IECON2015-Yokohama
November 9-12, 2015

Patient Simulator Using Wearable Robot:
Representation of Invariant Sitting-down andStanding-up Motions of Patients with Knee-OA
Naomi Yamada

Yasuhiro Akiyama

Yoji Yamada

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

Ryu Kubo

Shogo Okamoto

Shogo Nezaki

Nagoya University
Nagoya, Japan

Nagoya University
Nagoya, Japan

Abstract—It is ethically problematic to engage people with
impaired motor functions in experiments, wherein they may face
severe pain and unacceptable risk of injuries. We developed awearable robot for healthy adults and its control algorithm tosimulate the behaviors of patients with knee osteoarthritis. We
focused on invariant motions, which are typically presented foravoiding knee pain. In order to simulate patient movements by ahealthy person wearing a robot, we formulated a model motionthat represented invariant patient motions. We then determined
the output torque of the exoskeletal knee robot such that thewearer’s motion followed the model motion. The effectivenessof the method was testiﬁed for standing-up and sitting-down
motions, and some characteristic impaired motions such as body
inclination to the healthy side and imbalanced right and left knee
angles were manifested by the simulator.

I.

I NTRODUCTION

It is desired to establish a society in which people with
impaired motor function can enjoy the same quality of life
as enjoyed by healthy people. The reasonable and accurate
assessment of the burdens incurred by impairment in patients’
daily lives and the physical accessibility of public facilities
contribute to the improvement of patients’ quality of life.
However, it is ethically unacceptable to engage impaired
people in experiments to assess their burdens during certain
activities especially when the experiment may involve severe
pain or critical risks such as fall. To solve this problem, we
proposed a novel method whereby a healthy person simulates
the movements of an impaired person and participates inexperiments.
Several research groups have attempted to simulate theimpaired by the healthy. Pacala et al. reported a workshop
for medical students to experience the inconvenience felt
by elderly people needing caregiving [1]. They wore, forexample, arm-hanging implements to imitate a symptomatic
frozen shoulder. Wood held a similar workshop for participants
to experience chronic diseases of the aged, using bandages
and simulating the limited movements of knee joints causedby arthritis. She reported the educational effects of the study
on the participants [2]. Positive effects of such experiences
by wearing obstructive items have been also recognized byother researchers [3], [4]. Ullauri et al. employed a taping
technique to imitate the limited ankle movements caused by
the weakened muscles of the aged [5]. Aging simulators havebeen sold by some companies, with which the wearers aremeant to experience motor, visual, and auditory deﬁciencies by

978-1-4799-1762-4/15/$31.00 ©2015 IEEE

equipping their own bodies with weights, joint braces, goggles,
etc. However, these methods only allow us to imitate passively
obstructive factors. Recently, Huang et al. developed a patient
simulator with actuated shoulder joints for training of patienttransfer between a wheel chair and a bed [6]. However, they
focused on the case of patient transfer and did not simulate apatient walking in the environment.
In contrast, Ishikawa et al. proposed a patient simulation
method using exoskeletal robots for aiding in the training of
the manual examination technique for physical therapy [7], [8].
Their robots were computer-controlled such that the wearer’s
joint resistances were similar to those of the impaired. In the
study, Ishikawa et al. simulated several symptoms remaining
a sufﬁcient human likelihood unlike other robotic patient
simulators. However, the wearers were supposed to relax andnot move actively, as the purpose of the study was limited
to simulating patients being passively examined or treated byclinicians.
The objective of the present study was the simulation ofinvariant and typical motions of people suffering from impaired
knee motor function by using an exoskeletal knee robot. Suchattempt is novel compared with the earlier patient simulator
by passive obstructs [1]–[5]. We formulated model motionsof knee osteoarthritis (OA) according to the characteristic
abnormal movements of the impaired. We then determined thecontrol rule for the wearable robot such that the wearer’s motions resembled a model motion. We experimentally tested thedeveloped system for sitting-down and standing-up motions.
As a result, substantial similarities were observed between themodel and simulated movements, indicating the effectivenessof our framework.
II.

K NEE OSTEOARTHRITIS

We simulate the motions of knee-OA patients because of
the signiﬁcant effects of this impairment on patients’ daily
lives and popularity. In one report, the number of knee-OA
patients in Japan with noticeable symptoms was estimated tobe approximately ten million [9].
Knee-OA develops with aging and is typically accompanied by damage to the meniscus, cartilages, and bone
spurs [10]. Patients suffer from symptoms such as pain duringmotion, a limited motion range, inﬂammation, and joint deformation [10], [11]. Patients with moderate knee-OA experienceslight pain that hardly obstructs motions such as stair walking.

