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IEEE SYSTEMS JOURNAL, VOL. 10, NO. 3, SEPTEMBER 2016

An EMG-Driven Weight Support System
With Pneumatic Artificial Muscles
Jun-ichiro Furukawa, Tomoyuki Noda, Tatsuya Teramae, and Jun Morimoto

Abstract—In this paper, we introduce our newly developed
biosignal-based vertical weight support system that is composedof pneumatic artificial muscles (PAMs) and an electromyography(EMG) measurement device. By using our developed weight support system, assist force can be varied based on measured muscle
activities; most existing systems can only generate constant assist
forces. In this paper, we estimated knee and ankle joint torques
from measured EMGs using floating base inverse dynamics. Kneeand ankle joint estimated torques are converted to vertical forcesby the kinematic model of a subject. The converted verticalforces are used as force inputs for the PAM actuator system. To
validate our system’s control performance, four healthy subjects
performed a one-leg squat with his left leg while his right leg
was assisted by our proposed system. We used the vertical force
estimated from the measured EMG signals as a control input tothe weight support system. We compared EMG magnitudes withfour different experimental conditions: 1) normal two-leg squat;2) one-leg squat without the assist system; 3) one-leg squat with
EMG-based weight support; and 4) one-leg squat with constantforce support. The EMG magnitude with the proposed weightsupport system was much closer to that with normal two-leg squat
than that with one-leg squat without the assist system and thanthat with one-leg squat with constant force support.
Index Terms—Electromyography (EMG), force control, pneumatic actuators, rehabilitaion robotics, weight support system.

I. I NTRODUCTIONW

EIGHT support is essential in rehabilitation for lower
limbs, where standing up, stepping, walking, or balancing movements can be involved. In conventional weight support systems, assist force is typically constant, and operationalheight ranges are limited. Therefore, weight support devices
have only been used for safety or fault tolerance instead ofactively changing amount of weight support. On the other hand,
Manuscript received October 11, 2013; revised March 13, 2014 and April 24,
2014; accepted June 7, 2014. Date of publication July 8, 2014; date of current
version August 23, 2016. This research reflects the results of the “Developmentof BMI Technologies for Clinical Application” and “Brain Machine Interface
Development” carried out under the Strategic Research Program for Brain
Sciences by the Ministry of Education, Culture, Sports, Science and Technology of Japan. Part of this research was supported by MIC SCOPE anda contract with the Ministry of Internal Affairs and Communications entitled“Novel and innovative R&D making use of brain structures.” This research was
also partially supported by MEXT KAKENHI Grant Number 23120004, JSPS
KAKENHI Grant Number 24700203 and Strategic International Cooperative
Program, Japan Science and Technology Agency (JST) and by JSPA and MIZS:Japan Slovenia research Cooperative Program.
J. Furukawa is with the Graduate School of Frontier Biosciences, Osaka
University, Osaka 565-0871, Japan and with the Department of Brain Robot
Interface, ATR Computational Neuroscience Laboratories, Kyoto 619-0288,
Japan (e-mail: furukawa@atr.jp).
T. Noda, T. Teramae, and J. Morimoto are with the Department of
Brain Robot Interface, ATR Computational Neuroscience Laboratories, Kyoto
619-0288, Japan (e-mail: t_noda@atr.jp; t-teramae@atr.jp; xmorimo@atr.jp).
Digital Object Identifier 10.1109/JSYST.2014.2330376

Fig. 1. Schematic of our proposed weight support system. Upward anddownward movements are assisted with the force estimated from measured
electromyography (EMG).

