Wearable robot for simulating knee disorders inthe training of manual examination techniques
Shun Ishikawa1⋆ , Shogo Okamoto1 , Kaoru Isogai1,2 ,Naomi Yanagihara-Yamada3 , Yasuhiro Akiyama1 ,Yujiro Kawasaki1 , and Yoji Yamada1
1 Dept. Mechanical Science & Engineering, Nagoya University, Nagoya, Japan
2 Dept. Physical Therapy, Tokoha University, Hamamatsu, Japan
3 Dept. Physical & Occupational Therapy, Nagoya University, Nagoya, Japan

Abstract. This study addresses a haptic simulator of diseased knee
joints that is intended for use in the training of manual examination
techniques used in physical therapy. These techniques involve clinicians
moving the impaired joint of a patient to test the dynamic joint resistance caused by a condition. Our exoskeleton robot simulates ﬁve typesof common knee problems. The simulated symptoms were checked interms of subjective similarity to actual symptoms that physical therapists experience in their work. Three of the ﬁve symptoms could becorrectly identiﬁed whereas the other two were found to require furthertuning. The proposed patient simulator could improve the training ofmanual examination techniques by being able to simulate a variety ofsymptoms.
Keywords: Patient simulator, Physical therapy, Manual examination

1Introduction

Physical therapists manually examine the biomechanical and neural responses ofthe limbs of their patients to understand the nature of their disorders. In theseexaminations, the therapists test the dynamic joint resistance, as well as therange of joint motion, when the impaired limbs are moved passively. Although atherapist requires clinical experience to master these manual examination techniques, educational facilities rarely have the means of giving trainee therapists
experience with real patients. To solve this issue, we have been developing patient simulators, which will allow trainee therapists to experience the haptic
characteristics of diseased joints [2, 3]. A unique aspect of our approach is that
we use an exoskeleton robot to simulate joint impairment, rather than taking
the robotic patient approach of earlier schemes [4, 6]. In our approach, a healthy
person wears the exoskeleton, which simulates the abnormal behavior of a patient’s limb. Since the simulator incorporates part of an actual human body, ahigh level of realism can be achieved, including the complexity of human joint
motion, as well as the bone and skin features of the human body.
⋆This study was in part supported by Naito Foundation.

2S. Ishikawa, S. Okamoto, et al.

Fig. 1. Exoskeleton-type knee patient simulator. The healthy wearer (on the left) plays
the role of patient. The trainee (on the right) practices the examination techniques.

The objective of this study is to demonstrate and validate the authenticity ofa variety of simulated knee symptoms using a knee exoskeleton robot, building on
our previous work whereas we simulated a spastic knee joint and crepitus [2, 3].
In this study, we also set out to simulate the symptoms of contracture, lead-pipe
rigidity, cog-wheel rigidity, velocity-dependent resistance, and bony ankylosis,
which are common symptoms that physical therapists are likely to treat in aclinical setting, and which are described in the following section. To test theauthenticity of the simulated symptoms, we invited practicing physical therapiststo participate in a symptom-classiﬁcation experiment.

2

Wearable knee impairment simulator

Fig. 1 shows a mock patient, wearing the knee exoskeleton, and a trainee therapist whose task is to specify the knee joint impairment. The wearer relaxesand does not invoke any voluntary motions, while the trainee ﬂexes or stretches
the wearer’s leg to test the dynamic force feedback. This is undertaken in an
educational facility, where the trainees take turns in playing the role of patientwhen learning these manual techniques.
The knee robot consists of two links, fastened to the wearer’s femoral andlower thighs, and one DC motor (RE35, Maxon, stalling torque = 949 mN·m,1/86 gearing) which exerts a torque around the knee joint through a pair ofbevel gears. The DC motor is controlled by a microcomputer operating at 5 kHz
through a voltage-current converter (4-Q-DC ADS 50/5, Maxon). This system,
which was developed as part of another study, was able to measure the joint
angle and one-dimensional force applied to the tibia link.

Exoskeleton-type Knee patient simulator

33

Simulated symptoms

We simulated ﬁve diﬀerent symptoms. Three of these, namely, contracture, leadpipe rigidity, and velocity-dependent resistance, were also simulated in someearlier studies using robotic patient simulators. We focused on the authenticity
and feasibility of the ﬁve types of simulated symptoms produced by the wearable
joint simulator, rather than on developing sophisticated algorithms. Here, we
brieﬂy describe each symptom and how they were simulated. Each symptom
involves gain parameters, which determine the severity of a symptom or itscharacteristics. In a latter experiment, these parameters were moderately set by
the coauthors of this article, both of whom are physical therapists, such thateach symptom was barely discernible.
Lead-pipe rigidity: Lead-pipe rigidity, which typically accompanies Parkinson’s disease, is characterized by a constant stiﬀness of the joint. This stiﬀened
joint can be simulated by providing a constant resistance against passive joint
motion. We set the joint resistance torque of the lead-pipe rigidity to 0.5 N·m.
Cog-wheel rigidity: Cogwheel rigidity is similar to lead-pipe rigidity, however,
it is characterized by a non-uniform resistance. We modeled this symptom by{
1.0 N · mτcog =(1)π7π 2ππ0.5 N · m ( π9 < θ < 5π
36 , 6 < θ < 36 , 9 < θ < 4 )where the fully extended angle was 0 rad. As a result, intermittent joint stiﬀness
is experienced by the trainee as s/he manipulates the knee.
Contracture: Contraction involves chronically stiﬀened tendons or muscles
constraining the joint movement. This limitation of the range of motion is typically modeled by using a spring coeﬃcient. We also simulated contracture as anelastic resistance (5.7 N·m/rad) that acted only during knee ﬂexion. In addition,
to provide a resistance-free range, the elastic resistance was not exerted until thejoint angle reached π/18 rad.
Bony ankylosis: An abnormal bony deformation or an artiﬁcial joint also
limits the range of motion of the joint. The therapist can sense the contact
between the non-elastic bones at the end of the range of motion. We simulated
the resistance of this symptom by using a large spring constant of 28.5 N·m/rad.
The free motion range was set to 0 to 2π/3 rad.
Velocity-dependent resistance: A velocity-dependent joint resistance is causedby an abnormal stretch reﬂex of a joint, and is also referred to as spasticity. This
type of resistance was simulated as a viscosity relative to an angular velocity insome earlier studies [1, 5]. We also followed their approach and determined theresistance torque from{cθ̇(t) (θ̇(t) ≥ 0)
τvel =(2)0 (otherwise)
where the viscosity coeﬃcient was set to 1.5 Nm·s/rad.

4

S. Ishikawa, S. Okamoto, et al.

4Classification experiment to check the authenticity ofthe simulated symptoms and the knee patient simulator

4.1

Methods and Tasks

We conducted a classiﬁcation experiment in which physical therapists experienced and classiﬁed the simulated symptoms so as to validate the authenticityof our simulator. All of the procedures were approved by the IRB of the Engineering School of Nagoya University.
A healthy person wore the exoskeleton knee robot on the right leg and layon a bed in a relaxed posture as shown in Fig. 1. Nine physical therapists, eachwith more than ﬁve years of clinical experience, took part in the experiment
after providing their written informed consent. None of them had any experience of robotic joint impairment simulators. Before the main task, each wasshown a list of the symptoms to be simulated and then actually experienced allof them. At this point, they were not provided with any feedback on the presented symptoms. In the main task, each participant was allowed to freely test
each symptom for a maximum of one minute, before providing the name of thesymptom that s/he believed was being simulated. The experiment thus involved
a ﬁve-alternative forced-choice task. The simulated symptoms were presented
in random order with each being experienced ﬁve times in total. Hence, eachparticipant performed twenty-ﬁve trials.
4.2

Results - Positive answer ratios

Fig. 2 shows the averages and standard deviations of the answer ratios at which
the presented and classiﬁed symptoms matched. Lead-pipe rigidity, cog-wheel
rigidity, and bony ankylosis were classiﬁed as expected with statistically signiﬁcant probabilities. Especially, ankylosis was always classiﬁed correctly. However,the positive answer ratios for the simulated contracture and velocity-dependent
resistance were almost the chance. Velocity-dependent resistance was often confused with lead-pipe rigidity (0.31 on average) and contracture (0.33 on average). According to introspective reports provided by the participants after themain task, the velocity-dependent resistance was too small to diﬀerentiate fromthe other two symptoms. Moreover, contracture was often wrongly classiﬁed
as velocity-dependent resistance (0.40 on average). The participants could not
diﬀerentiate between these two types of symptoms possibly because the force
feedback gains were set to low values.

5Conclusion

In this study, we simulated several types of common knee disorders using an exoskeleton robot for providing training in manual examination techniques used byphysical therapists. Because the wearable simulator for joint impairment is wellbalanced in terms of realism and cost, its practical use would allow the simulation of a greater number of symptoms. The ﬁve symptoms, namely, contracture,

Exoskeleton-type Knee patient simulator

5Fig. 2. Average positive classiﬁcation ratios of simulated symptoms and standard deviations among the participants. Here, * indicates a statistically signiﬁcant diﬀerence
from the chance (0.2) with p < 0.05 of two-tailed t-test.

lead-pipe rigidity, cog-wheel rigidity, bony ankylosis, and velocity-dependent resistance, were simulated and evaluated. Although the gain that determines theprominence of the joint resistance has bet to be tuned to allow the device’s use
in an educational facilities, however, the simulated symptoms could be correctlyclassiﬁed in a test involving nine physical therapists.

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