004808TABLE I: Characteristic abnormal sitting-down and standingup motions of knee-OA patients

Goniometer

Slow movements due to the fear of pain.
Body inclination to the healthy side to avoid overloading on the
impaired side. Typically, 70 and 60% of the weight is loaded on
the healthy side during sitting and standing motions, respectively.
Patients stand up from the chair with their hands on their femoral
thighs.
Fall to the seat at the last phase of the sitting-down motion.
Range of the joint motion is limited. Full knee extension and deep
ﬂexion are avoided.

No.1
No.3

Exoskeletal knee
robot with two links
and one actuator

Fig. 1: Exoskeletal knee robot wornby healthy human (right leg). Goniometer (left leg, hidden).

No.2

Fig. 2: Locations offorce sensors (impaired side)step before experimentation on actual patients, we constructed
model motions based on typical impaired motions performed
by a healthy person. Even such a problem setting allowed us
to testify whether our method, which is described later, cansimulate speciﬁc abnormal motions that are different from the
motions of healthy persons.
A. List of invariant motions

Intermediate patients suffer from pain in moving their knee
joints, especially with a large load applied to the joints.
These patients’ motions differ from those of healthy persons,as the patients tend to avoid motions causing pain. Severe
patients undergo difﬁculties in their daily activities because of
frequent and signiﬁcant agony. In this study, we addressed theimitations of intermediate knee-OA patients. In particular, thefollowing conditions were considered. Knee OA is developed
at their right sides. The locomotive motions of patients areapparently distinct from healthy ones. Finally, they do not relyon supportive instruments such as a cane or hand rail.
III.

E XPERIMENTAL EQUIPMENT

As shown in Fig. 1, a wearable exoskeletal knee robot wasworn by a healthy adult on the right leg using two plastic
braces for the femoral and lower thigh, respectively. 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) were installed on the knee joint. The motor
was driven by a current driver (4-Q-DC ADS50/5, Maxon
Motor) through a computer control at 5 kHz. On the left knee,a potentiometer-based goniometer was attached using Velcro
tapes.
We measured the interaction forces between the right footand the ground. Three three-axial force sensors (USL08-H181kN-AP, Tec Gihan, Japan) were embedded into a shoe soleat the locations shown in Fig. 2. The sensors were placed on
the thenar and antithenar eminences and the heel. The sensor
heads were appropriately leveled, such that they covered theload paths between the human and the shoe sole.
IV.

I NVARIANT MOTIONS OF KNEE -OA PATIENTS

Our ﬁnal goal is to extract invariant motions from multiple
actual knee-OA patients and stimulate those motions using ahealthy adult with a wearable robot. However, to a greater or
lesser extent, experiments measuring patients’ motions impose
physical burdens on the patients. Therefore, as a preliminary

In this research, we selected sitting-down and standing-up
motions as the target motions to be simulated. These motions
occur often in daily lives and cause pain to patients. The model
motions were determined by the following two steps. First, welisted the typical motions of knee-OA patients according to theliterature [12] and opinions of a physical therapist (N.Y., oneof the authors of this article). Second, a healthy adult imitated
the typical motions of the impaired, under the supervision of
N.Y.
Table I lists the invariant abnormal motions of the impaired
while sitting-down on and standing-up from a chair. For
example, the patients typically incline their upper bodies tothe healthy side to avoid overloading on the impaired side.
B. Invariant motion of simulating target
According to the list of invariant motions and supervision
by N.Y., a healthy person imitated the typical sitting-down
and standing-up motions of knee-OA patients. To compare theimpaired motions with the healthy motions, we also measured
the healthy person’s normal sitting-down and standing-up
motions.
First, we checked whether the apparent features typical tothe patients were exhibited by the performer. Fig. 3 shows theapparent movements of the model motions. Compared with thesymmetric right and left knee changes for the healthy motion,
the model motions exhibited an asymmetric body inclination to
the healthy side. Furthermore we observed slower movements
and falls to the seat in the last phase of the sitting-down
motion.
Figs. 4 and 5 show the typical time courses of the knee
angles and foot loads for the healthy (normal) and imitatedmodel motions, respectively. In the case of the normal motions,both knee angles matched. In contrast, in the case of the model
motions, they did not: the knee of the impaired side tended totake more extending positions than that of the healthy side.
From the ﬁgure, we observed characteristics such as slow
movements and the avoidance of full knee extension, which
are consistent with the motion list in Table I.

