MASSAGE CONTROL TO ADAPT HUMAN SKIN MUSCLE

CONDITION BY USING MULTIFINGERED ROBOT HAND

Kazuhiko Terashima, Taku Kondo, Panya Minyong, Takanori Miyoshi

Department of Production Systems Engineering, Toyohashi University of Technology

Hibarigaoka 1-1, Toyohashi, 441-8580, Japan

Hideo Kitagawa

Department of Electronic Control Engineering, Gifu National College of Technology

Kamimakuwa, Motosu, Gifu, 501-0495, Japan

Keywords:

Human skin muscle model, multi-ﬁngered robot hand, massage control.

Abstract:

The purpose of this paper is to propose the adaptive expert masssage robot using a multi-ﬁngered robot.

Towards this goal, the present paper gives a modeling of human skin muscle through robot perception of

impedance, and control strategy using impedance control to implement adaptive control system, even if human

condition is changed. The model validity is demonstrated via many experiments by using multi-ﬁngered robot

hand and human body. Based on robot perception of human muscle impedance, impedance control is proposed.

1 INTRODUCTION

In present society, there are many health support

machine such as massage machines (M. Okada and

Oka, 2004), (S. Kajikawa, 2004), (example: http://

www.mew.co.jp /wellness /momimomi /realpro2 /in-

dex.html, ). Especially, in Japan, highly developed

massage machine was produced. Many pattern of

massage motion are installed according to body con-

dition and human preference, and adjusted by manu-

ally switching. Further, massage motion of most mas-

sage machine is realized by using roller’s movement

and swing. Then, the movable places for massage by

the present machine is limitted, and it is expected to

extend the possible region to conduct the massage.

Therefore, the development of ﬂexible massage robot

by using multi-ﬁngered hand is a challenging subject,

in recent decades.

Authors presented feedforward-type and Neural

Network (NN’s) (H. Kitagawa and Terashima, 2002)

massage motion control for human shoulder by off-

line learning in TUT (Toyohashi University of Tech-

nology) robot hand. This research described how a

two ﬁngered hand was applied, but results of force

and position control were insufﬁcient, because a feed-

back controller was not included due to the lack of a

force sensor. Therefore, the massage motion of this

hand was too limited. In the literature (K. Terashima

and Kitagawa, 2005), (P. Minyong and Terashima,

2003), position control was used before ﬁngertip of

robot hand touches to shoulder, and after touching,

controller was switched from position control to force

control. Reference massage force was taught by ex-

pert therapist, and those data were memoried into

computer by using sheet sensor. These teaching data

were realized by robot hand using teaching-playback

method. Reference force was exactly achieved by us-

ing fedback control. Precision of reproduction by ro-

bot of expert massage of therapist was well realized

(K. Terashima and Kitagawa, 2005), (P. Minyong and

Terashima, 2003). However, in the previous system,

reference massage motion must be taught by thera-

pist’s teaching whenever the change of human body

condition and massage position occurred.

Hence, development of auto-tuning adaptive mas-

sage robot is expected to appropriately adjust mas-

sage motion following to the impedance of human

skin muscle. Thus, in this paper, we present a model

of human skin muscle by using multi-ﬁngered robot

hand to know impedance of human skin muscle and

control strategy by means of impedance control to im-

plement adaptive control system, even if human con-

dition is changed, or massage position is shifted, and

person to be massaged is different.

2 MASSAGE ROBOT SYSTEM

The multi-ﬁngered, multi-jointed humanoid robot

hand is shown in Fig. 1. It has 4 ﬁngers with 13 joints.

The 1

ﬁnger (thumb) has 4 joints, and the 2

to 4

407

Terashima K., Kondo T., Minyong P., Miyoshi T. and Kitagawa H. (2005).

MASSAGE CONTROL TO ADAPT HUMAN SKIN MUSCLE CONDITION BY USING MULTIFINGERED ROBOT HAND.

In Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics - Robotics and Automation, pages 407-410

DOI: 10.5220/0001172304070410

 SciTePress

Figure 1: TUT hand with 6-

axis force sensor

Figure 2: Massage motion

control by robot hand

ﬁngers have 3 joints and are arranged like those of the

human hand. The thumb is opposable and redundant.

It has 203.9 [mm] length and 222.2 [mm] width, about

1.2 to 1.5 times larger than an adult man’s hand.

The small AC servo motor actuator for the robot

hand is 30 [mm] in diameter, 30 [mm] length, 70

[g] in weight, and generates 1.4 [Watts]. The small

sized-motor was manufactured by the Yaskawa Elec-

tric Corporation. The servomotor has an integrated

harmonic gear (1/80) and encoder, and directly drives

each joint. The ﬁngertip force sensor is the ﬁnger-

tip type of 6-axis force sensors made by BL Au-

totech Ltd. By using this sensor, three components of

force (F

) and three components of momentum

) could be measured.

