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Patient Simulator Using Wearable Robot to Estimate
the Burden of Knee-Osteoarthritis Patients during
Sitting-down and Standing-up Motions
Naomi Yamada

Yasuhiro Akiyama

Yoji Yamada

Nagoya University Nagoya University Nagoya University Nagoya University
Nagoya, Japan
Nagoya, Japan
Nagoya, Japan
Nagoya, Japan

Ryu Kubo

Ayaka Hirukawa

Shogo Okamoto

Nagoya University
Nagoya, Japan

Nagoya University
Nagoya, Japan

Abstract—The estimation of the physical burdens from whichpeople with motor impairment suffer helps us establish welfare
techniques comprising personal care equipment and assessmentof critical risks, such as fall risks. However, the involvementof actual patients in the evaluation and development of thisequipment is costly and involves the exposure of patients to longand exhausting experiments. To solve this problem, we developeda robot wearable by a healthy person and the associated control
algorithm to simulate typical motions of patients with knee
osteoarthritis, which is a common symptom for the elderly
and causes pain during movement. To estimate the physicalburdens inflicted by knee malfunctions, we computed the knee
flexion and extension moment of the simulated patient during thestanding-up and sitting-down motions. The moments, estimatedunder certain conditions, are qualitatively consistent with thoseconsidered clinically, which corroborates the validity of ourpatient simulation techniques.

I.

I NTRODUCTION

Recently, the development of personal care equipment
and promotion of impediment removal at public facilities
have become essential. For the fulfillment of the functional
requirements of these objectives, it is necessary to estimate
the burdens of people with motion impairments. However, theparticipation of impaired people in experiments that estimate
their burdens or risks, during which they may suffer various
degrees of danger or pain, is ethically unacceptable. Althoughthese experiments are important to ensure the safety of patients,
the subjects are likely to face significant dangers, especially inexperiments involving hazardous situations that may cause afall. Therefore, experiments in hazardous and painful situations
can not be conducted by an impaired person.
This paper reports on the development of a patient simulator that provides a framework to simulate the motions of an impaired person using a healthy person [1], to engage simulated
patients in various experiments, and to estimate the burdens
or risks of the impaired while avoiding ethical problems andrisks. Experiments conducted by a healthy person instead ofa real patient can lower the subject’s risks to an acceptablelevel in various cases. Typically, risk assessment and evaluation
experiments in the preliminary step of the development of
personal care equipment are conducted by healthy subjects.
However, these tests provide only limited data because of
the large differences between the motor functions of healthy
and impaired people. The introduction of our simulator tothese experiments removes or alleviates these limitations andcontributes to the development of personal care equipment.

978-1-5090-1897-0/16/$31.00 ©2016 IEEE

Thus far, some research groups have reported that it
is useful for healthy people to experience impaired motor
functions. Wood and Verkey et al. [2], [3] reported on aworkshop in which medical students experienced the motor
functional difficulties of aging people by using several orthoses
simulating their movements. Ullauri et al. [4] attempted tosimulate the elderly’s gait by restraining the muscle activity at
the lower thighs using a taping technique. Many researchers
conducted studies related to experiences of impaired motor
functions that have contributed to the improvement of patients’quality of life [5]–[8]. Several companies (for example Koken
Co., Ltd, Japan, and Sanwa Manufacturing Co., Ltd, Japan)
sell aging simulators with springs and weights; however, theseproducts are intended for enable healthy people to experience
the inconvenience that patients feel and not to estimate theburdens that patients may face.
Some researchers have developed robots to simulate themotions of people with motion functional disabilities. Huang
et al. [9] developed a robotic patient with actuators in bothof shoulder joints for the training of medical students inthe transfer of patients between a wheelchair and a bed.
Ishilawa et al. [10], [11] constructed a robot that was wearable
on a knee joint for the training of physical therapists incare and examination procedures. However, these studies did
not consider situations in which patients actively move; forexample, walking, standing up from or sitting down on a chair,and climbing stairs.
Several researchers have investigated the motions of theimpaired and reported physical quantities related with thephysical burdens incurred by the patients Mak et al. [12]compared the standing-up and sitting-down motions of healthy
subjects and of Parkinson’s disease patients and suggested thatthe patients’ slow movements were caused by the reduction of
the hip flexion and extension moment. Astephen et al. [13]analyzed the gait motions of patients with knee osteoarthritis (knee-OA) of different severities and proposed a relation
between the severity of the symptom and joint moments during
a gait. Anan et al. [14] reported the inefficiency of knee-OA
patients’ motions in terms of physical energy. These studies
examined cases of real patients but not simulations of theimpaired.
The purpose of the present study is the estimation of thephysical burdens that impaired people experience in their daily
lives by using our simulated patient that has been reported in
the previous article [1]. We verified the proposed method byexperiments involving standing up from and sitting down on a