004809Sit down phase

Stand up phase
(1)

(2)

(4)

(3)

(5)

(6)

Fig. 3: Healthy person mimicked impaired standing-up and sitting-down motions. Subject’s feet positions remained ﬁxed. He
imitated the abnormality in his right knee. Pictures were arranged chronologically from left to right. The robot was not actuated,
but was used for data collection.

Stand up
Sit down

0
0

2

4

0

6 8 10 12 14 16
Time [s]80

Right = Left
Sample

60
40
20
0

0

Stand
up

0

2

4

Sit
down

6
8
Time [s]10012Fig. 5: Knee angles and foot load of model motion of theimpaired. Performed by a healthy adult without robot control.

Knee angle [deg] (impaired side)

Knee angle [deg] (right side)

Fig. 4: Knee angles and foot load of healthy motion.

100

100

Foot load (right foot) [N]100Foot load (right)
500

Knee angle [deg]Knee angle [deg]5000Left knee angle
Right knee angle

Foot load (right)
Foot load (right foot) [N]Left knee angle
Right knee angle

20
40
60
80
100
Knee angle [deg] (left side)100
80

Right = Left
Fitted curve
Sample

60
40
20
0

0

20
40
60
80
100
Knee angle [deg] (healthy side)

Fig. 6: Knee angles during normal sitting down on andstanding up from chair. A dotted line is θref = θh .

Fig. 7: Knee angles during model motion of sitting down on
and standing up from chair with ﬁxing feet positions. A red
curve is a ﬁtted quadratic function. A dotted line is θref = θh .

We also observed some preferable characteristics regarding
the foot loads. In the case of healthy motions, the loads werelargely constant at 340 N during the standing-up (at 2.0–3.0 s
in Fig. 4) and sitting-down (at 11.5–12.5 s) phases. The loads
were almost equal for the right and left feet, provided that

340 N was approximately half of the subject’s weight (70 kg).
In contrast, in the case of the imitated motions, the foot load on
the impaired side was obviously reduced (150–200 N) in thetransition phases of the sitting and standing motions, as shownin Fig. 5 at the time intervals of 3.6–5.0 s and 8.0–10.5 s.

004810Sit down phase

Stand up phase
(1)

(3)

(2)

(4)

(5)

(6)

Fig. 8: Simulated impaired standing-up and sitting-down motions with robot control. Subject’s feet positions remained ﬁxed.
Pictures were arranged chronologically from left to right.

These loads were approximately 20–30% of the subject’s bodyweight, and the results corresponded with the features in the
motion list.

B. Torque-control rules to realize model motions
We controlled the torque output of the robotic knee joint
as follows:

The aforementioned observations indicate that the model
motions performed by the healthy person were consistent withthose in the motion list in Table I. Therefore, the model
motions can be references for the invariant motions of typicalknee-OA patients.
V.

τ (t) = τa (t) + τb (t)f (t)τa (t) = Kp
(θref (θh , t) − θi (t))
fmax
f (t)
θ̇i (t) g(θi )τb (t) = −Cp
fmax
1g(θi ) =1 + exp(−a(θi (t) − b))

T ORQUE - CONTROL BASED ON THE FORMULATEDTYPICAL MOTIONS OF KNEE -OA PATIENTS

A. Formulation of impaired knee angle
Because the wearer wore the robot on his right leg, only
his right knee joint was directly inﬂuenced by the robot. To
determine the set values of the right-knee angle, we formulated
the model motions, which were deﬁned in the previous section,
as a function of the knee angle of the healthy (left) side. This
enabled us to imitate the impaired motion using only a onesided robot.
Fig. 6 shows the samples (green markers) of the right andleft knee angles when a healthy adult repeated normal sittingdown and standing-up motions six times. For the healthy
motions, θref = θh nearly held. In contrast, as shown in
Fig. 5, the impaired knee angle tended to be smaller thanthat of the healthy side at moderate knee angles, and thistrend did not rely on the type of motions, i.e., sitting-down
and standing-up motions. Fig. 7 shows the samples when thesubject imitated the abnormalities of the impaired. The twoknee angles were effectively expressed by a quadratic function.
Hence, we formulated the set angle of the impaired knee θrefas a quadratic function of the healthy knee angle θh as follows:2θref (θh , t) = a2 θh (t) + a1 θh (t) + a0

(1)

where a2 , a1 , a0 were the coefﬁcients speciﬁed by a leastsquares method. The relationship between the two knee anglesof the imitated motion (degree) was formulated as2

θref (θh , t) = 0.0043 θh (t) + 0.57 θh (t) + 2.6.

