearly half of the range of motion. When the knee joint’s
range of motion is 0 < θ(t) < θmax (extension position is0), using modulus of elasticity k, the resistance torque at theend of the range of motion τe (t) is expressed as(0 ≤ θ(t) < θe )−k(θ(t) − θe )τe (t) = 0(θe ≤ θ(t) < θmax − θe )k(θ(t) − θmax + θe ) (θmax − θe ≤ θ(t) ≤ θmax ).

(2)Here, θe denotes the angle at which the joint motion starts
being limited and depends on the severity of spasticity.
The resistance torque around the knee joint due to spasticity τ (t) is expressed as
τ (t)

=

τc (t) + τe (t).

(3)

In this model, the parameters characterized by the severityare τ0 , ω0 , c, θe , and k. We specified these parameterswith the assistance of a physical therapist on the basis ofa characteristic resistance for each level and the joint angle
at which the resistance began to increase.
IV. EXPERIMENT
Two experienced physical therapists were invited to manually assess the severity of simulated spasticity using theMAS. The objective of this experiment was to determine
whether the developed model and wearable dummy joint
could present the six graded severity levels of the MAS.
One goal of our research is to develop a patient dummythat can be used to assist a physical therapy student to learn

302

Modified Ashworth Scale

4

Assessor 1
Assessor 2

3
2
1+
1
0
1

2

3

4

5

6

7

8

9 10

Simulated spasticity level (stimulus number)
Fig. 3.

Example of experimental setup

Fig. 4.

manual examination techniques, including the judgment of
the severity of spasticity. Such an ability to accurately assess
the spasticity in joints is of crucial importance not only in thepractice of providing proper care but also for communication
among therapists. This experiment was part of a pilot study
to establish patient simulators for the accurate classificationof spasticity.
A. TASKA single healthy person wore the dummy knee joint on his
right leg and lay down in a relaxed manner. The assessor held
and moved the wearer’s leg and experienced joint resistancetorques caused by the passive extension of the knee joint(Fig. 3). The assessor was allowed to freely test the joint
torques within the limited time, i.e. 1 min/trial. The assessor
was told that the dummy joint simulated the knee joint of aspasticity patient with disordered flexor or extensor muscles.
The judgment was made on the basis of the six MAS levels
listed in Table I.
B. PARAMETER SETS OF SIMULATED SPASTICITYAs previously described, the simulated spasticity was characterized by multiple parameters. For a simulated symptom
with the highest severity, τ0 , ω0 , c, θe , and k were set
to 3.0 N · m, 0 rad/s, 9/π Nm · s/rad, 16π/45 rad, and54/5π N · m/rad, respectively. For a simulated symptom
with the lowest severity, i.e., a healthy knee, these parameters were 0 N · m, π/2 rad/s, 0 Nm · s/rad, 0 rad, and
0 N · m/rad, respectively. We used 10 stimulus levels whose
individual parameters were linearly changed between theabove maximum and minimum values, as listed in Table II.
The stimuli with the lowest and highest parameter sets werereferred to as stimuli 1 and 10, respectively. The assessor
was presented with 10 stimuli in a random order, with eachbeing presented only once.

Only stimulus 1 (no simulated resistances) was classified
as level 0, whereas the other stimuli were judged to be
impaired. Stimuli 2, 3, and 4 were classified as level 1 or 1+,
indicating moderate severity. Stimuli 5–7 were judged to belevel 2, and level 3 was assigned to stimuli 8 and 10. As ageneral trend, the MAS levels increased as the severity of thesimulated symptoms rose, with the exception of stimulus 9.
It is speculated that this exception was observed because of
the aftereffects of the previous stimulus.
Level 4 was not assigned to any simulated symptoms.
Level 4 is the most severe class, at which the knee joint canhardly be moved. Thus, to simulate this level, the simulator
simply needs to exert torques greater than those used in theexperiment.
In the results of assessor 2, the simulated symptoms wereclassified as MAS levels 0–2. Though, the trend of theirresults was similar to that for assessor 1.
According to the introspective reports of the assessors, thelimitations of the range of knee motion played an effectiverole in their ratings for the symptoms. Such information
was especially used of as a judgment criterion between
levels 2 and 3. The simulated spasticity stimuli with significant perceived joint motion limitations were classified
as level 3. In contrast, the velocity-dependent resistancesmight become a cue to judge moderate classes. The effects of other parameters of the simulated spasticity on the
MAS assessments were intriguing and should be clarified.
Nonetheless, from these results, it is highly likely that tuning
the parameters of our spasticity model will make it possible
to represent knee spasticity with various degrees of severity.
Although just two assessors were invited to participate inthis experiment, if similar results are reported from multiple
assessors, the consistency between the simulated symptoms
and their clinical experiences would be confirmed.
V. DISCUSSION