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2016 IEEE International Conference on Systems, Man, and Cybernetics • SMC 2016 | October 9-12, 2016 • Budapest, Hungary

chair. The standing-up and sitting-down motions are common
motions in daily life and are easily influenced by a motor
impairment.

GoniometerIn our experiment, a healthy person wearing an exoskeletal
knee robot performed sitting-down and standing-up motions
under different conditions, such as with or without a simulated
impairment, different chair heights, and the use of a supportive
hand guide. Subsequently, we compared the physical burdens
evaluated during the simulated motions for all different conditions and examined their consistency with those consideredin clinical settings to validate our physical burden estimation
approach. This simulator can be applied to the evaluation of
personal care equipment in its early development phase and toassess potential risks caused by the malfunction and ill designof this equipment.
II.

K NEE OSTEOARTHRITISWe selected knee-OA as our target disease because of its
frequent occurrence and severe effects, which obstruct thepatient’s motions in daily life. According to a report of the
Ministry of Health, Labor, and Welfare [15], the number ofknee-OA patients in Japan, including those with underlying
symptoms, was approximately 30 million.
Knee-OA develops with age and is generally accompanied
by damage to the bone spurs, cartilages, and meniscus [16].
Patients suffer from symptoms such as a limited motion
range, pain during motion, joint deformation, and inflammation [16], [17]. Patients with moderate knee-OA experience alittle pain that scarcely obstructs motions like stair climbing.
Intermediate-severity patients suffer from pain when moving
their knee joints, especially when large loads are applied tothe joints. The motions of these patients differ from thoseof healthy persons, as the patients tend to avoid painful
movements. Severe-knee-OA patients experience difficulties intheir daily activities because of significant and frequent pain.
In this research, we focused on intermediate-knee-OA patients.
In particular, the following conditions were considered: OA
develops in the right knee. The apparent motions of patientsare distinct from those of healthy people. Finally, the patients
do not need supportive instruments, such as hand rail or a cane.
III.

E XPERIMENTAL EQUIPMENT

As shown in Fig. 1, a healthy adult wore a wearable
exoskeletal knee robot on the right leg by two plastic braces
for the lower and femoral thigh. We installed a DC motor (RE-35, Maxon Motor, Netherlands, 90 W, continuous
maximal torque 97.2 mN·m) with a reduction gear (GP32HP,
Maxon Motor, 1/86) and an encoder (MR Type L, Maxon
Motor, 1024 ppr) in the knee joint. This motor was driven bya current driver (4-Q-DC ADS50/5, Maxon Motor) througha computer control unit at 5 kHz. A three-axial accelerometer (KXM1050, Kionics, America) was attached on the middle
point of the lower link of the robot. A potentiometer-basedgoniometer was installed on the left knee using Velcro tapes.
The interaction forces between the ground and the rightfoot were measured by a shoe in which three three-axial
force sensors (USL08-H18-1kN-AP, Tec Gihan, Japan) were
mounted. The sensors were placed on the heel and the thenar
and antithenar eminences. These sensors were appropriately
leveled, such that they covered the load paths between thehuman and the shoe sole.

Exoskeletal knee
robot with two links
and an actuator

Fig. 1: Exoskeletal knee robot worn by healthy subject (rightleg) and goniometer (left leg, hidden). [1]IV.