The typical kinds of ﬁnger movements performed

by an expert massage therapist consists of ”pushing,

”picking up”, and ”rubbing”.

”Pushing” is done strongly by thumb, while the

other ﬁngers are used to support the person being

massaged. The tips of the other ﬁngers touch the body

while the tip of the thumb is placed on the shoulder

and pushes toward the tips.

As the ﬁrst step on designing an expert massage

robot, the ﬁngertip force control of ”Pushing” was

achieved by robot hands described in this paper

3 MODELING OF HUMAN SKIN

MUSCLE AND PARAMETER

ESTIMATION

As human skin model, a lot of models are proposed by

using viscosity-elastic theory. However, each model

is insufﬁcient, and therefore it is impossible to com-

pletely explain all phenomena of muscle state by one

model (Yamada, 1970), (R. Kenedi, 1965).

Then, in this paper, we shall consider spring-mass-

damper model with the correction term of error, and

also consider to investigate the state of human skin

muscle in real time.

Skin muscle model is represented by

f(t) = d + Kp(t) + C ˙p(t) + M ¨p(t) (1)

, where f(t) ∈ R

; ﬁngertip force of robot hand,

p(t) ∈ R

; ﬁngertip position of robot hand, K; spring

coefﬁcient, M; mass weight, C; damping coefﬁcient,

and d; deviation.

If ”(1)” is discretized, then it follows

= Θ

(2)

, where

= f

+ 2f

k−1

+ f

k−2

∈ R

, ψ

[1, p

, p

k−1

, p

k−2

] ∈ R

, Θ = [4d, L

, L

]

= K +

, L

= 2K −

, L

K −

and k is a time step at time kT .

Then, the forgetting factor is given by

k,i

= r

k−1,i

(k > i) (3)

k,i

= 1 − r

, r

= 2

−∆u

∆u

= min



k p

− p

k−1



(4)

, where T is sampling time, and T

and X

are design

parameter. In this research, we used T = 1 [ms], T

= 0.1, X

= 0.015.

Here, performance index to determine the esti-

mated parameter Θ = [4d, K, C, M]

is given by

(Θ) =

i=i

k,i

(φ

− Θ

)(φ

− Θ

)

= Θ

Θ − Θ

− Q

Θ + F

(5)

, where R

i=i

k,i

, Q

i=i

k,i

, F

i=i

k,i

, and i

a starting time to estimate the parameter.

Explanation about the forgetting factor of ”(3)” and

”(4)” follows. When motion is fast, the position shift

will be large every sampling, while the position shift

will be small if motion is slow.

Thus, it is thought to be reasonable that the data

should be forgotten in constant rate if the position

shift is large. On the other hand, the past data should

be stored during long interval without forgetting soon

if the position shift is small in every sampling.

Parameter is estimated such that J is minimized.

The details of deriving parameter estimation is writ-

ten in the literature (Kikuuwe and Yoshikawa, 2003)

for robot perception of impedance which is a kind of

Least Square Method.

Then, the parameter estimation value

becomes

= R

−1

∈ R

(6)

Here, if we put

′

= [d, K, C, M ]

, the

following equation is obtained, and the parameter

{d, K, C, M} can be identiﬁed.

′

= T

(7)

ICINCO 2005 - ROBOTICS AND AUTOMATION

408

Figure 3: Simulation results to estimate M, K, C and d

Figure 4: Measurement position of human body

, where

T ,







1/4 0 0 0

0 1/4 1/4 1/4

0 T

/4 0 −T

0 T

/16 −T

/16 T

/16







(8)

Fig. 3 shows the simulation results to estimate the

parameters of ”(1)”. Reasonable estimation results

were obtained by the present identiﬁcation method.

Then, each parameter of M, K, C and d were well es-

timated as shown in Fig. 3

Fig. 4 shows the measurement position to measure

the impedance of human arm (a) and hand (b). Posi-

tion (a) is the hard side of human hand, and position

(b) is the soft part of human arm. Experimental re-

sults to check the model validity were shown in Fig.

Reference input force with amplitude of 5 [N] and

period of 1.884 [rad/sec] was given from robot ﬁnger

to human skin muscle in experiments.

Table 1 shows the estimated values of K,C,M and

d for the measured position (a) and (b), while Fig. 5

shows the comparison between observation value in

real experiments and estimated value calculated from

Table 1: Estimated parameter of human skin muscle for

each position

K C M d

(b) 879.9 27.61 0.0128 -17.31

(a) 233.29 11.41 0.0015 -2.38

model of ”(1)” using the estimated parameter. From

the results, model validity was shown.

