2021 30th IEEE International Conference on Robot andHuman Interactive Communication (RO-MAN)
August 8 - 12, 2021 - Vancouver, BC, CA (Virtual Conference)

Virtual tactile texture using
electrostatic friction display for natural materials:
The role of low and high frequency textural stimuli
Kazuya Otake1 , Shogo Okamoto1,2 , Yasuhiro Akiyama1 and Yoji Yamada1
1. Nagoya University, Aichi, Japan
2. Tokyo Metropolitan University, Tokyo, Japan

Abstract—As touchscreens have become a standard feature in
mobile devices, technologies for presenting tactile texture feedback on the panel have been attracting attention. We tested a new
method for presenting natural materials using an electrostatic
tactile texture display. In this method, the frictional forces aredecomposed into low- and high-frequency components. The lowfrequency component was modeled based on Coulomb’s friction
law, such that the friction force was reactive to the ﬁnger’s normal
force. The high-frequency component was modeled using an
auto-regressive model to retain its features of frequency spectra.
Four natural material types, representing leather, cork, denim,
and drawing paper, were presented to six assessors using thismethod. In a condition where only the low-frequency friction
force components were rendered, the materials were correctly
recognized at 70%. In contrast, when the high-frequency components were superposed, this rate increased to 80%, although
the difference was not statistically signiﬁcant. Our approach tocombine a physical friction model and frequency spectrum forlow- and high-frequency components, respectively, allows people
to recognize virtual natural materials rendered on touch panels.
Index Terms—Tactile texture display, Electrostatic friction
stimuli, Auto-regressive model, Natural materials

I. I NTRODUCTION
As touch panels have become more prevalent as computer
user interfaces, tactile feedback technologies for touch panels
have been intensively studied [1]. One tactile display technology suitable for touchscreens is the friction-variable type.
It presents tactile stimuli by manipulating the surface friction
generated when tracing the panel with a ﬁnger. The electrostatic friction display is a type of friction-variable display;various studies have been conducted using this display type.
For example, the roughness intensity perceived by humans onelectrostatic friction displays has been investigated [2], [3],[4], [5].
The objective of our research is to develop a method torender natural material textures, comprising cloth and wood,
on a tactile touch panel display. Previous studies of electrostatic friction displays have attempted to virtually presentroughness textures with periodic surface asperities [2], [6],[7] and surface textures of natural materials [7], [8], [9],[10]. Whereas the virtual stimuli quality that simulated regular

978-1-6654-0492-1/21/$31.00 ©2021 IEEE

roughness textures was relatively high, the method for presenting the surface texture of natural materials has not been wellestablished. For example, some material textures can rarely
be identiﬁed using existing methods. In contrast, vibrotactile
display methods have been developed by numerous researchers
to present virtual textures similar to those of natural materials
by reproducing the frequency characteristics [11], [12], [13],[14], [15], [16], [17], [18].
We consider that simulating or representing the frequency
response is not sufﬁcient to generate virtual natural material
textures, and realism can be improved by adopting a physically consistent model. However, it is difﬁcult to apply thehigh-frequency component caused by the ﬁne and irregularsurface texture of natural materials. In this study, we used anelectrostatic friction display to output virtual texture stimuli
that simulate natural materials by combining two stimulus
types: physical model-based and frequency-based. The shear
frictional force, obtained by touching the material surface
beforehand in our earlier study [19], was separated into lowand high-frequency components, and these were presented on
our electrostatic friction display. The low-frequency component was represented based on Coulomb’s friction law. The
high-frequency component was approximated using an autoregressive model. To evaluate these stimuli, we performed atexture discrimination task under two conditions: One in which
only the physical model-based low-frequency component of
the friction force was output, and the other in which a
combination of low- and high-frequency texture components
was output. Through this task, we investigated the extent towhich the combined use of low- and high-frequency texture
components contributed to the tactile texture transmitted fromthe panel to the ﬁngertips. This study was approved by theInstitutional Review Board of the School of Engineering,
Nagoya University (#20-8).
II. A PPARATUS : TACTILE TEXTURE DISPLAY USINGELECTROSTATIC FRICTION STIMULI

An electrostatic friction display changes the friction force
by controlling the electrostatic attractive force generated by392

Finger movement
Load

Friction force----

Insulator

Conductor++ ++Attractive force
Fig. 2. Tactile texture display used in the experiments. Electrostatic friction
stimuli were output from the ITO panel covered with an insulating ﬁlm. Triaxis
force sensors placed at the four corners measure the load on the ﬁnger andestimate its panel position.