M ETHOD OF PATIENT SIMULATIONBY A HEALTHY PERSON

We adopted the method developed in our previous study [1]to simulate typical motions of knee-OA patients. In thismethod, an exoskeletal knee robot leads the wearer throughmotions modeled after typical impaired motions.
First, we determined the representative movements of kneeOA patients using the literature [18] and observations of aphysical therapist (N.Y., one of the authors of this article).
Secondly, a healthy adult performed these representative movements as a model motion under the supervision of N.Y. Since
actual knee-OA patients generally develop other diseases, ourapproach aided us to focus on representative behaviours causedby knee-OA. While performing this motion, he remained his
feet on the ground. Fig. 2 shows both knee angles and the footload of the impaired side of the model motion. The impaired
knee shows a greater tendency to extend than the healthy knee
and the foot load of the impaired side decreases during thestanding-up and sitting-down motions in the model motion.
These typically impaired motions develop for avoiding thepossible pains on the impaired knee.
Fig. 3 shows samples (gray markers) of both knee angles
during the standing-up and sitting-down model motions performed by the healthy subject, which were repeated five times
for each. We formulated the desired angle of the impaired knee,
θref , as a quadratic function of the knee angle of the healthyside, θh , as follows:2θref (θh , t) = a2 θh (t) + a1 θh (t) + a0

(1)

where a2 , a1 , a0 are coefficients calculated by the least-squares
method. From this approximate equation, we can determine thereference angle of the impaired knee that corresponds to themeasured angle of the healthy knee at a certain moment.
We controlled the torque output of the exoskeletal robot
according to this equation:
τ (t) = Kpf (t)(θref (θh , t) − θi (t))
fmax

(2)

where f (t), fmax , and Kp are the total foot load of the

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2016 IEEE International Conference on Systems, Man, and Cybernetics • SMC 2016 | October 9-12, 2016 • Budapest, Hungary

Foot load (right)

Knee angle [deg]

500
100

Stand
up

0

0

2

4

Sit
down

6
8
Time [s]100Knee angle (impaired side) [deg]100
Right = Left
Approximate curve
Sample

60
40
20
0

0

ࢀ૛

Lower thigh

20
40
60
80
100
Knee angle (healthy side) [deg]Fig. 3: Knee angles during the model motions of sitting down
on and standing up from a chair with fixed feet positions. A
black curve is a quadratic function used to approximate thesamples. The dotted line corresponds to θref = θh .

impaired side, the weight of the wearer, and the proportional
gain, respectively. The output torque τ (t) is proportional tothe difference between the measured impaired-knee angle θi (t)and the reference one and guided the wearer’s motions to themodel motions.
We have verified this simulation method in a previous
study [1]. One healthy adult who had no background information on our apparatus repeated the standing-up and sittingdown motions under the guidance of the robot. We observed
several similarities between the motions of the subject andthose of the model; for example, an inclination of the upper
body, load reduction on the impaired knee, and a significant
extension of the impaired knee compared to the healthy knee.
These results indicate that our method allows us to simulate
the apparent motions of the patients by a healthy person using
the wearable robot.
E STIMATION OF THE PATIENT ’ S BURDEN BASED ON
THE KNEE EXTENSION AND FLEXION MOMENTS

Our simulator aims to estimate the patient’s physical burdens through experiments conducted by a simulated patient.

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࣓૛

࢞૛
࢝૛
ࢌ૚

ࢀ૚

ߠଵ

െࢀ૚

ࢌ૙

࣓૚

࢘૚ ࢘૙ ࢝૚

࢞૚

Foot

െࢌ૚

Fig. 4: Multibody diagram of a leg in the sagittal plane.

