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Real-Time Remote Transmission of Multiple Tactile Properties
through Master-Slave Robot System
Takahiro Yamauchi, Shogo Okamoto, Masashi Konyo, Yusuke Hidaka, Takashi Maeno, and Satoshi Tadokoro

Abstract— Remote transmission of high quality sense of touch
requires the representation of multiple tactile properties andcompensation of communication delay. We developed a realtime remote transmission system that can deliver multiple
tactile properties using a master-slave robot system. First, we
assessed what type of tactile properties should be transmitted
and how to connect them in real time. Three tactile properties—
roughness, friction, and softness—were transmitted on thebasis of the real-time estimated physical properties of threemain wavelengths, a kinetic friction coefficient, and spring
constants, respectively. Tactile stimulations were generated insynchronization with hand exploration at the master side
by using local tactile generation models to compensate forcommunication time delay. The transmission of multiple tactile
properties was achieved by the integration and enhancementof our previously reported methods for vibrotactile displays
and tactile sensors. A discrimination experiment using different
materials showed the feasibility of the total system involving thethree tactile properties.

I. I NTRODUCTION
Cutaneous sensation is required in a number of applications such as a robotic surgery system and dexterous
telemanipulation system to transmit more realistic contact
information from a remote site. One important issue, however, is that cutaneous sensation requires the representationof multiple tactile properties because these are based on
multiple physical properties. For example, tactile information
is expressed using multiple adjectives such as rough/smooth,
soft/hard, sticky/slippery, and warm/cool. Another issue isthe asynchronous transmission between hand explorationand tactile feedback due to communication delay. Humantactile perception is closely related to hand exploration. The
decoupled relation between them adversely affects tactile
sensation. For example, the authors confirmed that a delay of
approximately 40 ms in tactile feedback in response to hand
movement caused changes in tactile sensation [1]. To solvethese problems, we present a feasibility study of a real-time
remote transmission system for multiple tactile properties.
Several studies have reported remote tactile transmission
systems that deliver tactile information detected by a remote tactile sensor to the operator. For example, tactile
transmission systems for a medical gripper or glove usedin minimally invasive surgery or palpation were reported[2] [3]. These studies aim to transmit pressure distribution
and total load in the contact area between the probe andT. Yamauchi, S. Okamoto, M. Konyo, and S. Tadokoro are with the
Graduate School of Information Sciences, Tohoku University, 6-6-1, Aramaki Aza Aoba, Aoba-ku, Sendai, Miyagi, 980-8579, Japan. {konyo,

tadokoro}@rm.is.tohoku.ac.jp
Y. Hidaka and T. Maeno are with the Graduate School of System Designand Management, Keio University, 4-1-1 Hiyoshi, Kohoku-ku, Yokohama,
Kanagawa, 223-8522, Japan

978-1-4244-5040-4/10/$26.00 ©2010 IEEE

body tissue. A tactile transmission system for a prosthetic
leg, which transferred the position of center of gravity andtotal load in the sole, was also developed [4]. A prosthetic
hand that transferred pressure in the hand to the user was
also developed [5]. Several master-slave-type robot systems
aim to transfer tactile information, such as pressure [6] andhigh-frequency vibrations [7] [8], from the slave hand tothe master side to support telemanipulation. Yamamoto etal. developed a master-slave system that transfers vibration
detected by a tactile sensor running on material surfaces
for the transmission of texture information [9]. The abovementioned studies basically focused on a special use and aparticular tactile property. On the other hand, Caldwell etal. [10] reported a transmission system for multiple tactile
properties. Their system delivered roughness on a textured
surface, vibration delivered from stick-slip friction, totalload, and temperature change to the operator. However, they
did not demand natural tactile sensation for the operator because the total load was symbolically replaced by a stepwise
vibration intensity. In addition, no studies have dealt with thecompensation of communication delay to synchronize tactile
stimulation with hand exploration.
The authors of this study have proposed tactile display methods using vibratory stimulation for representing
roughness, softness [11], and friction [12]. These methods
were based on frequency response characteristics of humanmechanoreceptors. It is expected that the superposition ofthese vibratory stimulations can represent multiple tactile
properties simultaneously. We have partly confirmed thatsuch superposition could represent several tactile feelings
such as those of cloth materials [11], in which the tactile display methods for each tactile property are not well connectedto the physical properties of the materials. In addition, theauthors have also developed a tactile sensor that can estimate
multiple physical factors, including wavelength of textured
surfaces, friction coefficients, and Young’s modulus, which
are related to the tactile properties [13].
In this paper, we report the first trial of an integration of
our tactile sensor and display systems. We develop a remotetransmission for multiple tactile properties through a masterslave robot system in real time. It is a key component for
the integration how to transmit the signals detected by thetactile sensor to the tactile display. We discuss what type oftactile properties should be transmitted and how to connect
them in real time. Real-time synchronization between masterand slave sides is a critical issue for tactile transmission,
as mentioned above. We apply a time-delay compensation
method, which has been proposed in a previous study [14].
In this method, tactile stimulation can be generated in