for active weight and movement support, research attention on
the development of an exoskeleton robot is increasing [1]–[6].
However, it remains difficult for therapists to work with such
systems since exoskeleton robots generally require complicatedsetups. In this paper, we propose a weight support systemthat has a wider operational range and can adaptively change
the amount of support rather than simply generating constantforce. To satisfy the aforementioned specifications, we usedpneumatic artificial muscles (PAMs) with force sensors and a
device to measure the PAM length. PAMs are used for robotics
applications [6]–[10]. Fig. 1 shows a schematic of our proposed
weight support system.
To adaptively change the weight support force based on thesubject’s status, a user’s movement intention must be estimated.
For predicting user movements to control assistive devices,
EMGs are commonly used [1], [4], [11]–[18]. In this paper,
we estimate knee and ankle joint torque from measured EMGs.
We first simultaneously measure the joint angle trajectories
and the EMG profiles of a subject. Then, the torque sequencethat corresponds to the measured joint angle trajectories isderived using inverse dynamics. Although previous studies also
used inverse dynamics to estimate knee-joint torque from EMG
signals, the knee movements were generated when a subject
was sitting [19], [20]. To deal with such daily life related
behaviors as squat movements, we must explicitly consider the

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FURUKAWA et al.: EMG-DRIVEN WEIGHT SUPPORT SYSTEM WITH PNEUMATIC ARTIFICIAL MUSCLES

ground reaction force in inverse dynamics. Therefore, we use
floating base inverse dynamics [21].
To find the relationship between the target torque and themeasured EMG signals, we built a real-time torque estimation
model, which is composed of the Hill–Stroeve model [22], [23],to derive the muscle force from the measured EMGs and apulley-tendon model to convert the muscle force to joint torque.
The estimated torques of the knee and ankle joints are used
to derive vertical forces using the subject’s kinematic model.
Finally, we used the derived vertical forces as force inputs for
our PAM actuator system. To validate its control performance,a subject performed one-leg squats by his left leg while hisright leg was assisted by our proposed system. We used thevertical force estimated from the knee and ankle movement
related EMG signals measured by seven sensor channels as
control input for the weight support system. We compared
EMG magnitudes with four different experimental conditions:
1) normal two-leg squat; 2) one-leg squat without the assist
system; 3) one-leg squat with EMG-based weight support;
and 4) one-leg squat with constant force support. The EMG
magnitude with our proposed weight support system was muchcloser to that with a normal two-leg squat than that with a oneleg squat without using the assist system.
The rest of this paper is organized as follows. In Section II,
we introduce our newly developed weight support system thatis composed of PAMs and an EMG measurement device. In
Section III, we explain how we derive the assist force fromthe measured EMG signals. In Section IV, we introduce acalibration strategy for our weight support system and its
experimental setups. In Section V, we show the results of squat
experiments with four different conditions. We finally conclude
in Section VI.

Fig. 2. Torque tracking performance of PAM-based weight support systemwith frequencies of 0.1, 0.25, and 0.5 Hz. A thirty-kilogram weight was
attached to the bottom of the support system.

where ε is the contraction rate of PAM, and D0 and ψ0 are thePAM diameter and the angle of the embedded spiral wire at theatmosphere pressure, respectively. This can be represented asa quadratic function multiplied with the current PAM pressurep, i.e.,fpam = g(ε, p) = p(aε2 + bε + c)

(2)

where
3πD02
3πD02
, b=−
2
4 tan ψ0
2 tan2 ψ0


3
πD02
1
c=
−
.
4
tan2 ψ0sin2 ψ0a=

II. W EIGHT S UPPORT S YSTEM
A. PAM Actuators
To assist motions by estimated torques of the knee and ankle
joints, we develop a weight support system (see Fig. 2), where
the actuator is a paired PAMs.
We use 1.4-m-long PAM (FESTO Inc.), which has a diameter of 10 mm and 25% contraction rate from natural length.
According to the specification provided by FESTO Inc., thePAM can generate 630-N maximum force. The system is about
0.8 kg without including air compressor and valve. In addition,
the system can operate up to 0.5 Hz with the amplitude of
0.35 m. These specifications were experimentally validated as
described in Section IV-A.
While PAM is lightweight, it can produce large force, converting pressured gas energy into contraction force through its
rubber tubes. The force is generated by the path contraction of
the spiral fiber expansion embedded by the pneumatic bladder.
With proportional pressure valves, PAM pressure p can be controlled. The force generated by PAM depends on the pressurep and the contraction rate; the PAM force model [24], [25] isgiven by
3
πD02 p2(1 − ε) − bias
(1)
fpam =
4tan2 ψ01027(3)