Earlier studies indicated that the adduction moment of theimpaired knee is correlated with subjective pain during motion [17]. Some researchers have analyzed the efficiency ofthe sit-to-stand motion based on the moment of the knee
joints [12], [19]. Hence, it is reasonable to consider that theflexion and extension moments of the knee, which are kinetic
strains, are related with subjective burdens experienced byknee-OA patients.
First, we describe the method used to calculate the moments. Fig. 4 shows a multibody diagram of a lower limb ona sagittal plane. We formulate the motion equation of the footas follows:f 0 − f 1 + w1 = m1 ẍ1
f 0 × r 0 + f 1 × r 1 − T 1 = I1 ω̇ 1

(3)
(4)

and that of the lower thigh as follows:f 1 − f 2 + w2 = m2 ẍ2
f 1 × r 2 + f 2 × r 3 + T 1 − T 2 = I2 ω̇ 2

(5)
(6)

where r0 , r1 , r2 , r3 are the position vector, T1 , T2 are themoment at each joint, f0 is the floor reaction force, and, f1 ,f2 are the joint force. w1 , w2 are the gravity force, ω1 , ω2
are the angular velocity of the center of gravity, x1 , x2 are
the position vector of the center of gravity, I1 , I1 are themoment of inertia, and m1 , m2 are the mass of each bodylink. We assume ẍ1 ≃ 0 and ω̇ 1 ≃ 0 because the subject’s feet
did not leave the ground and hardly moved from their initial
positions during the standing-up and sitting-down motions. We
also assume ẍ2 ≃ 0 because the acceleration of the lower limb
was significantly smaller than the other physical quantities.
Finally, using equations (3)–(6) we formulate the knee flexionand extension moments during the motions as follows:
T 2 =f 0 × r 0 + (f 0 + w1 ) × r 1 + (f 0 + w1 ) × r 2+ (f 0 + w1 + w2 ) × r 3 − I2 ω̇ 2 .
VI.

V.

െࢀ૛

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12

Fig. 2: Knee angles and foot load of model impaired motion
performed by a healthy adult without robot control.

80ࢌ૛Femoral thighFoot load (right foot) [N]Right-knee angle
Left-knee angle(7)

E XPERIMENT

A. Objectives
We conducted experiments to verify our method for estimating the burdens experienced by patients. Generally, kneeOA patients try to limit burdens on their impaired knees

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2016 IEEE International Conference on Systems, Man, and Cybernetics • SMC 2016 | October 9-12, 2016 • Budapest, Hungary

Higher chairthan otherexperiments

Hand guide(The subject puts ahand on the desk)

55 cm
40 cm

Fig. 5: Experimental scenes. Left: the case of a high chair (experiment 3). Right: the case of a hand guide (experiment 4).

because overloading the impaired knee causes pain. Additionally, physical therapists instruct patients on movements that
reduce loads on the impaired knee. We conducted standingup and sitting-down experiments under certain conditions,
namely, control motion by a healthy person, simulated impaired motion, and simulated impaired motion following the
instructions of a physical therapist. We then computed andanalyzed the flexion and extension moments of the knee foreach condition. We expected that the moments would be large
in order of the control motion, the simulated motion, andthe simulated motions following clinical instructions. If theburdens estimated under these conditions qualitatively match
those observed clinically, then it corroborates the validity ofour patient simulation technique.
B. Participants
The participant was one healthy man who had no background information on our apparatus and experimental objectives.
C. Tasks
We conducted experiments of standing-up and sitting-down
motions under the following conditions:
Experiment 1: Case of a healthy person
Experiment 2: Case of a simulated patient
Experiment 3: Case of a simulated patient with a high chair
Experiment 4: Case of a simulated patient with a hand guide.
The height of the high chair was 55 cm and that of the normal
chair was 40 cm. Fig. 5 shows the experimental environmentsof experiments 3 and 4 according to clinical settings.
D. Analysis
We analyzed the maximum and mean values of the flexionand extension moments during the examined motions using a ttest. We adjusted the significance level based on the Bonferroni
correction for comparing of experiments 2-3 and 2-4.