500

K [N/m]

C [Ns/m]

0.02

M [kg]

d [N]

4 6 8 10 12

Time[s]

0 2 4 6 8 10 12

Time [s]

Force[N]

Figure 5: Experimental results of model validity (at the po-

sition (a))

4 CONTROL STRATEGY OF

MASSAGE ROBOT

Control strategy of expert massage robot which can

adapt for human muscle condition is shown in Fig. 6.

Human muscle condition by robot perception of

impedance is estimated, and then based on the mus-

cle condition, controller is suitably selected. For ex-

ample, strong impedance control is executed for the

hard muscle, while weak impedance control for the

soft muscle. Impedance of human muscle is mea-

sured by means of force and position’s information

using multiﬁngered robot hand in short sampling pe-

riod such as 10 [msec]. Furthermore, sense informa-

tion such as blood pressure, cardiac rate, brain wave,

etc is measured in long sampling period such as 60

[s], and hence, massage control will be appropriately

adapted by feedback of both impedance and sense in-

formation.

As a basic study to implement a ﬁnal goal shown

in Fig. 6, impedance control was designed, where the

impedance controller is shown as controller i (i = 1,

2, ... n).

Impedance characteristics of impedance control is

given by

x + D

(

x −

−

) + K

(x − x

− x

)

= K

(f − f

)

(9)

, where f ∈ R

is ﬁngertip force, and x ∈ R

x ∈ R

, are ﬁngertip position, velocity, acceleration,

respectively. f

∈ R

is reference force, x

∈ R

MASSAGE CONTROL TO ADAPT HUMAN SKIN MUSCLE CONDITION BY USING MULTIFINGERED ROBOT

HAND

409

Figure 6: Schematic diagram of adaptive control system for

developing expert massage robot

0 2 4 6 8 10

-0.1

0.1

0.2

0.3

0.4

0.5

0.6

Torque [Nm]

Time [s]

Motor 1

Motor 2

Motor 3

Motor 4

(a) Torque of each motor

0 2 4 6 8 10

-1

x-axis

0 2 4 6 8 10

-1

y-axis

0 2 4 6 8 10

-6

-4

-2

z-axis

Time [s]

Reference

Simulation

Force [N]

(b) Force of fingertip

0 2 4 6 8 10

0.1

0.2

x-axis

0 2 4 6 8 10

0.1

0.2

y-axis

0 2 4 6 8 10

-0.1

-0.05

z-axis

Time [s]

Reference

Simulation

Position [m]

(d) Trajectory of fingertip

0.1

0.2

0.05

0.1

0.15

0.2

0.25

-0.1

-0.08

-0.06

-0.04

Position of x-axis [m]

Position of z-axis [m]

Reference

Simulation

Position of y-axis [m]

Figure 7: Simulation for massage motion of 1

ﬁnger

reference position trajectory until robot ﬁnger touches

human skin muscle and x

∈ R

is reference position

trajectory after robot ﬁnger touches human skin mus-

cle.

Then, control law having the reference impedance

characteristics is given by

τ =M(θ)J

−1

(f − f

) − D

(

x −

−

)

− K

(x − x

− x

)} −

θ +

h(θ,

θ)] − J

(10)

, where M(θ) is an inertia term of robot hand,

h(θ,

θ) is

a nonlinear term of centrifugal force, coriolis, gravity

and friction term, and J is a Jacobian matrix. K

is a

feedback gain as K

= diag[0.2225, 0.2225, 0.2225].

As a force reference, sinusoidal wave such as mag-

nitude in z-direction is 3 [N] and frequency is 0.3 [Hz]

was given. Control simulation using two ﬁngers was

conducted. In each ﬁnger, the same impedance char-

acteristics was given by considering the massage mo-

tion of expert therapist. The control results are given

in Fig. 7, where Fig. 7 is for the 1

ﬁnger.

In this simulation, the model parameter of human

skin muscle for 1

and 2

ﬁnger was given by

M = 0.001, K = 360, C = 18, d = 0. Under this

model, x

is calculated when f

is given. Further-

more, M

= I, K

= diag[1000, 1000, 1000] and D

= diag[61.73, 61.73, 61.73].

From Fig. 7, position and force output well agreed

with reference values. Massage motion was well

achieved to realize the ideal impedance according to

the impedance of human skin muscle in this time.

5 CONCLUSIONS

In this paper, modeling of human skin muscle and pa-

rameter identiﬁcation has been presented for the pur-

pose such as exactly know the state of human mus-

cle and conduct adaptive massage according to im-

pedance information based on the model of human

skin muscle. Model validity has been demonstrated

through many experiments using a lot of position in

human body. Adaptive control strategy for imple-

menting an expert massage robot to be adjusted for

various conditions has been proposed.

ACKNOWLEDGMENTS

This work was partially supported by the 21

Century

COE Program ”Intelligent Human Sensing”

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