If desired force fpam needs to be generated at contraction
rate ε, desired valve pressure p∗ can be derived from the inverse
model p∗ = g −1 (fpam ; ε).
B. EMG Measurement
We estimate the muscle force from ten sample histories of
EMG signals, where the sampling period is Δt = 4 ms. In otherwords, we use a 40-ms sample history to estimate muscle force.
Then, for the muscle force estimation, we consider an input
variable
qti =

uit
i
umax

(4)

where
uit =

t


eik Δt(5)k=t−9where eik represents low-pass filtered and full-wave rectifiedmuscle activity measured by the ith EMG sensor channel at1028IEEE SYSTEMS JOURNAL, VOL. 10, NO. 3, SEPTEMBER 2016

where ft = (f 1 , . . . , f m , 1) is the muscle contraction force,
and w = (w1 , . . . , wn , w0 ) is the model parameter vector of aconstant pulley model at knee and ankle joints. These parameters are determined by the least square estimation method tominimize the torque estimation error1(τt − τ̂t )2
E=
2 t=0
N

(8)

where the target torque τt is derived from the inverse dynamics
model, as explained in the following section. N is the numberof the training sampling.
Fig. 3. Control strategy of the weight support system. Muscle forces are
estimated from full-wave rectified and bandpass-filtered EMG signals. Joint
torque τ̂ is derived from the estimated muscle force by using the tendon-pulleymodel. Then, the derived torque is converted to desired assist force fpam for
PAM by taking measured joint angles into account.

time k with the cutoff frequency of 2 Hz. uimax is the measured
maximum muscle activity to normalize an impedance changeof an EMG electrode.
III. M ETHOD
Fig. 3 shows our control strategy of the proposed weightsupport system. First, we measure the EMG signals and thejoint angles simultaneously from the left leg during the normal
squat motion, and the parameters of the tendon-pulley model in(7) are estimated from the acquired EMG and joint angle data.
Then, the online operation of the weight support system is asfollows.
1) Online EMG and joint angle measurements during the
motion.
2) Derive the muscle force from the Hill–Stroeve model.
3) Estimation of the knee and ankle joint torques by using
the tendon-pulley model with the identified parameters.
4) Calculate the vertical assist force from the estimated knee
and ankle joint torques by using the Jacobian matrix,
which represents the relationship between the joint movements and a hip position movement.
A. Estimating Muscle Force
The muscle force is derived by using the Hill–Stroeve model[22], [23] from EMG signals. The model takes the nonlinearities of the muscle length–tension relationship and force–
velocity relationship, i.e.,

  
i
qti
(6)
fti = k ξti h ηti , ξti fmax
where each nonlinear function k(·) and h(·) cite Hatze’s parameterized fitting function [26] with two parameters, namely,
a1 and a2 (see the Appendix).
B. Torque Estimation Model
We consider knee and ankle joint estimation torques τ̂t andmuscle forces f t using the following standard simple linear
model:
τ̂t = w ft(7)