1753

Kinetic Information (e.g., Force, Position, Velocity, Acceleration)

Time Delay
Slave Arm

Master Arm
Human Operator

Roughness Signals
Vibrotactile
Stimuli

TactileDisplay

Friction Signals
Softness Signals

Tactile Sensor
Model for Perceived Roughness

Time Delay

Wavelength Estimator

Model for Perceived Friction

Time Delay

Spring Constant Estimator

Model for Perceived Softness

Time Delay

Friction Coefficient Estimator
Materials

Master Side

Fig. 1.

Slave Side

Block Diagram of Master-Slave-Type Tactile Transmission System for Multiple Tactile Properties

synchronization with hand exploration at the master side
by using a local tactile generation model. This paper isthe first report that applies the concept to multiple tactile
properties. To develop the transmission system, we enhance
the tactile sensing methods developed by the authors [13]to achieve more detailed properties robustly in real time.
We combine physical parameters estimated by the tactile
sensor with the tactile displayed methods developed by theauthors [11] [12] with several improvements. Finally, we
confirm the feasibility of the total system involving threetactile properties selected through a material discrimination
test.
II. S YSTEM D ESIGN
A. Selection of Multiple Tactile Properties
In general, haptic cues for object recognition are categorized into material properties and geometric properties [17].
Geometric properties represent volume or shape of theobject. This information can be provided by a force-feedback
device. This paper does not focus on force-feedback methods because many studies have been proposed for timedelay communication [15] [16]. On the other hand, material
properties are highly related to tactile properties. We, however, do not have the generalized classification for tactile
properties. Different classifications of tactile properties havebeen reported mainly because the classifications dependon the target material. For example, roughness, softness,and temperature are clearly used in recognition of material[17]. Hollins et al. reported that roughness, softness, andsticky/slippery are perceptual bases [18]. Yoshioka et al.
determined that these three factors explain 96%–97% of
the perceived variances of textures and that information on
stickiness has a high correlation coefficient between kinetic
friction coefficients of textures [19]. According to Shirado
et al., dry/wet is considered to be another possible tactile
property in addition to roughness and softness [20]. Notethat friction coefficients are related to both sticky/slipperyand dry/wet sensations [21]. On the other hand, Shirado etal. reported that friction coefficients are not attributed to
an independent tactile property because they affect several
tactile properties in a cross-sectoral manner [20].
In this study, we selected three tactile properties—
roughness, softness, and friction—which have reasonableagreement with conventional classification. Furthermore,these properties have been used in our tactile display andsensing methods. We attempted to combine both methods inreal-time transmission and to verify the tactile transmission
quality by testing discrimination scores of several materials
to confirm the feasibility of the combinations.
B. Synchronization of Hand Exploration and Tactile Feedbacks
In order to cancel the temporal gap between the operator’s
manipulation and corresponding sensory feedback, a local
model of the remote environment, such as geometric information, is constructed at the master-side system [15]. In thecase of tactile feedback, the local model includes physicalparameters of textures that affect tactile sensations. For
instance, a tactile-roughness transmission system constructs
a local model involving the surface wavelengths of textures[14].
In this study, we applied this method for friction and softness transmission in addition to roughness. We assumed thatthe physical parameters of friction and softness properties arefriction coefficients and spring constants, respectively. Fig. 1
shows a block diagram of the developed system. This system
has a standard force-reflecting-type force feedback for kinetic
information. In addition, the tactile sensor installed on theslave-side system estimates physical parameters of target
materials in real time. The estimated physical parameters
are transferred to the local models of the master-side system.
The local models continue to be updated by the transferred
physical parameters. In the master-side system, tactile stimuli
are generated by combining the hand movements of theoperator and local models. Since the tactile stimuli andhand movements are synchronized, there is no temporal gap
between the tactile feedback and the operator’s movements.
This method does not compensate for the delay of physicalparameters itself because it is affected by the estimation timeat the slave side and communication delay. If the remotematerial changes rapidly, the tactile feedback cannot follow
the change of materials. However, the change of material
usually corresponds to movement of the operator’s hand,which is not conducted at more than several Hz. As describedlater, our update rates of physical parameters are enough highcompared to hand movements (250 Hz for wavelengths andfriction coefficients and 125 Hz for spring constants).
C. Transmission between Measured and Displayed Signals
An important aspect of transmission is how to connect
measured signals at the tactile sensor with stimulations at thetactile display. As we mentioned in Section II-B, measured
signals should be converted into physical parameters at the1754