(2)

The red curve in Fig. 7 is the function deﬁned by (2). Using
this function, we could specify the set knee angle of theimpaired side from that of the healthy side.

(3)
(4)
(5)
(6)

where Kp , f (t), and fmax were the proportional gain, total footload of the impaired side, and weight of the wearer, respectively. g(θi ) was a sigmoid function that smoothly switchesfrom 0 to 1 around a given knee angle. a and b were set to 1.0
and 100, respectively such that g(θi ) becomes 0 and 1 below
and over: θi = 100 deg. Kp and Cp values were determinedon a trial and error basis.
The output torque τ (t) was determined by two functions ofτa (t) and τb (t). τa (t) was in proportion to value of difference
between measured knee angle of impaired side and reference
one and made wearer’s motions follow the model motions.
τb (t) prevented the wearer’s motions from going out of thespace of the model motions, concretely it prohibited thewearer from moving under deep ﬂexion (higher than 100 deg,
therefore b = 100 on (6)) of his impaired knee. Knee-OA
patients avoid moving under such situation. Increase of footload of the impaired side causes severe pain and it prevents
knee-OA patients from moving. To represent this feature, both
functions τa (t) and τb (t) were multiplied by the normalizedfoot load of the impaired side. For example, the robot exerts
large torque when the wearer leans on the impaired side andno torque when the impaired foot does not contact the ﬂoor.
VI.

E XPERIMENT

A. Tasks and participants
Our method to simulate the model motions was testiﬁed
for standing-up and sitting-down motions. A healthy adult thatwas naive about our simulator wore the exoskeletal robot andrepeated these motions for 20 times, of which 19 trials were004811Knee angle [deg] (impaired side)

Fig. 9 shows the samples of the right and left knee angles ofthe simulated motions for 19 trials, and the reference functions(red curve) deﬁned by (2). They indicated that the knee anglesof the simulated motions fairly matched the reference curve. A
time course sample of the angles of the both knees shown in
Fig. 10 also corroborate the similarity between the two typesof motions, i.e. impaired model motion and simulated motion.
Speciﬁcally, in the simulated motions, both knee angles did
not exactly match, and the knee of the impaired side tended
to be smaller (extension) than that of the healthy side.

Right = Left
Reference
Sample

100
80
60
40
20
0

0

20
40
60
80
100
Knee angle [deg] (healthy side)

Fig. 9: Healthy and impaired knee angles of the simulated
motions. The red curve is the model motion deﬁned by (2).

Stand up

100

Foot load (right)
Sit down
Max. gap

00Knee torque [N䞉m]500Foot load (right foot) [N]Knee angle [deg]Left knee angle
Right knee angle

Fig. 10 (bottom) shows a sample of the torque output by thewearable robot. The negative torque is to extend the impaired
knee. For this example, in the standing-up phase, the torquemostly acted to extend the impaired knee, which might have
led to the body inclination to the healthy side. In the sittingdown phase, the torque was also generated in the directionof knee extension, which functioned as an impediment to thewearer’s motion. As a result, the left leg preceded the right(supposedly impaired) leg.

0
-2
-4

0

2

4
Time [s]Fig. 10 also shows the foot load measured at the impairedside. The foot load on the impaired side was smaller than
200 N for the large part of the motions whereas the weightof the wearer was 65 kg (637 N). In the case of the example
shown in Fig. 10, they were 70–180 N (at 1.0–3.0 s in Fig. 10)
and 50–180 N (at 8.0–10.0 s in Fig. 10) during the standing-up
and sitting-down motions, respectively. This reduction of thefoot load on the impaired side indicates the imbalanced bodyuse of the wearer.