C. Floating Base Inverse Dynamics
To derive joint torques from joint angle trajectories, aninverse dynamics model of an approximated subject body isused. However, different from previous studies that worked on
estimating knee movements from EMG signals when a subject
was sitting on a chair [19], [20], we consider squat movementsthat are involved in daily behaviors. Therefore, we need to
explicitly take the ground reaction forces fgrf into account. For
this purpose, we use floating base inverse dynamics
M(q)q̈ + h(q, q̇) + g(q) = S τ + Jc (q)fgrf(9)

where q represents the general coordinate system of the joint
angles, M (q) is the floating base inertia matrix, h(q, q̇) is
the floating base centripetal Coriolis, g(q) is the gravity
force, S is the actuated joint selection matrix, and Jc is theJacobian matrix, which represents relationships between joint
angle movements and deviations of contact points. If fgrf can bemeasured by a force sensor, the inverse dynamics torques canbe computed from (9). However, this approach is undesirable
because the force sensors must be located at all contact points.
Therefore, we calculate the torques and the ground reaction
forces that correspond to the joint angles, the velocity, andthe acceleration by computing the QR decomposition of Jc[21], i.e.,
 
R
=
Q
(10)
J
c
0
where Q is orthogonal, and R is an upper triangle matrix ofrank k if given rank(Jc ) = k. We can decompose the dynamics(9) into two independent equations from (10), i.e.,
Sc Q (Mq̈ + h + g) = Sc Q S τ + Rfgrf 

Su Q (Mq̈ + h + g) = Su Q S τ .

(11)
(12)

Although (11) and (12) are independent, the full dynamics is represented with either equation. Equation (12) describes the full dynamics without ground reaction forces,
and the ground reaction forces are computed from (11),where S = [In×n 0n×6 ], Sc = [Ik×k 0k×(n+6−k) ], and Su =[0(n+6−k)×k I(n+6−k)×(n+6−k) ]. In×n is a unit matrix of then rows and n columns.

FURUKAWA et al.: EMG-DRIVEN WEIGHT SUPPORT SYSTEM WITH PNEUMATIC ARTIFICIAL MUSCLES

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D. Weight Support Force
Finally, the estimated joint torques are converted to the force
inputs for the PAM with using joint angle information measured
by the goniometers. The measured joint angles are used toderive the Jacobian matrix


∂yhip
∂yhip
J=
(13)
∂θknee ∂θankle
where yhip denotes vertical hip position, θknee denotes left knee
joint angle, and θankle denotes left ankle joint angle (see also
Fig. 3). By using the derived Jacobian, we convert the estimatedknee and ankle joint torques to the vertical support forcesτ̂fpam = (JJ )−1 J knee
(14)τ̂anklewhere τ̂knee and τ̂ankle are estimated left knee and ankle
torques, respectively.

Fig. 4. EMG electrode and goniometer placements.

IV. E XPERIMENTAL S ETUPS
Here, we introduce our experimental setups. In Section IV-A,
we explain a calibration procedure of the PAM system. In
Section IV-B, we show the four different setups of the squat
experiments.
A. Calibrations
1) PAM Pressure to Force Model: At the calibration stage,
we obtain load cell variables that measure the actual vertical
PAM force, the valve pressure, and the contraction rate byperiodically changing the loads. Using the least square method,
we estimate the PAM parameters in (2) with using the initialvalues in (3) and initialize encoder so that we can find an
initial length of PAM to calculate PAM contraction rate. For thequadratic regression of (2), we consider forces and contraction
rates as inputs and the valve pressure as output. Fig. 2 shows thefrequency responses of the PAM-based weight support system.
We obtained the data by generating support force with a range
from 100 to 300 N. The amplitude error and the phase error are
shown in Fig. 2.
2) EMG to Joint Torque Model: The parameter of thetendon-pulley model in (7) was estimated from squat motion
data for 30 s with different frequencies and depths. In otherwords, the number of samples used to find the parameters was
N = 7500. Fig. 4 shows the placements of EMG electrode andgoniometers. We measured EMG signals of the femoral muscle(Channel 1: e1 ), the biceps muscle of the thigh (Channel 2: e2 ),the vastus medialis muscle (Channel 3: e3 ), the vastus lateralis
muscle (Channel 4: e4 ), the tibialis anterior muscle (Channel 5:e5 ), the lateral head of the gastrocnemius muscle (Channel 6:e6 ), and the medial head of the gastrocnemius muscle (Channel
7: e7 ). The left leg pitch angles of hip, knee, and ankle joints
are measured by goniometers. Then, the measured joint angles
are used in the floating base inverse dynamics model.
We sample the amplified EMG and goniometer signals witha sampling rate of 250 Hz, i.e., sampling period is Δt = 4 ms.