Electric charge
Fig. 1. Principles of electrostatic friction stimuli. The friction force is changed
by controlling the electrostatic attractive force generated by the applicationof a voltage between the ﬁngertip and panel. The friction force promotes theﬁngertip deformation in the shear direction.

applying a voltage between the ﬁngertip and the panel, as
shown in Fig. 1. This frictional force varies with the applied
voltage magnitude and promotes ﬁngertip deformation in theshear direction. Therefore, electrostatic friction stimuli are
effective in presenting textured surfaces where frictional forces
are dominant [8]. An insulator exists between the ﬁngertip
and panel and the current does not ﬂow through the skin.
In comparison with the vibrotactile display, the electrostatic
displays do not involve mechanically driven parts, resulting in
implementation advantages.

Leather

Cork

Denim

Drawing paper

Fig. 3. Four types of natural materials used in this experiment: leather, cork,
denim, and drawing paper.

III. F RICTION MODELING FOR NATURAL MATERIALTEXTURES

For the experiments, we used a tactile texture display that
presents electrostatic friction stimuli, as shown in Fig. 2.
Electrostatic friction stimuli were presented by applying a
voltage between the ﬁngertip and an indium tin oxide (ITO)
panel to which an 8-µm insulating ﬁlm (Kimotect PA8X,
KIMOTO Co., Ltd., Japan) was attached. Experimental participants experienced stimulation by touching the panel while
holding a stainless-steel rod connected to ground. The applied
voltage was amplitude-modulated according to the algorithm
described below (carrier frequency: 20 kHz). The modulated
voltage signal was ampliﬁed by a voltage ampliﬁer (HJOPS1B20, Matsusada Precision Inc., Japan, maximum output: ±1
kV, response: 75 kHz).
Four triaxial force sensors (USLG25, Tec Gihan Co., Ltd.,
Japan) were placed under the ITO panel to measure thenormal and shear forces in real time. The ﬁngertip position
was estimated from the ratio of the four sensor outputs inthe normal direction. The voltage ampliﬁer and force sensors
were connected to a DAQ board (PEX-361216, Interface
Corporation, Japan). The sampling frequency for the force
sensors was 125 Hz, and the control frequency for the friction
display was 20 kHz.

A. Four natural material types presented as virtual stimuli
We analyzed the friction force acquired in [19], where avariety of natural textures were scanned using a ﬁngertip. The
force data were resampled at 2 kHz. We selected four materials
that are often touched in daily life: leather, cork, denim, anddrawing paper, as shown in Fig. 3. If these natural materials
are directly touched by bare ﬁngers, they can be distinguishednearly 100%.
B. Division of friction into low- and high-frequency components
The frictional force generated when tracing surfaces with aﬁnger (fs (t)) is represented by,fs (t) = µ0 fn (t) + µt fn (t),(1)

by dividing it into a low-frequency component and a highfrequency texture component. µ0 is the ﬁxed friction coefﬁcient of the material surface, fn (t) is the normal force
or load on the ﬁnger, and µt is the high-frequency friction
coefﬁcient component above 2.5 Hz. The reason for setting
2.5 Hz as the threshold is that human ﬁnger movements areat most a few Hz [20], [21]. A Butterworth ﬁlter was used
to separate the frictional forces by frequency. As an example,393

(a) Friction force caused when afinger slides over cork
Low-freq. components (N)

0.2

0.1

0
0

1

2

3

4

5

6

7

8

9

10

Texture components (N)

0.3

0.3

Shear force (N)(c) High frequency (> 2.5 Hz)
component of friction force(b) Low frequency (< 2.5 Hz)
component of friction force

0.2

0.1

0.04

0.02
0

-0.02

0
0

1

2

3

4

5

6

7

8

9

10

-0.04
0

1

Time (s)Time (s)

2

3

4

5

6

7

8

9

10

Time (s)

Cork

8

8

6
4

2
0
0

Power density (1/Hz)

Power density (1/Hz)

Leather5
1015Frequency (Hz)termined by dividing the high-frequency friction force by theaverage normal force over 5 s. Fig. 5 shows the frequency
characteristics of the high-frequency friction coefﬁcient component. In the frequency range above 20 Hz, there was no
signiