Proceedings of the 2007 IEEE/RSJ International
Conference on Intelligent Robots and Systems
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WeD7.2

Roughness Feeling Telepresence System on the Basis of Real-time
Estimation of Surface Wavelengths
Shogo Okamoto, Masashi Konyo, Takashi Maeno and Satoshi Tadokoro
Abstract— Tactile telepresence has been expected to be technology which encourages operators of robotic systems to remotely maneuver objects or recognize materials being touchedthrough robotic arms. In remote environments, temporal disparity in tactile sensations is caused by temporal latency
between a touch motion of the operator and a tactile stimulus
applied on him. A framework of tactile telepresence systems,
which is suitable in remote environments, has been proposedin the present paper. In the framework, a tactile display locallygenerates the tactile stimulus in synchronization with the touch
motion of the operator in order to cancel communication delay.
The tactile stimulus is generated by combining the motion andtactile factors, which are sensed by a tactile sensor. The factors
are characteristic parameters of objects and effect on tactile
sensations. In the present paper, physical parameters of theobjects are selected as the factors because the tactile stimuli canbe computed using physical models involving the motions andthe parameters. Based on the proposed framework, a roughness
feeling telepresence system was implemented and successfullytransferred a roughness factor to the operator.

I. INTRODUCTION
Tactile telepresence technology, which enables operatorsof robotic systems to feel sense of touch like roughness
or softness feelings, has been expected to promote robotic
solutions in various fields. For instance, tactile information
processing would allow operators of remote manipulation
systems to detect incipient slippage on holding objects or torecognize materials of objects. The final aim of the present
project is transferring to operators realistic tactile sensations
which are acquired by robots in remote environments.
In general, a tactile telepresence system is composed oftactile sensors which sense tactile information instead ofhuman operators, tactile displays which artificially synthesize
cutaneous sensations, and a master-slave robotic system
which connects the sensors and the displays.
As related works, palpation systems for minimally invasive surgery [1][2]!$the systems which transferred softness
feelings [3] and texture sensations [4] were reported. These
related works can be classified into the same group from thepoint of view of information communication from the tactile
sensor systems to display systems. In these systems, tactile
sensors detected physical phenomena which occured at thesensor side, such as deformation of the sensor or states ofcontacts. Tactile information sensed by the tactile sensors
S. Okamoto, M. Konyo and S. Tadokoro are with Graduate
School of Information Sciences, Tohoku University, 6-6-01, Aramaki
Aza Aoba, Aoba-ku, Sendai, 980-8579, Japan {okamoto, konyo,
tadokoro}@rm.is.tohoku.ac,jp
T. Maeno is with Department of Science and Technology, Keio
University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, 223-8522, Japanmaeno@mech.keio.ac.jp

1-4244-0912-8/07/$25.00 ©2007 IEEE.

was sequentially transferred to the display side systems
through networks. Then tactile displays reproduced similar
physical phenomena on the fingers of operators.
These existing technology has not considered active touchin remote environments. Even if the existing methods wereapplied to tactile telepresence in remote environments, latency between a touch motion of the operator and tactile
feedback would complicate active touch. Latency causes
temporal disparity in tactile sensations. For instance, if an
operator could not sense any tactile feedback when hestarted his motions, subjective wrongness would occur in his
sensations. Wrongness is caused by a mismatch between the
motion and the tactile stimulus.
This study has proposed a new framework which is suitable in remote environments and solves the above problem.
In the framework, stimuli generation by a local loop in thedisplay side system cancels sensory mismatches between
kinetic and tactile sensations. Because the local loop recomputes the tactile stimuli based on the motions of the operator
and tactile factors which are sensed and transferred by thesensor. The tactile factor is any information, from which thestimuli can be computed by combining with the motionsin the display side system. In this study, the tactile factors
are physical parameters of objects since generally physicalrelationships between the physical parameters, the motions
and the stimuli can be utilized.
In implementing the framework, it is preferable that physical parameters of target objects are estimated in real-time.
Because once physical parameters are estimated and transferred to the display system, then the display can produce
tactile stimuli without latency between the hand movementsof an operator, however, until they are transferred proper
stimuli cannot be generated. The framework cannot resolve
problems of communication time-delay themselves; however
temporal disparity in tactile sensations due to latency can becanceled. The framework is considered to be effective whenmaterials of the object are not frequently varying.
A prototype of a roughness feeling telepresence systems
has been developed following the proposed framework. According to early psychophysical studies, the relationships
between physical parameters and roughness sensations areclearer than other tactile sensations, which helps the discussion to concentrate on the effectiveness of the proposed
framework. However, the proposed framework can be applied to other tactile sensations like softness, moistness andthermal feelings.

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II. D ESIGN OF ROUGHNESS F EELINGS T ELEPRESENCES YSTEM BASED ON R EAL - TIME E STIMATION OF
S URFACE WAVELENGTHS
A framework for the proposed tactile telepresence system
in remote environments is described in this section.
In the framework, a hand of an operator and a robotic arm
are connected by a master-slave system so that the systemachieves tactile sensing according to his hand movements.
The human hand is equipped with a tactile display, and atactile sensor is installed on a tip of the robotic arm. As
the human hand moves, the robotic arm moves and thetactile sensor estimates physical parameters of target objects.
Estimated parameters are continuously transferred to thedisplay side system and the tactile display applies stimuli
on the operator.
Tactile sensations occur when mechanoreceptors detect deformation of finger tissue. Factors which cause deformation
are kinetic properties such as rubbing speeds or pressing
force and physical properties of the objects such as spatial
frequencies or Young’s modulus. According to the psychophysical studies on roughness sensations, when humans
scan the roughness samples like the ones adopted in thisstudy, their subjective sensations are almost determined bygroove widths and surface wavelengths of the samples [5][6].
Hence, the present study has selected surface wavelengths as
one of the roughness factors.
Fig. 1 is a block diagram of the proposed roughness feelingtelepresence system. It is master-slave typed. In the diagram,the tactile sensor estimates surface wavelengths of objects
in real-time and transfer them to the display side system.
In the display side system, tactile stimuli are generated by
combining rubbing speeds and the wavelengths. The system
is able to present tactile stimuli in synchronization with touch
motions.
III. R EAL - TIME E STIMATION OF S URFACEWAVELENGTHS OF ROUGHNESS S PECIMENS
A. Tactile Sensor for Estimation of Surface Wavelengths
Tactile sensors for tactile telepresence systems are expected to cause same kinds of physical phenomena such as
deformation or stick-slip effect in rubbing objects. This study
adopted a tactile sensor emulating the tissue structure andperceptual mechanism of human fingers [7]. Fig. 2 shows aphoto and a schematic view of the sensor. The sensor has alayered structure whose layers are designed to have similar

Fig. 1.
A block diagram of a master-slave typed roughness feelingtelepresence system by surface wavelengths of target objects, which cancels
the mismatches between tactile and kinetic sensations of human operators

Fig. 2. Tactile sensor adopted in the present study, mimicking the major
features of human fingers

Young’s moduli of cutaneous fat and epidermis of humanfingers. It is covered with ridges which have epidermal ridges
like curvatures, and dimensions of the sensor are three times
as big as a human finger. Five strain gauges are embedded
inside and the sensor is able to acquire vibratory information.
A six axial torque sensor is installed on the base of the sensor.
It was confirmed that the sensor could estimate surface
wavelengths of objects by computing Fourier transform whenit scanned objects at constant speeds, although the computational process was off-line [7]. In the present study, thereal-time estimation method has been proposed. This sensor
was redundant for the aim of this study. However it can bepotentially used for multimodal tactile telepresence system.
B. Algorithm to Estimate Surface Wavelengths in Real-time
The algorithm to estimate surface wavelengths of objects
in real-time is described in this subsection. The algorithm
estimates a vibratory frequency of the sensor. A surface
wavelength can be derived from the relationship of λ = v/f ,where v is a rubbing speed. A frequency element whose
spectrum is largest can be considered most influential inperceived roughness while many frequency elements exist
in a vibration of the sensor. Therefore, the algorithm wasdesigned to be capable of estimating a major vibratory
frequency in real-time.
The sensor scans a roughness sample illustrated in Fig.
3, which is a trapezoidal grating scale whose ridge widthand groove width are equivalent. Then band-pass filteredsignals of sensory outputs are like in Fig. 4. The pass bandof the filter is 5–100 Hz, considering a frequency responseof Meissner’s corpuscles. The algorithm emits an impulse
when the filtered signal crosses a zero level. Fig. 4 showsthe example of impulses emitted at zero crosses of sensory
outputs. The existence of the impulse is written by (1). The
algorithm estimates the vibratory frequency of the sensor bycounting the number of the impulses for a certain period A.
The estimated vibratory frequency is denoted by (2).
1when the impulse is emittedp(t) =(1)0
when no impulse is emitted
Σt
p(τ )
(2)
fe (t) = τ =t−A
2A

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As a visual aid of (8)!$v(t), f (t − A/2), fe (t) and λe (t)are plotted in Fig. 5.

Fig. 3. Acrylic roughness specimen

Fig. 4. Example of band-pass filterred sensory
outputs and impulses which are emitted at zerocrosses

C. Optimization and Numerical Analysis of the Proposed
Algorithm
To evaluate the algorithm proposed in the prior subsection and decide an optimal value of A, the algorithm andits expected error are formulated in this subsection. The
algorithm estimates a vibratory frequency at t by smoothing
the frequency of the past A second. Therefore an estimatedvibratory frequency fe (t) is approximated to be (3).
tf (τ )dτt−A(3)fe (t) =A|v(t)|
f (t) =
(4)
λwhere f (t) is a true value of a vibratory frequency of thetactile sensor and it is presented by (4). λ is a surface
wavelength of the target object and v(t) is a rubbing velocity.
The estimated wavelength (5) is derived from (3), (4) and therelationship λ = v/f .
|v(t − A
2 )|
+X
(5)
fe (t)
where X is an error caused by the noise in sensory outputs.
The integral computation delays fe (t) a half A so that λe (t)refers to v(t−A/2). From above definition (5), the estimatederror of the surface wavelength is defined as (6).
λe (t) =|v(t − A2 )|− λ + X|(6)fe (t)The optimal A is a value which minimizes (6). A has been
designed so that (6) is extremum when v(t) is sinusoidal,
which is close to a natural rubbing motion of humans. v(t)is described as (7).
|λe (t) − λ| = |v(t) = α sin (2πfv t)

Fig. 5 implies that accuracy of estimation is deteriorated
in range T2 , in which acceleration of velocity is larger than
range T1 or the direction of velocity is about to change.
In the algorithm, the mean value for a period becomes anestimated vibratory frequency; therefore it makes sense thatprecision is deteriorated when the rubbing motion is highly
accelerated or decelerated. Also, the algorithm is designed toperform when the sensor is completely slipping on objects.
However, the sensor and the objects are considered to stickwhen a relative velocity between them is slow. Therefore,
different algorithms should be applied to range T1 and T2
for estimation.
The proposed algorithm is suitable for range T1 . For
further evaluation of range T1 , the estimated error of surface
wavelength (9) is derived from (6), (7) and (8).
|λe (t) − λ| =(7)where α is a maximal speed and fv is a reciprocating frequency of the rubbing motions. With the sinusoidal rubbing
velocity, the estimated vibratory frequency is (8) from (3),(4) and (7).
α|S(πfv A)S(2πfv (t − A2 )| Afv λπ
if t ∈ T1(8)
fe (t) =α(1 − |C(πfv A)C(2πfv (t − A
))|)

2


 Afv λπif t ∈ T2
S(x) = sin(x), C(x) = cos(x)∀t(t ∈ T1 ↔ v(t)v(t − A) ≥ 0)∀t(t ∈ T2 ↔ v(t)v(t − A) < 0)

Fig. 5. Examples of simulated values of the rubbing velocity v(t), thetrue and the estimated vibratory frequencies f (t − A/2), fe (t) and theestimated wavelength λe (t): A = 0.2 sec, λ = 2.0 mm, α = 100.0
mm/sec, fv = 1.0 Hz, X = 0 mm≤

Afv λπ− λ + X|sin (Afv π)
Afv πλ|− 1| + |X|
sin (Afv π)|(9)where the estimated error of surface wavelengths is the sumof the approximation error (1st term) and the error causedby signal noise (2nd term). Assuming that noise is subject
to normal distribution, N (0, σ 2 ), the expected value of |X|
is denoted as (10).
2σE(|X|) = √
(10)
2πAfs
where fs is a sampling frequency. According to (10), as the
sampling period A rises, the increased number of samplings
for the computation reduces the effect of noise.

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The estimated error of the surface wavelength is redefinedas (11) from (9) and (10).
|λe (t) − λ| = λ|

2σ
Afv π
− 1| + √sin (Afv π)
2πAfs

TABLE IM EAN VALUES OF ESTIMATED SURFACE WAVELENGTHS WHEN SAMPLESWERE SCANNED AT CONSTANT SPEEDS(11)

In (11), the condition to make the approximation error zerois Afv → 0. However, the smaller A is, the larger the effectof noise becomes. Both the approximation error and the error
caused by noise must be balanced. The optimal value of A
is decided so that (11) reaches its minimum value.
It is fair to guess that A is not a fixed value and should be
adaptive to environments, referring some variables. Equation(11) implies that A should be adjusted according to fv ,which is the reciprocating frequency of the rubbing motions.
Because in (11), A always appears with fv in the first term.
However, it is not straightforward to estimate fv in realtime. The authors assigned 1 Hz to fv as a representativevalue since the reciprocating frequency of natural touch
motions is supposed 0–2 Hz. A standard deviation σ wasassigned 1.0 mm, which was from a standard deviationof the estimated values when the sensor actually scanned
the roughness sample whose wavelength was 1 mm at 70mm/sec. fs was 1 kHz. Under the above conditions, A wasdetermined to be 0.080 seconds.
D. Experimental Evaluation
Four roughness samples used in experimental evaluation
were depicted in Fig. 3. Their wavelengths were 1–4 mmwith an interval of 1 mm. In the experiment, the sensor was
installed on a vertical slider which was fixed on a single-axis
arm. The sensor was pressed to the samples with a depth of
1 mm in a static state (the reaction force was about 0.6 N),then it scanned samples. The photo of the experimental setup
is Fig. 10.
1) Evaluation of Precision: Precision of estimation wasmeasured when the sensor scanned the samples at constant
speeds which varied from 30 to 70 mm/sec. The meanvalues of the estimated surface wavelengths were computedfrom data which were recorded for two seconds. Those
computed values are shown in table I. Table I shows thatfour wavelengths were completely discriminated. Fig. 6 is agraph of table I. To equally dispose dots along the temporalaxis, seven more experimental results were added to table
I. In Fig. 6, the horizontal axis is the vibratory frequency
defined by v/λ. The vertical axis is accuracy of estimation,
which is a ratio of the estimated and the true values ofthe surface wavelengths. Fig. 6 suggested that accuracy of
estimation depended on the vibratory frequency of the sensor
and the rubbing speeds. The sampling period of the algorithm
was 0.08 seconds; hence accuracy was not good with thevibratory frequency less than about 10 Hz. The frequencyof stick-slip phenomena was quick enough that the bandpass filter could not eliminate its effect when rubbing speeds
were fast. Therefore, the accuracy was not good with highvibratory frequencies in Fig. 6.