slave side to apply our compensation method for communication delay. Thus, the tactile display methods should be related
to the physical parameters. We considered the connection of
signals by dividing into each tactile property as follows.
For the roughness property, we have confirmed that vibrotactile stimulation based on surface wavelengths and hand
speed can represent the roughness sensation [14] (Section
IV-B and Section V-A).
For the friction property, the human perception mechanismof friction is not well understood. We have suggested thathigh-frequency vibrations on the finger skin generated bystick-slip phenomena can be a cue for understanding thefriction property [12]. We proposed a friction display methodthat reproduces approximate stick-slip transition based ona model of a single-DOF vibration system with Coulomb
friction. If the tactile sensor at the slave side could measure
the human-like stick-slip phenomena, the physical parameters in the approximate model should be transmitted to themaster side. However, it is very difficult to reproduce stickslip phenomena in the same manner as human skin because
the tactile sensor has different friction characteristics. In
this study, we selected friction coefficients measured by athree-axis force sensor as physical parameters (Section IVC). At the master side, the friction display model generates
stick-slip transition based on measured friction coefficientsand fixed material parameters, which were determined byobservations of contact between human skin and an acrylic
plate in a previous study [12] (Section V-B). In this sense,
the friction display in this study does not reflect real material
properties. However, we have confirmed that changes offriction coefficients can control the magnitude of perceived
friction sensation [12]. This means that the measured friction
coefficients can represent the magnitude of friction sensationeven using fixed material properties.
For the softness property, the human perception mechanism of softness is less understood. In this study, we assumed
that a spring constant represents the physical parameter ofstiffness, which is related to the softness property. Note that
Young’s Modulus, which was used in [13], does not reflect
the stiffness related to the geometric shape of a material;
the spring constant is better as a measure of stiffness. We
propose a real-time estimation method of spring constants atthe slave side (Section IV-D). For the display, we assumed
that the amount of activity of the SA I-type mechanoreceptor
is related to the softness property. This assumption canbe supported by results of a previous study that perceived
softness can be controlled by changing the contact area
against the pushing force [22] because human detection of
the contact area is highly related to the activity of SA I.
To control the activity of SA I at the master side, we used
the pressure display method that we proposed previously[11]. This method produces a static pressure sensation using
vibrotactile stimuli at a very low frequency (< 5 Hz) where
SA I is more sensitive than other mechanoreceptors. Thus,
the activity of SA I can be controlled by the amplitude ofthe low-frequency vibrations. However, this method cannot
be immediately applied to the tactile transmission system.
This is because the relationship between the estimated spring

Master-Side System

Fig. 2.

Slave-Side System

Schematic View of Master-Slave Systemconstant and the vibration amplitude is not clear. In thepresent study, we assumed a simple inversely proportional
relationship between the spring constants and the displayed
amplitudes (Section V-C). These relationships need to beelucidated in the future.
III. D EVELOPMENT OF M ASTER -S LAVE S YSTEM
We first provide an overview of the master-slave system.
Fig. 2 shows the developed master-slave system. For robot
arms, PHANToM Premiums (SensAble Technologies) wereadopted. The force feedback was conducted as a forcereflecting type based on a force sensor attached to theslave arm. A human mimetic tactile sensor, which is described in Section IV-A, was installed on the slave arm.
A tactile display, which was installed on the master arm,is required to generate single-DOF vibrations against finger
skin in the vertical direction with enough force. We haveconfirmed that even single-DOF vibrations can represent
multiple tactile sensations (pressure sensation, roughness
sensation, and friction sensation) by selecting vibration
frequency and controlling vibration amplitude in response
to hand movement [11]. In this study, a voice coil typespeaker (AURA, SOUND/NSW1-205-8A) was used for thevibrator. The speaker corn was covered by a 0.5-mm thickacrylic plate. Stimulation of finger skin was provided throughthe vibration of this plate. The vibrator was connected to
the robot arm with a gimbal mechanism, which allowed
operators to smoothly move their hands.
The detailed sensing methods and display methods foreach tactile property are described in Sections IV and V,
respectively.
IV. TACTILE S ENSOR AND S ENSING M ETHODS
A. Tactile Sensor
A tactile sensor for the tactile transmission system was
required for estimating surface wavelengths, friction coefficients, and spring constants, as described in Section II.
A tactile sensor that the authors developed [13] satisfies
these requirements. The sensor adopted in this study is ahemispherical version [23] of this tactile sensor, such thatthe sensor is able to scan the texture in its planar direction.
Fig. 3 shows the tactile sensor used in this study. The
sensor is composed of a hemispherical silicone rubber and