Fig. 5. Torque estimation performances. The estimated (a) knee and (b) ankle
joint torques from EMG signals are indicated by solid lines. Target joint torqueprofiles derived from joint angle trajectory with using floating base inverse
dynamics model are plotted by dashed lines. The estimation performances areevaluated with using a test data set that is not used for finding the parameters of
the tendon-pulley model in (7).

To estimate the knee joint torque, we compute muscle
forces with an augmented input for bias estimation: f =(f 1 , . . . , f 7 , 1) from the EMG signals measured by the sevensensor channels. Similarly, the ankle joint torque is estimatedby three muscle forces f = (f 5 , f 6 , f 7 , 1) . f 1 is derived fromthe femoral muscle activity, f 2 is from the biceps muscleof the thigh, f 3 is from the vastus medialis muscle, f 4 isfrom the vastus lateralis muscle, f 5 is from the tibialis anterior
muscle, f 6 is from the lateral head of the gastrocnemius muscle,
and f 7 is from the medial head of the gastrocnemius muscle.
Fig. 5 shows torque estimation performances. This estimation
performances are evaluated with using a test data set that is not
used for finding the parameters of the tendon-pulley model in(7). The correlation between the predicted knee torques fromthe EMG signals and target torque trajectories was 0.86, andthe root-mean-square error was 33.9 N · m. The correlation
coefficient between the predicted ankle torques from the EMG
signals and target torque trajectories was 0.86, and the rootmean-square error was 8.62 N · m. These results indicate that
our torque estimation method from the EMG signals is useful
to control our PAM-based weight support system. Note that,since the knee joint torque is dominant in the squat movement,

1030

IEEE SYSTEMS JOURNAL, VOL. 10, NO. 3, SEPTEMBER 2016

Fig. 6. Experimental setups. (a) Normal two-leg squat. (b) One-leg squat
without using the assist system. (c) One-leg squat with the weight support. In
experimental setups (b) and (c), subjects are instructed to touch the wall tomaintain the balance. (a) Two-leg normal squat. (b) Left leg squat. (c) Left leg
squat + assist right side.

we mainly analyze the EMG signals around the knee joint in
Section V.
B. Assisting Squat Movements
We compared EMG magnitudes with four different experimental conditions: 1) normal two-leg squat; 2) one-leg squat
without the assist system; 3) one-leg squat with EMG-based
weight support; and 4) one-leg squat with constant force support, where the constant force is derived as the mean valueof target assist force calculated from the floating base inverse
dynamics. Fig. 6 shows the three squat experimental setups
used for the four different experimental conditions.
We set two different frequencies as lower and upper limits
for each squat in order to validate real-time assist performanceof the weight support system: 0.2 Hz for the slow dynamic
motion and 0.5 Hz for the fast dynamic motion. The lower limitthat the subject was able to squat continuously with using one
leg without weight assist was decided as 0.2 Hz. The upper
limit that was able to be accurately controlled with respect tothe response of the valve from the result of PAM calibrationsin Section IV-A was 0.5 Hz. Four healthy subjects took partin the experiments (ages 23–30, males). The subjects were
instructed to match the timing of squat motion to metronome
sound.
V. R ESULTS
Here, we show the results of real-time assist performance ofthe squat movements by using the developed weight support
system.
A. Individual Data Analysis
Fig. 7(a) and (b), respectively, shows mean and varianceof the knee joint angles of representative subject (subject A)