Rubbing speed,v [mm/s]
30
50
70

Wavelengths of Samples, λ [mm]
1.0
1.006
1.006
1.128

2.0
2.072
1.844
1.978

3.0
3.390
3.096
3.023

4.0
4.321
4.038
4.075

Fig. 6. Accuracy of wavelength estimation when samples were scanned atconstant speeds2) Evaluation at the Sinusoidal Velocity: An example ofthe transition of the estimated wavelength when the sensor
scanned the sample at the sinusoidal velocity is shown in
Fig. 7. The wavelength of the sample was 2 mm. For themost part, the wavelength was estimated well. However incontrary to the numerical analysis in III-C and Fig. 5, evenin range T1 accuracy was deteriorated. The same trend wasobserved at other speeds and the reciprocating frequencies. It
is considered that this performance decline was caused by theunexpectedly shorter slipping phases, and the algorithm wasdesigned to perform on the assumption that the sensor slippedon objects. This problem can be solved by introducing
detection of slipping phases.
3) Evaluation of Real-time Property: The experiment forevaluating the real-time property of the algorithm was conducted. From a nature of the algorithm, the estimated value iscomputed from sensed data for the past 80 milliseconds, thealgorithm can deal with a sudden change of wavelengths in
80 milliseconds. Fig. 8 shows the transitions of the sensory

Fig. 7.
A transition of the estimated surface wavelength when thesensor scanned the sample at the sinusoidal velocity; λ = 2 mm, v =70 sin (2π0.83t) mm/sec

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Fig. 10.

Slave side system

Fig. 11.

Master side system

TABLE IIS PATIAL WAVELENGTH DISCRIMINATION LIMENS MEASURED BY
LIMITATION METHOD WHEN THE STIMULI WERE GIVEN BY ICPF

Fig. 8.
Example of the sensory outputs and the estimated surface
wavelengths when the sensor scanned two different specimens

outputs and the estimated wavelengths when the sensor
scanned two roughness samples, which were arranged side
by side beneath the sensor. The wavelengths of the samples
were 1 mm and 4 mm. At point B in Fig. 8, the sensor
reached the boundary of two samples, then 80 milliseconds
later the estimated value was roaming around 4 mm.
From above three kinds of evaluation, the algorithm has
potential to transfer differences of surface wavelengths from
1 to 4 mm by 1 mm in real-time.
IV. ROUGHNESS D ISPLAY M ETHOD V IA ICPF TACTILED ISPLAY
The present study employed ICPF tactile display [8]. Fig.
9 shows pictures of the display. The device is light, wearable
and does not constrain touch motions of the operator. It
composes of fifty pieces of gel actuators made of ionic conductive polymer films, which bend when electric voltages areapplied. The actuators respond to high frequencies more than
200 Hz. Desired tactile stimuli are generated by controlling
vibration of the actuators. The voltages for the actuators were
given by (12).
v(12)
y = 5.0 sin (2π t) [V ]λwhere v was a speed of the hand movement and λ was thesurface wavelength of the samples. The maximal voltage
effects on generating force by the actuators. Waveforms
and generating force were not considered in (12). Only
the vibratory frequencies of the stimuly was controlled in
response to hand motions.
Resolution of displayable wavelengths by the method wasinvestigated by a psychophysical experiment. In the test, each

TACTILE DISPLAY ( AVERAGE OF THREE SUBJECTS )Average lower limen
Average upper limen
Avg. discrimination limen
SD of discrimination limen

Standard stimuli [mm]
1.0
2.0
3.0
4.0
0.82
1.78
2.80
3.55
1.28
2.14
3.42
4.40
0.23
0.18
0.31
0.43
0.095 0.10 0.082 0.13

subject equipped his index finger with ICPF tactile display
and stuck it through a tip of a master arm. The appearanceof the test looked like Fig. 11. The subject moved his handalong x-axis and v was an x directional element of thevelocity. To constrain the hand movements on the x-y plane,a virtual plane was made by the master arm.
Wavelength discrimination limens were measured by limitation method. Standard stimuli were 1, 2, 3, 4 mm andcomparison stimuli were given by 0.2 mm. Two ascending
series and two descending series were given per a standard
stimulus. The subject could orally switch the standard andthe comparison stimulus. There was no time limit in thetests. He answered which surface wavelength he felt longer.
The answers were manually recorded by an experimenter.
The subjects were three men in their twenties. The measured
discrimination limens are shown in table II. Table II showsthat the discrimination limens were smaller than 1 mm and it
is concluded that the display method has an ability to display
differences in wavelengths by 1 mm.
V. ROUGHNESS F EELING T ELEPRESENCE S YSTEM
In this section, the roughness feeling telepresence system
which combines the tactile sensor and the display is described. It was confirmed that the sensor and the display
could discriminate the wavelengths by 1 mm individually inprior sections. The integrated performance and results of theexperiment to transfer surface wavelengths are shown.
A. System Integration

Fig. 9.