E. Results
The number of valid trials in experiments 1, 2, 3, and 4
were 10, 8, 8, and 8, respectively.
1) Apparent and representative features of motions undereach condition: Figs. 6–9 show the knee angles and the flexionand extension moments during the examined motions in a
representative trial. In experiments 2, 3, and 4, we observed
slower movements and a body inclination to the healthyside (not shown in the figures). Experiment 1 showed a fullknee extension in the standing position that was not observedin the other experiments. These results are consistent with thecharacteristics of knee-OA patients.
In experiment 1, the moments exhibited large values, whichwere approximately 20 N·m at the times when the participant’s
hip separated from (time tA ) and contacted to (time tB ) theseat. In experiment 2, the moments showed peaks at times tA
and tB , but the measured values were smaller than 10 N·m
and lower than those of experiment 1. In experiment 3, themoments did not showed a peak at times tA and tB , and theirvalues were lower than 10 N·m throughout the motions. In experiment 4, we observed small peaks of the moments at times
tA and tB and their values were lower than 10 N·m, similarly toexperiment 3. The simulated patient exhibited lower moments
than the healthy person, and the two cases (experiments 3
and 4) in which they followed clinical instructions showed
lower values than the other cases. These results reflect thecharacteristics of knee-OA patients and the clinical instructions
provided to reduce the burden on the impaired side.
2) Statistical analysis: Figs. 10 and 11 show the maximum
and mean values of the flexion and extension moments of theimpaired (right) knee in each condition. We compared thesevalues between experiments 1-2, 2-3, and 2-4 using t-tests with
the Bonferroni correction in order to test the differences of thephysical burdens due to the disease, the height of the chair, or
the hand guide.

2-1) Validity and effect of simulated impairment: We
compared experiments 1 and 2, which differed in terms of
the presence of the disease, to test the effect of our simulated
impairment. In experiment 1, both the maximum and meanvalues of the moments of the right (impaired) knee weresignificantly larger during the standing-up and sitting-down
motions than those in experiment 2 (p < 0.001). These results
indicated that the burden on the impaired knee was smaller forthe patient than for the healthy person. In general, knee-OA
patients lean their loads on the healthy side and reduce theburden applied to the impaired side. The experimental results
were in good agreement with this general trend of knee-OA
patients and confirmed that our simulated patient developed
one of the typical characteristics of actual knee-OA patients.
2-2) Effects of chair-height and a hand guide on burdens:
By employing our simulated patient, we confirmed that theknee flexion and extension moments decrease when following
clinical instructions for knee-OA patients.
First, we compared experiments 2 and 3, in which thechairs of 40 and 55 cm height were used, respectively. The
maximum moment of the impaired knee was significantly
larger in experiment 2 than in experiment 3 during thesitting-down motion (p < 0.001) and the standing-up
motion (p < 0.1). Similarly, the mean moment value inexperiment 2 was considerably larger than that observed in

SMC_2016 002356

・

10

0
2
Time [s]4Flexion and extension
momentSit down
20

・

10

0
2

4
Time [s]

10

2

0

4
Time [s]

100

0

・

0

Flexion and extension moment(right knee) [N m]Knee angles [deg]Stand up20

0

0

Fig. 6: Experiment 1. Knee angles and flexion and extension
moment of the right knee in the case of a healthy subject. The
chair height was 40 cm and no hand guide was used. A greater
the moment is observed at the beginning of the standing-up
motion and the end of the sitting-down motion.
Right-knee angle
Left-knee angle

Sit down

100

6

0

Fig. 7: Experiment 2. Knee angles and flexion and extension
moment of the impaired knee. A healthy adult simulated kneeOA patients with robot control. The chair height was 40 cm
and no hand guide was used.

Right-knee angle
Left-knee angle
100Stand up

Flexion and extension
momentSit down

20

Flexion and extension moment(right knee) [N m]0Stand up

Flexion and extension
momentFlexion and extension moment(right knee) [N m]

20

100

Right-knee angle
Left-knee angle

Knee angles [deg]Sit down

Stand upKnee angles [deg]Flexion and extension
momentKnee angles [deg]Right-knee angle
Left-knee angle

Flexion and extension moment(right knee) [N m]2016 IEEE International Conference on Systems, Man, and Cybernetics • SMC 2016 | October 9-12, 2016 • Budapest, Hungary

・

10

0
0

2

4
Time [s]60Fig. 8: Experiment 3. Knee angles and flexion and extension moment of the impaired knee. A healthy adult simulated knee-OA patients with robot control, using a highchair (height = 55 cm), and no hand guide.