during one cycle of 0.2- and 0.5-Hz squat movements in thefour different squat conditions. (c-1)–(c-8) in Fig. 7(c) show themean and variance of EMG signals measured from the vastus
medialis muscle (e3 ) and the vastus lateralis muscle (e4 ) of
subject A during one cycle of 0.2-Hz squat movements in thefour different squat conditions, and (c-9) shows the box plotof the corresponding EMG magnitude. (d-1)–(d-8) in Fig. 7(d)show the mean and variance of EMG signals measured fromthe vastus medialis muscle (e3 ) and the vastus lateralis muscle(e4 ) of subject A during one cycle of 0.5-Hz squat movementsin the four different squat conditions, and (d-9) shows the box
plot of the corresponding EMG magnitude. Fig. 7(e) and (f)shows the EMG data of subject B. Fig. 7(g) and (h) shows the
EMG data of subject C. Fig. 7(i) and (j) shows the EMG dataof subject D. We focused on monitoring these two muscles, i.e.,the vastus medialis and vastus lateralis muscles, because theseknee-joint related muscles mainly contributed to generate thesquat movements. All the EMG profiles in Fig. 7 are low-passfiltered with a cutoff of 2 Hz and full-wave rectified.
As presented in Fig. 7(a) and (b), the subject generated squat
movements with similar amplitude and frequency in the four
different squat conditions. Fig. 7(c)–(j) shows the amplitudesof EMG signals in the four different squat conditions. These
results indicate that the magnitude of EMG signals in thenormal and the EMG-based assist conditions is less than that in
the one-leg condition, and that in the constant-force-based assist
conditions is little less. Table I shows the root mean square
T(RMS: (1/T ) 0 x(t)2 dt for 30 s, T = 30) of raw EMG
signals from seven muscles (e1 −e7 ) with the two different
movement frequencies of 0.2 and 0.5 Hz in the four different squat conditions. Results in Table I show that the vastus
medialis muscle activities (e3 ) and the vastus lateralis muscle
activities (e4 ) in the normal squat condition (i) and the one-leg
squat with EMG-based weight support (iii) are comparable ineach subject, whereas these muscle activities are much larger inthe one-leg squat without weight support condition (ii). These
two muscles in the one-leg squat with constant force support(iv) are also assisted, but not as much as EMG-based weightsupport.
On the other hand, for the other five muscles, we did notobserve consistent differences in EMG signals in the four
different experimental conditions among the four subjects (seealso Table I). For subject D, we found much larger muscle
activities on the tibialis anterior muscle e5 . This is possibly
because subject D intensively uses that muscle to maintain thebalance during the squat movements.

B. Integrated Data Analysis
Fig. 8(a) and (b) shows the average %MVC of the vastus
medialis muscle activities (e3 ) and vastus lateralis muscle
activities (e4 ) with the two different movement frequencies of
0.2 and 0.5 Hz in the four different squat conditions acrossall subjects. The %MVC is normalized muscle activity (e) for
each subject and each muscle separately, by using maximum
value of rectified and low-pass filtered EMG signal (emax ) :%MVC = e/emax . This allows comparing among different

FURUKAWA et al.: EMG-DRIVEN WEIGHT SUPPORT SYSTEM WITH PNEUMATIC ARTIFICIAL MUSCLES

1031

Fig. 7. Squat movement profiles of each subject with frequencies of 0.2 and 0.5 Hz among the four different experimental conditions with box plot. Blue lines
show normal squat, green lines show one-leg squat without weight support, red lines show one-leg squat with EMG-based weight support, and yellow lines show
one-leg squat with constant force support. (a) and (b) Mean and variance of knee joint angles of representative subject during one cycle of 0.2- and 0.5-Hz squat
movements in the four different squat conditions. (c)–(j) Squat movement profiles of the vastus medialis and vastus lateralis muscles with frequencies of 0.2
and 0.5 Hz.