1755

Soft Inner Layer
Hard Outer Layer

0.1 mm

0.6 mmR = 9.75 mm

Strain Gauges

Fig. 3.

R = 11.25 mm

22.29

Sampled Data

11.76

㸼

40.0 mm22.0 mm

Acrylic Bone

Spring Constant [kN/m]

Force Sensor

Ki

Estimated
Spring Constant

4.71

1.0
0.8

vi

Normalized 0.6
Strain-Gauge
Output0.4

Schematic View of Tactile Sensor

Fn
0.2

a force sensor (NITTA, EFS-18M20A25-M10). The hemispherical rubber is formed as a hard outer layer and a soft
inner layer. The outer layer is covered with distal ridges withwidths of 0.5 mm and intervals of 0.6 mm close to thatof the epidermal ridges of a human finger. As transducers,
four strain gauges are embedded at the border of two layers
just beneath the distal ridges. The disposition of the gauges
was designed such that the gauges efficiently respond to thevibration of the ridges [13]. The strain gauges are connectedto the control computer by way of a strain amplifier. The
strain signals are sampled at 5 kHz.
B. Estimation of Surface Wavelengths
The estimation method for the surface wavelength of
textures is the same as the Fast Fourier Transformation(FFT) based method we used previously [14]. Because of
the surface roughness of the textures, the sensor vibrates
when scanning the textures. The strain gauges respond to
this vibration. By computing the FFT of the output voltagesof the strain gauges, the vibratory frequency of the sensor isacquired. The surface wavelength of the texture is determinedby λ = |v(t)|/f (t), where λ, v(t), and f (t) are the surface
wavelength, scanning velocity of the sensor, and vibratory
frequency of the sensor, respectively.
In order to estimate the vibratory frequencies of the sensor,
the computed power spectrum is used. N peaks are extracted
from the power spectrum. The frequencies corresponding toeach peak are f1 , f2 ..., fN , and the corresponding powers are
A1 , A2 ..., AN , where A1 > A2 ... > AN , which means thepower peaks are extracted in descending order. Ai is used for
determining the amplitude of the roughness stimuli in Section
V-A. We confirmed that this sensor could detect at least 0.2
mm wavelength when the velocity was 20 mm/s and thenormal force was 1.0 N. The estimation accuracy decreases
when the normal force is small and the hand movement isslow. Thus, the wavelengths and the powers were updated
only when the normal force was more than 0.2 N and thehand velocity was more than 50 mm/s. The update rate was
250 Hz.
C. Estimation of Friction Coefficients
The stick-slip model for the friction display, as describedin Section V-B, requires both kinetic and static friction
coefficients. However, it is difficult to estimate the static
friction coefficients because the developed tactile sensor has

Fig. 4.

0.4

0.6

0.8

1.0

Normalized Normal Force [N]Mapping Model for Estimating Spring Constantsa small sticking phase when stroking with materials. In thisstudy, we focused on transmitting the friction coefficients
rather than the reproduction of accurate stick-slip phenomena, as described in Section II-C. Thus, we assumed that theperceived friction is related to the kinetic friction coefficientand the static friction coefficients are simply proportional to
the kinetic coefficients.
Friction coefficients between the tactile sensor and materials were estimated using the force sensor. The friction
coefficients were determined by
|Ft |
,
|Fn |

(1)

µs = 2µk

(2)