Fig. 9: Experiment 4. Knee angles and flexion and extension
moment of the impaired knee. A healthy adult simulated kneeOA patients with robot control and a hand guide. The chairheight was 40 cm.

experiment 3 during the standing-up and the sitting-down
motions (p < 0.001). Using a high chair following clinicalinstructions enables patients to move their center of gravity
easily at the times tA and time tB . Furthermore, it reduces theburden on the impaired knee. We confirmed the same tendency
for our simulated patient, whose knee moments were smaller
for the higher chair than for the shorter one.

burdens experienced by actual patients are consistent withthose on the knee moments of the simulated patient. As
previously stated, the physical burdens on the impaired kneesof knee-OA patients that follow clinical advice or instructions
from therapists are smaller than those of patients who do not.
Furthermore, because patients avoid overloading their impaired
knees, the physical burdens on these knees are smaller thanthose of healthy people. These trends were represented using
our patient simulation, in which the moment around the knee
was used as an index of physical burden. These results corroborate the validity of the patient simulation, although further
validation from different viewpoints remains to be studied.

Secondly, we compared experiments 2 and 4, which differed in terms of the use of a hand guide. Both the maximum
and the mean values of the flexion and extension moments of
the impaired knee were significantly larger in experiment 2
than in experiment 4 during the standing-up (maximum moment: p < 0.001, mean moment: p < 0.01) and sittingdown motions (maximum moment: p < 0.01, mean moment:
p < 0.01).
According to the clinical instructions for knee-OA patients,
the use of a hand guide reduces the burden on the impaired
knee by distributing the body weight to three points. In theexperiment involving our patient simulation, the moment along
the impaired knee decreased because of the use of the handguide, which is in agreement with the clinical cases of actual
patients.
The general effects of the clinical instructions on theVII.

C ONCLUSION

To solve economic, safety, and ethical problems related
to experiments involving motor-impaired patients, we havedeveloped a patient simulator with which a healthy person
mimics the impaired motion with the guidance of an exoskeletal robot. Such techniques will help us evaluate theeffectiveness of personal care equipment by allowing us toestimate the physical burdens that motor-impaired users may
face.
In this study, we demonstrated a simulation of the sittingdown and standing-up motions of knee-OA patients. Using

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Maximum value of flexion andextension moment [N m]2016 IEEE International Conference on Systems, Man, and Cybernetics • SMC 2016 | October 9-12, 2016 • Budapest, Hungary

***
***

[2]
+

+ : p < 0.1** : p < 0.01*** : p < 0.001

**
***
**

‧20

Standing up
Sitting down

10

[4]

[5]

0

Exp. 1

Exp. 2Exp. 3

Healthy

ImpairedHigh chair Hand guideExp. 4[6]

Experimental conditions

Fig. 10: Maximum values of the flexion and extension moments of the knee during each of the standing-up and sittingdown motions. Comparison between experiments 1-2, 2-3,
and 2-4 using t-tests with the Bonferroni p-value correction.
The error bars are the standard deviation calculated for all
trials.
20

Mean value of flexion andextension moment [N m]

[3]

***
***

‧

[8]

[9]
** : p < 0.01*** : p < 0.001

**
***
**
**

10

0

[7]

[10]

Standing up
Sitting downExp. 1

Exp. 2Exp. 3

Healthy

ImpairedHigh chair Hand guideExp. 4[11][12]

Experimental conditions[13]

Fig. 11: Mean values of the flexion and extension momentsof the knee during each of the standing-up and sitting-down
motions. Comparison between experiments 1-2, 2-3, and 2-4
using t-tests with the Bonferroni p-value correction. The error
bars are the standard deviation calculated for all trials.

[14]

[15]

the flexion and extension moment around the impaired knee,
we evaluated the degree of physical burden during thesemotions. We compared the knee moments under four different
conditions: control (healthy), simulated impairment, simulated
impairment with a high chair, and simulated impairment witha hand guide. We then found that the order of the maximum
and average knee moments measured in our experiments wasin good agreement with those observed in clinical settings,
which in part corroborates the validity of our patient simulation
method. Whereas, further detailed validations are necessary.
The developed patient simulator may enable us to estimate
realistically the physical burdens of patients with motor impairment by employing healthy subjects.

[16]

[17]

[18]

[19]

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