subjects. emax is the maximum value of the muscle activity
e, which is calculated in each experiment and regarded as themuscle activity level of maximum voluntary contraction (MVC)
in this study. We applied a t-test to the average %MVC of
one-leg squat without the weight support (ii), one-leg squatwith the EMG-based weight support (iii), and one-leg squatwith the constant force support (iv) with reference to normal

two-leg squat (i). We found a significant difference between(i) and (ii) (p < 0.01) and between (i) and (iv) (p < 0.05),
but we found no significant difference between (i) and (iii)(p > 0.05) in the 0.2-Hz squat movements. In addition, we also
found a significant difference between (i) and (ii) (p < 0.01)
and between (i) and (iv) (p < 0.01), but we found no significant
difference between (i) and (iii) (p > 0.05) in the 0.5-Hz squat

1032IEEE SYSTEMS JOURNAL, VOL. 10, NO. 3, SEPTEMBER 2016

TABLE IRMS OF THE R AW EMG S IGNALS

movements. These results suggest that the developed weightsupport system can assist the left leg of the subjects with thesimilar force that the right leg is supposed to generate usinga PAM-based weight support system. Our system is controlled
by the force estimated from the EMG signals of the left leg,assuming that humans move both left and right legs with thesame force while squatting. The constant force support also
assisted, but the effectiveness was less than the EMG-based
weight support. Consequently, we showed the usefulness of our
proposed weight support system for actively assisting subjects
by using the measured EMG signals.
VI. C ONCLUSION
In this paper, we have introduced our newly developed PAMbased weight support system, which can be controlled by theestimated joint torques with the online EMG measurement. The
parameters of the torque estimation model are calibrated withusing an inverse dynamics model, which represents physicalproperty of a subject. In particular, we considered using floatingbase inverse dynamics to explicitly take the ground reaction
force into account so that we can treat the movements withboth feet on the ground such as squat behaviors. As a concrete
example, we applied our developed system to assist squat
movement. The results show that muscle activities measured
from vastus medialis and vastus lateralis muscles EMG signals
were not significantly different between one-leg squat with the
EMG-based weight support and normal two-leg squat, whereas
the differences between normal two-leg squat and one-leg squatand between normal two-leg squat and one-leg squat withthe constant force support were significant. As an application,
this system can possibly be used for partial weight bearing
therapies. Concretely, it can possibly be used for compensating
the disabled side of the body for the early stage of therapies.
Then, the vertical component force can be gradually decreased
as the patient recovers lower body motor functions.
Since the PAM-based weight support system is lightweightand safe, has high power-weight ratio, and is easy to attach

FURUKAWA et al.: EMG-DRIVEN WEIGHT SUPPORT SYSTEM WITH PNEUMATIC ARTIFICIAL MUSCLES

1033

Fig. 8. Statistical comparisons with bar plots of the vastus medialis muscle and the vastus lateralis muscle %MVC across all subjects. Blue bars show the normal
squat movement, green bars show the one-leg squat movement without weight support, red bars show the one-leg squat movement with EMG-based weightsupport, and yellow bars show the one-leg squat movement with constant force support. (a) 0.2-Hz squat movement. (b)0.5-Hz squat movement.

to the ceiling or a small gantry, the system can potentiallybe useful not only for rehabilitation but also for industrial
applications.
Although we focused on assisting one particular movement,i.e., squatting, it would be possible to apply our weight support
system to assist other kinds of movements such as steppingor walking by using the estimated torque from EMG signals
with considering phase difference between the left leg and rightleg movements. Moreover, we can possibly use our developed
weight support system with constant assist force to simply
compensate the gravity, but with much wider movement range
than existing devices so that the weight support system can helpa therapist when the therapist is nearby a patient. This gravity
compensation strategy can be also useful for the industrial
applications, such as the load carriage assistance.
To further improve the force control performance of theweight support, it can be beneficial to explicitly take hysteresis
model of the PAM into account. In addition, in this study, weused load cells only for the calibration and did not use it forforce feedback control since there is delay in the air-pressurebased control system. However, using the load cell feedbackfor online adaptation of the parameters of the weight support
system would be one of the interesting directions as a part of
future study.
A PPENDIX
In this appendix, we explain the Hill–Stroeve model used in(6). h(η i , ξ i ) and k(ξ i ) are computed as follows:h(η i , ξ i ) =1 + tanh(a1 η̇ i − a2 )i
− b1 e−2.6(ξ −1)b2k(ξ i ) = 0.32 + 0.71e−1.112(ξ −1)× sin 3.722(ξ i − 0.656)