6

Fig. 10: Sample of simulated motion. Top: Knee angles andfoot load of the impaired side. Bottom: Knee torque output by
the wearable robot. Negative torque is to extend the wearer’s
knee.

valid and used for the later analysis. During the experiment, he
was instructed not to attempt to resist the torque presented by
the wearable robot, but to be guided by the robot. The robot
does not exert torques large enough to forcefully move thewearer’s knee, but it guides and impedes the wearer’s motion.
B. Experimental results of movement simulation
Fig. 8 shows an example of the simulated motion acquiredin the experiments. A body inclination to the healthy side,slower movements, and falls to the seat at the last phase of thesitting-down motions were observed in the simulated motions.
These are consistent with the characteristic impaired motions
listed in Table 1, suggesting that some features of the model
motions were well represented.

One of the evident differences between the healthy andimpaired motions lies in the imbalanced right and left knee
angles. As previously described, for the impaired motion, rightknee angle tends to be smaller (extension) than the left knee
angle or the body inclines to the healthy side to avoid theloading on the impaired side. To test this angle differences
between the right and left knees, we calculated the maximum
difference during each of the simulated standing-up and sittingdown motions, as shown in Fig. 10, for all 19 trials. The
means and standard deviations of the maximum knee angle
differences were 7.2±0.51 and 6.9±0.64 deg for the standingup and sitting-down motions, respectively, as shown in Fig. 11.
These values are signiﬁcantly higher than zero (standing up:
z = 61.8, p < 0.001, sitting down: z = 47.1, p < 0.001),
indicating that the right (impaired) knee angle was more
extended than the left (healthy) knee angle.
The aforementioned results indicated that our torquecontrol rules physically guided a healthy wearer to the formulated model motions. In contrast, some apparent differenceswere observed between the model and simulated motions.
The simulated motions were faster and the foot loads of thesimulated motions did not completely match those of themodel motions (comparison between Figs. 5 and 10). These
differences were due to the limitation of the model function as
determined by (1), which included only knee angles, but notangular velocities and force loads.
VII.

C ONCLUSION

To resolve the ethical barriers in experimental study involving motor-impaired patients, we developed a ﬁrst prototype ofa patient simulator using a wearable robot. We focused on

004812

Maximum difference between
right and left knee angles [deg]

8

***

***

Stand-up

Sit-down

6
4
2
0

Fig. 11: Maximum differences between right and left knee angles of simulated motions. *** indicates statistically signiﬁcantdifference compared with zero at p < 0.001.

[8] S. Ishikawa, S. Okamoto, K. Isogai, Y. Akiyama, N. Yanagihara, andY. Yamada, “Assessment of robotic patient simulators for training inmanual physical therapy examination techniques,” Plos one, vol. 10,
no. 4, p. e0126392, 2015.
[9] Ministry of Health, Labour and Welfare, “The provision for motor
system disease to advance preventive care,” http://www.mhlw.go.jp/
shingi/2008/07/dl/s0701-5a.pdf, 2015.2.26.
[10] R. D. Altman, “The merck manual for health care professionals- Osteoarthritis (OA),” http://www.merckmanuals.com/home/bone
joint and muscle disorders/joint disorders/osteoarthritis oa.html,
2015.2.26.
[11] N. Kito, “Research of the moment of varus of knee joint for the outerside and the muscular function of the lower limbs during walking withmedial knee osteoarthritis,” Ph.D.Thesis, Hiroshima University, 2009.
[12] M. Anan, K. Tokuda, N. Kito, and K. Shinkoda, “Kinematic analysis
of sit-to-stand motion in knee osteoarthritis,” Physiotherapy Science,
vol. 25, no. 5, pp. 755–760, 2010.

knee-OA patients because of their prevalence. Our strategy
was ﬁrst to formulate the typical motions of knee-OA patients.
We then devised a control rule for the exoskeletal knee robot
to guide the wearer’s motions to match the designated patient’s motions. In our experiment, sitting-down and standingup motions were simulated by the wearer, and they weresimilar to those of typical knee-OA patients with regard to thetemporal proﬁles of the knee angles, upper body inclination,and reduced foot load during the motions. The wearable patient
simulator potentially allows us to conduct experiments toassess the accessibility of environments for people with motor
impairment with few ethical challenges.
However, due to the primitiveness of the formulation of
the model motions, the simulated motions did not dynamicallymatch the model motions i.e. angular velocities of the knee
and foot loads. Also, our method is not still applicable toother motions such as walking. Hence, some extension is fairly
needed for further improvement.
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