Tactile display adopted in the present study

In the integrated system, the tactile sensor was installed ona single axis arm as shown in Fig. 10 and it was controlled tomove at the velocity same as the x-directional velocity of themaster arm. The tactile display was equipped an index finger
of a subject like Fig. 11. The sensor continuously estimatedthe wavelengths of the samples and the estimated values weretransferred to the display side system at 100 Hz. Among the2702

TABLE III
E XPERIMENTAL RESULTS OF WAVLENGTH TRANSFER : PERCENTAGES OF
CORRECT ANSWERS

4 mm 3 mm
2 mm
1 mm
4 mm
0.7
1.0∗∗∗
0.9∗
3 mm
0.9∗
0.7
2 mm
0.9∗
∗∗∗ and ∗ are significant at level 99.9% and 95% respectively.

The metric for the tactile telepresence systems is needed,considering perceptual characteristics of humans. The abilities of the systems cannot be simply compared with the casethat bear fingers scan objects. All actuators in the employedtactile display were driven in the same phase, and they did
not cause spatial distribution in the contact area between afinger. Under the similar condition, roughness sensations are
impaired [9][10].

TABLE IV

VII. C ONCLUSION

T HURSTONE C ASE V SCALES OF TRANSMITTED WAVELENGTHS
Sample
Score

1 mm
0

2 mm
0.5461

3 mm
1.092

4 mm
1.449

all estimated values, values in range T1 were used. Basedon the transferred wavelengths and the rubbing velocity ofthe subject, applied voltages were determined by (12) for thetactile stimuli.
B. Experimental Methodology
Four roughness samples whose wavelengths were 1–4 mm
were used in the experiment. The number of the subjects was
one and he was one of the three subjects tested in section IV.
He was considered to have an ability to discriminate the four
samples. The experimental method was paired comparison.
Six combinations were made from the four samples and eachpair was tested ten times. The systems were disposed so thatthe subject could not see the tactile sensor side system. He
knew that two different specimens were arranged at the rightand the left side of the initial position of his hand. He wasallowed to freely check two samples and answered whichsample had a longer wavelength.
C. Experimental Results
Table III shows the experimental results. A value in acell is a percentage of the correct answers, when the subject
tested two samples whose wavelengths were designated bya row and a column. Z-test with no consideration of multiplicity showed that four among six pairs were significantly
discriminated. Table IV shows Thurstone Case V scorescomputed from table III. Internal consistency of the model
was tested by Mosteller’s χ2 test and the model fit theexperimental case (χ2 = 8.72 and χ2 < χ27 (0.05) = 14.07).
VI. D ISCUSSION AND F UTURE W ORKS
In the experimental results, as to two pairs, the subject
could not discriminate two samples in a view of statistics.
To improve the abilities of the telepresence system, thesensor and the display system should be individually sophisticated. Detection of the slipping phases of the tactile sensor
contributes to accuracy of wavelength estimation because
the algorithm used in this study did not perform in thosephases. It is necessary for the display method to involve
physical parameters other than spatial wavelengths, although
this study simply controlled unique temporal frequencies of
the actuators.

The present study has proposed a new framework oftactile telepresence systems, which is suitable for remoteenvironments. In the proposed framework, in order to cancel
latency between kinetic and tactile sensations, tactile stimuli
are generated by combining physical parameters of target
objects and touch motions of operators in a local loop of
the display side system. A roughness feeling telepresence
system was implemented based on the framework and it
was confirmed that a subject could discriminate a physicalcharacter of roughness specimens through the system.
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