µk =

where Ft and Fn are the lateral force and normal force
exerted to the sensor. The force data were sampled at 1 kHz,and the estimated values were determined by the average for20 ms. The estimation becomes unstable when the normal
force is small and the hand exploration is in turn-roundmotion. Thus, the friction coefficients were updated only
when the normal force was more than 0.2 N and the handvelocity was more than 40 mm/s. The update rate was 250
Hz.
D. Estimation of Spring Constants
When the tactile sensor is pressed into elastic objects, its
deformation depends on the elasticity of the objects and thenormal force applied to the sensor when the tactile sensor
has a constant elasticity. The magnitude of the deformationreflects on the outputs of the strain gauges embedded in thesensor. The authors previously proposed a method to estimate
the elasticity of target objects from the variance of the strain
gauge outputs [13]. However, the variance was susceptible tomanufacturing error in the arrangement of the strain gauges.
In the present study, we adopt a more robust method, using amapping table determined by a prior experiment as follows.
In order to prepare the mapping table for each strain gauge,
the sensor was thrust into three silicone rubber samples withknown spring constants (4.71, 11.76, 22.29 [kN/m]). These
spring constants were calculated as a reduced value regarding

1756

Estimated Spring Constant [kN/m]

25.0

adjacent estimated value is used as a current spring constant.
In this condition, the slave arm usually estimates the spring
constant only at the initial contact with the object. This canbe a limitation, but the estimation is done relatively rapidly
and robustly. The update rate of the estimation was 125 Hz.

1.0N
1.5N

20.0
Ideal

15.0

V. TACTILE D ISPLAY M ETHODS
10.0

A. Roughness Property

5.0
0.0
0.0

5.0

10.0

15.0

20.0

25.0

Spring Constant of Softness Sample [kN/m]
Fig. 5. Validation of Estimation Method for Spring Constants (Error bars
for 1.5 N are too small to be seen)

the shape of the samples as a cylinder 20 mm of thick and3.14 × 10−4 m2 in cross section, when Young’s Moduli of
the samples were 0.30, 0.75, and 1.42 MPa, respectively.
Thrusting forces varied from 0.2 N to 2.0 N by 0.2 N. Fig.
4 shows an example of the observed relationships between
spring constants of the samples, the normal forces, and theoutputs from a strain gauge. Measurement of each condition
was conducted just once because repeatability was high.
Fig. 4 shows that the gauge outputs increase as the normal
force and spring constants increase. Since the tactile sensor
has four strain gauges, the mapping table is constructed
for each strain gauge. The table is represented as K̂i =θi (vi , Fn ), where vi and Fn are the output voltage of the i-th
strain gauge and the normal force, respectively. θi linearlyinterpolates the points in the table, as shown in Fig. 4. For
example, when vi and Fn are measured by the tactile sensor,
three neighborhood points are found in the table. A planeg(K̂, v, F ) = 0 is formed by these three points. By solvingg(K̂i , vi , Fn ) = 0, K̂i is estimated. The final value of theestimated spring constant is determined by the average value
from the four mapping tables.
In order to validate this estimation method, we carriedout an experiment in which the sensor was moved in thenormal direction and thrust into the softness specimen madefrom silicone rubber. No lateral movement was applied tothe sensor. Fig. 5 shows the average and standard deviationof the estimated spring constant when Fn = 1.0 and 1.5
N. The maximum error of the estimation was 44%. Thiserror comes from a sparse setting of the spring constants of
the rubber samples for the mapping model. More gradualsampling of the spring constants will improve the accuracyof the estimation. The estimation is affected by the surface
shape of the contact area, which is considered a natural
behavior because human-perceived softness is also affectedby the contact area [22].
For remote transmission, when a lateral force is applied
to the tactile sensor, this estimation method is not available
because the estimation table was constructed from the dataacquired with no applied lateral force. Therefore, the estimation of the spring constant was performed only when
|Ft | < 0.3 N. When the estimation does not work, theAs described in Section II-B, a tactile-displaying method
and local model for the roughness property are basically thesame as a previously reported method [14]. The previous
method has successfully transmitted the roughness sensationsof grating scales with surface wavelengths varying from 0.8
mm to 2.0 mm by 0.2 mm. In this study, we extended
the previous method for multiple wavelengths of texture
surface. When the surface wavelengths consist of λ1 , λ2 ,...λN , deformations of the finger skin in stroking the texture
is approximated asy(t) =N∑