(15)

i

(16)

i,max
˙
where ξ i = (li (θ)/li,n ) and η i = (ξ i (θ)/v), v i,max = 3.0,ii,nl (θ) is the current length of muscle i, l is its natural length,and v i,max is its maximum contraction rate. In this paper, thenatural muscle length is approximately set as the same tothe link length. The current muscle length is computed byadding partial periphery length of the joint pulley to the naturalmuscle length with considering the current joint angle. b1 =[1 − tanh{a1 (1 + a2 )}]/b2 , and b2 = tanh{a1 (1 + a2 )}. The
parameters a1 and a2 depend on the muscle type, and theapproximate range of values for a1 and a2 are a1 = 1.6−2.7
and 2.9–3.8 and a2 = −0.05−0.1 and −0.01–0.1 for fast andslow fibers, respectively. In this paper, we defined these parameters for each muscles experimentally by repeating the crossvalidation to minimize the error of torque estimation. The fmax
for the muscles were determined anatomical information [27];1
2
3= 975 N, fmax
= 3000 N, fmax
= 2250 N,
and we used fmax
45
6
7
fmax = 2250 N, fmax = 1125 N, fmax = 1875 N, and fmax=
1875 N.
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Jun-ichiro Furukawa received the B.E. and M.S.
degrees from Osaka University, Osaka, Japan, in2011 and 2013, respectively. He is currently working
toward the Ph.D. degree in the Graduate School of
Frontier Biosciences, Osaka University.
He is also currently with the Department of Brain
Robot Interface, ATR Computational Neuroscience
Laboratories, Kyoto, Japan. His research interests include machine learning, biosignal processing, computational neuroscience, brain–machine interface,and robotics.

Tomoyuki Noda received Ph.D. degree in engineering from Osaka University, Osaka, Japan, 2009.
In 2009, he joined the Institute for Neural Computation, University of California, San Diego, La
Jolla, CA, USA, as a Visiting Research Scholar. He
has been a Postdoctoral Researcher with ATR Computational Neuroscience Laboratories, Kyoto, Japan,
since 2010.
Dr. Noda was the recipient of the Best Video
Nominate at IEEE AAAI 2008, the Best Video at
LAB-RS 2008, in a development of a whole-bodyhumanoid robot with tactile sensation and compliant joints, and the Best Paper
Nominate Award at IEEE Humanoids 2012 in BMI controlled exoskeleton.

Tatsuya Teramae received the Ph.D. degree in engineering from Tottori University, Tottori, Japan,
in 2011.
He is currently a Researcher with the Department
of Brain Robot Interface, ATR Computational Neuroscience Laboratories, Kyoto, Japan. His researchinterests are optimal control and human–machineinterface.
Dr. Teramae is a member of The Society of Instrument and Control Engineers of Japan and The
Institute Of Electrical Engineers of Japan.

Jun Morimoto received the Ph.D. degree in information science from Nara Institute of Science andTechnology, Nara, Japan, in 2001.
From 2001 to 2002, he was a Postdoctoral
Fellow with The Robotics Institute, Carnegie Mellon
University, Pittsburgh, PA, USA. Since 2002, hehas been with Advanced Telecommunications Research Institute International (ATR), Kyoto, Japan,
where he was a Researcher in the Computational Brain Project, the International Cooperative
Research Project, Japan Science and Technology
Agency from 2004 to 2009 and is currently the Head of the Department of
Brain Robot Interface, ATR Computational Neuroscience Laboratories.