C. RESULTS
Fig. 4 shows the MAS levels assigned to each simulated
symptom in the experiment. The simulated symptoms wereclassified as MAS levels 0–3 in the results of assessor 1.

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Modified Ashworth Scale assigned to simulated knee spasticity

The universal goal of a spasticity simulator is to present
resistive torques caused by general spasticity joints. Whereas
our method is model-based, some may consider that an

303

TABLE II
PARAMETER SETS OF SIMULATED SPASTICITY

τ0
ω0
c
θe
k

1
0
π/2
0
0
0

2
0.3
4π/9
1/π
16π/405
6/5π

3
0.7
7π/18
2/π
32π/405
12/5π

4
1.0
π/3
3/π
16π/135
18/5π

Stimulus number
5
6
1.3
1.7
5π/18
2π/9
4/π
5/π
64π/405 16π/81
24/5π
6/π

approach based on recording and precisely reproducing actual values might be more appropriate for presenting thejoint resistances of patients. In the latter approach, the joint
resistance torques of real patients are recorded and presented
as precisely as possible. However, for training purposes, suchan approach involves several flaws.
First, the data from actual patients encompass substantial
individual differences. The representation of such differenceswith high fidelity is not valuable for patient simulators,
through which trainees are expected to experience general
symptoms.
Second, patients rarely develop single symptoms. Thosewith motor impairments suffer from multiple symptoms.
Hence, the resistance torques recorded from patients arecollectively caused by co-occurring or mixed symptoms. In
the educational environment, trainees need to study how todiscriminate or specify symptoms. The patient dummies are
required to simulate individual symptoms independently. Our
spasticity model tunes the severity of respective symptoms.
This allows the trainers to synthesize simulated symptoms
according to the educational stage. For example, they can
exaggerate the influences of certain symptoms and mix orseparate the resistance torques caused by multiple symptoms.
For these reasons, in the case of patient dummies for training purposes, model-based simulations are more effective
than the precise representation of recorded data.
VI. CONCLUSION
This article developed a simulated spasticity model for the
robotic patient dummies that are used for training physicaltherapists. The model produced representative joint resistance torques observed when the therapist manually tested
knee joints with spasticity. In addition to the resistances
caused by the tonic reflex, which is considered to be atypical symptom of spasticity, those from constant muscle
tone and a limit of joint motion range were also simulated.
Although these symptoms are collaterally developed withspasticity, early studies on simulated spasticity did not focus
on the combination of these symptoms. Thus far, someearly studies have simulated the tonic reflex using robotic
dummies [2], [3]. However, the simulated symptoms orresistance torques have yet to be assessed in terms of theirsimilarity to those of real patients. The MAS, which is aseverity scale for spasticity based on the haptic sensations
perceived by physicians or therapists, allowed us to validate
the spasticity model in accordance with the experience of
medical professionals.

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7
2.0
π/6
6/π
32π/135
36/5π

8
2.3
π/9
7/π
112π/405
42/5π

9
2.7
π/18
8/π
128π/405
48/5π

10
3.0
0
9/π
16π/4554/5π

We implemented the spasticity model using a wearable
dummy joint that enabled us to simulate the behavior of
impaired joints while maintaining good human likelihood.
Two physical therapists assessed our simulated spasticity
levels, and the results indicated that the simulated model wascapable of presenting symptoms covering levels 0 to 3 of theMAS. In the wake of this feasibility study, we will continue
to improve the spasticity model on the basis of assessmentsfrom additional therapists.
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