Ai sin (2π

i=1

x(t)
),
λi

(3)

where x(t), Ai , and N are the finger position on the texture,amplitude of skin deformation caused by the component of
λi , and the number of surface wavelengths the texture has,
respectively. λi and Ai are determined from FFT from theoutputs of the tactile sensor, which is described in Section VA. N affects the quality of the roughness stimuli transmitted
to the operator. In this study, N was set to 3 because ourpreliminary evaluation showed little difference in the qualityof roughness when N is larger than 3. The electric voltage
applied to the tactile stimulator is given byyr (t) = αy(t),(4)

where α [V] is a scaling constant to transform the roughness
stimuli to the voltage.
B. Friction Property
As described in Section II-C, the local model and tactiledisplaying method for the friction property are based on
our previous study [12]. In this study, we made several
improvements on the waveforms. Fig. 6 shows the conceptof the display method. The method represents the friction
sensation of the stick-slip transition by controlling the FA
II activities using high-frequency vibration, which is justsensitive to FA II. As shown in Fig. 6, at the transition from

Vibrotactile
Stimuli to Skin

400

Fig. 6. Concept of Displaying Method for Friction Sensation (modified
from [12])1757where Af is the amplitude of the vibratory stimuli and f1 isthe vibration frequency (400 Hz). Af is determined by

Af =Fig. 7.

Single-DOF Vibration Model for Simulating Stick-Slip Motion

the sticking phase to the slipping phase, FA II is stimulated
by a burst-like waveform vibration at the frequency of 400
Hz, at which frequency FA II is more sensitive than the othermechanoreceptors. In our previous study [12], the envelopeof the waveform had a step-wise shape. This caused an
unnatural feeling, similar to a flicking action. In this study,
we increased the amplitude of the vibration gradually (td1
period). After the peak, the envelope decayed gradually basedon the viscosity of the skin (td2 period). The peak amplitude
Amax depends on the elastic energy that is stored during the
sticking phase.
Fig. 7 illustrates the single-DOF vibration model witha Coulomb friction to generate the approximated stick-slip
transition at the finger skin, as described in Section II-C.
The mass in the model is coupled with the wall (Bone). Thismodel assumes the texture moves and the wall is connectedto the ground. x(t), V , and W are the displacement between
the mass and wall, the moving velocity of the texture, andthe normal force applied to the skin, respectively. µs and µk
are the static and kinetic friction coefficients, respectively. F
is the lateral force applied to the skin, which depends on thecontact status of the skin. When the skin is in the sticking
phase (ẋ(t) = V ), F is equal to the static friction force.
When the force exerted to the mass exceeds the maximum
static friction force, the contact status changes from the
sticking to slipping phase. This condition is described asmẍ + cẋ + k(x − x0 ) > µs W,(5)

Where x0 is the equilibrium position of the spring.
After the mass starts slipping, it vibrates if the system is
not over damping. In the slipping phase, F is equal to thekinetic friction force. Therefore, the equation of motion is
given bymẍ + cẋ + k(x − x0 ) + sgn(ẋ − V )µk W = 0,

(6)where µk is kinetic friction coefficient. When the velocity ofthe mass ẋ(t) becomes equal to V , the contact status changes
to be the sticking phase again. ẋ(t) in the slipping phase isanalytically given by solving (6) [12].
As mentioned in II-C, the physical parameters in the model
are substituted by the fixed parameter, which was determinedby the observation (using a high-speed camera and force
sensors) of the stick-slip phenomena of the human finger
skin in contact with an acrylic plate [12]. m, c, and k were1 × 10−6 kg, 1 × 10−5 Ns/m, and 2 N/m, respectively.
The electric voltage applied to the tactile stimulator at the
stick-to-slip transition isyf (t) = Af sin (2πf1 t),

(7)

8
>
>
>
<

Amax
(t − ts )
td1

>
Amax
>
>
(t − ts − td2 )2:t2d2

if (t − ts ) < td1(8)
if td1 < (t − ts ) < td2 ,where Amax and ts are the maximum amplitude of vibratory
stimuli, and the time at which the stick-to-slip transition
occurred, respectively. td1 and td2 are constant values todetermine the decay-period of the stimuli. They were set to 5
and 30 ms in our preliminary adjustments. These parameters
should be optimized in the future. Amax is defined to be
proportional to the restoring force of the spring. Amax isgiven by
Amax = βk(x − x0 ),(9)where β [V/N] is a scaling constant to transform the vibratory stimuli to the voltage applied to the stimulator.
C. Softness Property
As described in Section II-C, a tactile display method
for the softness property is based on stimulation of SA I,
assuming that SA I is related to the perception of the contact
area, which corresponds to human-perceived softness [22].
This method controls the activity of SA I using the amplitudeof vibrotactile stimuli, which is low-frequency (< 5 Hz).
When the spring constant of the object becomes small, thecontact area of the finger skin increases and the population of
the activated SA I is also expected to increase. In this study,
we assumed that the relationship between the spring constantand the amount of SA I stimulation is inversely proportional.
The actual relationship should be determined in the future.
The vibrotactile stimuli for softness sensations ys (t) is
determined by,γys (t) =sin (2πf2 t),(10)
K
where K is the spring constant of the object. f2 is thevibration frequency (f2 = 5 Hz), and γ [104 × V N/m] is ascaling constant to transform the vibrotactile stimuli to thevoltage applied to the stimulating actuator.
D. Integration of All Properties
For integrating all the tactile properties, the superpositionof eqs. (4), (7), and (10) is applied for the electric voltage
V (t) to the voice coil as follows:
V (t) = yr (t) + yf (t) + ys (t),(11)

where yr (t), yf (t), and ys (t), are the displayed signals forroughness, friction, and soft properties, respectively.
The magnitude of each perceived property is adjusted by
the scaling constants, which are α, β, and γ in eqs. (4), (7),and (10), respectively. In order to present a natural feeling tothe operator, we should select the optimal scaling constants.
In this study, however, the scaling factors were determinedby an expert who is an author of this paper and has goodtactile sensitivity because the purpose of this study was toconfirm the feasibility of a combination of selected multiple
tactile properties. As a result, we determined that α = 0.4,
β = 3.5, and γ = 2.0.

1758

TABLE I
A NSWER RATINGS OF MATERIAL DISCRIMINATION EXPERIMENT

Displayed
Material

Embossed Paper(unevenly rough and hard)
Grating Scale(uniformly rough and hard)
Copying Paper(flat and hard)
Fleece Fabric(smooth and soft)
Boa Fabric(rough and soft)

Embossed Paper

Grating Scale

Answer
Copying Paper

Fleece FabricBoa Fabric

0.90 ± 0.10
(0.23 ± 0.06)
0.13 ± 0.23
(0.23 ± 0.12)
0.00 ± 0.00
(0.21 ± 0.10)
0.00 ± 0.00
(0.10 ± 0.00)
0.00 ± 0.00
(0.17 ± 0.06)

0.10 ± 0.10
(0.10 ± 0.10)
0.73 ± 0.15
(0.27 ± 0.15)
0.00 ± 0.00
(0.03 ± 0.06)
0.00 ± 0.00
(0.13 ± 0.06)
0.03 ± 0.06
(0.03 ± 0.06)

0.00 ± 0.00
(0.53 ± 0.15)
0.07 ± 0.06
(0.17 ± 0.06)
0.57 ± 0.21
(0.31 ± 0.12)
0.20 ± 0.10
(0.17 ± 0.21)
0.07 ± 0.06
(0.07 ± 0.12)

0.00 ± 0.00
(0.10 ± 0.00)
0.03 ± 0.06
(0.27 ± 0.15)
0.27 ± 0.21
(0.41 ± 0.20)
0.70 ± 0.10
(0.43 ± 0.21)
0.20 ± 0.10
(0.17 ± 0.06)

0.00 ± 0.00
(0.03 ± 0.06)
0.03 ± 0.06
(0.07 ± 0.06)
0.17 ± 0.21
(0.03 ± 0.06)
0.10 ± 0.10
(0.17 ± 0.06)
0.70 ± 0.17
(0.57 ± 0.15)

Values in parentheses are the answer ratings for the master-slave system with force feedback only. Gray cells indicate correct answer ratings.

VI. E XPERIMENT: M ATERIAL D ISCRIMINATION

B. Experimental Results

An experiment in which the participants explored and discerned materials through the developed master-slave system
was performed. It is important to evaluate the transmissionof each tactile property thoroughly. However, before we
conduct the detailed experiments and analysis, we needed
to confirm the total potential for our selection of tactile
properties through a feasibility study. This study evaluatedthe total performance for material discrimination involving
all three tactile properties. As a reference, we also compared
our results with the performance from force feedback without
tactile feedbacks.
A. Experimental Procedures
Using the tactile transmission system developed as described in Section III, an experiment to discriminate five
types of materials was conducted. The experiment was performed under two different feedback conditions. Under the
first condition, both the tactile feedback and force feedback
were available, while in the second condition, only the force
feedback was available.
The participants explored five types of materials throughthe developed master-slave system. Five materials were an
embossed coated paper (unevenly rough and hard), an acrylic
grating scale with a surface wavelength of 1.5 mm (uniformlyrough and hard), a piece of copy paper (flat and hard), a
fleece fabric approximately 3 mm thick (smooth and soft),
and a boa fabric approximately 7 mm thick (rough and soft).
The participants were instructed to identify the material
that they felt was closest to the one they explored through themaster-slave system. The participants were not allowed to see
the slave side and heard a pink noise over the headphones.
As comparison stimuli, the same materials were prepared inorder for the participants to freely explore the materials with
their bare fingers during the experiment.
For each material, 10 trials were performed. In total,each participant performed 50 trials. After the first 25 trials,
the participants took a 5-minute break. No time limit was
set for the trials. The material was displayed in a random
order. The participants practiced before the main trials forapproximately 3 minutes. The number of participants wasthree. All the participants were studying haptic research andhad previous experience in using tactile displays.

Table I lists the average answer ratings and the standard
deviation of the experiment for each material. Gray cells
indicate the correct answer ratings. Values in parentheses are
the ratings for the force feedback only system.
The correct answer ratings with the tactile feedback systemshow good potential over 70% except for the paper (57%).
From the results, we confirmed the feasibility of the total
performance of the integrated multiple tactile properties. The
correct answer ratings, however, depended on the materials.
Optimization of each tactile display and of the sensing
methods is required. In addition, there is a possibility thatthe three tactile properties selected are not sufficient fortransmitting total touch feelings. Further investigation isneeded in the future.
The correct answer ratings for the tactile feedback systemtend to be higher than those for the force feedback system.
In particular, in the force feedback condition, the correct
answer ratings for the embossed paper and grating scale wereapproximately at chance level, while in the tactile feedback
condition, the correct answer ratings for these materials
were much higher. A two-way ANOVA was applied to thecorrect answer ratings, with the factors being a type of
feedback and a type of material. The test showed that there
was a significant difference in the correct answer ratings
between the force feedback and the tactile feedback systems(F (1, 20) = 40.5, p = 3.29 × 10−6 ), with no significant
interaction between the two factors.
VII. C ONCLUSIONSIn this study, we developed a tactile transmission systemthat transmits multiple tactile properties of roughness, softness, and friction sensations from remote environments. We
report the first trial of the integration of our tactile display
methods and sensing methods for multiple tactile properties. We discussed the system design from the viewpointof selection of multiple tactile properties, synchronizationof hand exploration and tactile feedback, and transmission
method between measured and displayed signals. In order
to compensate for communication time delay for the threetactile properties, local models and display methods thatinvolved physical parameters and explored the movementsof operators were constructed. We also enhanced the tactile
sensing methods proposed in our previous studies to achieve
more detail in real time.

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Fig. 8.
World’s First Demonstration of Remote Tactile Transmission
between Tokyo and Sendai

To confirm the feasibility of the total system involving thethree selected tactile properties, we performed an experimentpertaining to material discrimination. The results showed
the potential for discrimination to be mostly over 70%of the correct answer ratings. The detailed evaluation andanalysis of the potential of individual tactile properties will
be reported in our next paper.
We demonstrated the performance of the tactile transmission system in an actual remote environment at the 14th
Annual Conference of the Virtual Reality Society of Japan(September 9–11, 2009, Waseda University). Fig. 8 showsa photograph of the demonstration. We were able to deliver
multiple tactile properties from Tohoku University in Sendai
to the operators at Waseda University in Tokyo; the direct
distance between these two locations is 300 km. As forthe communication delay, the round-trip time of the PING
command was 20 – 25 ms, which was relatively stable. Many
operators, who were participants at the conference, could
discriminate between the touch of different materials (fleece,
embossed paper, fake leather, etc.) placed at the remote site.
To our knowledge, this demonstration is one of the world’s
first remote tactile transmission that includes multiple tactile
properties.
ACKNOWLEDGEMENTS
This study was partially supported by a Grant-in-Aid for
Scientific Research B (18760179) and a grant from the
Ministry of Internal Affairs and Communications SCOPE(082102006).
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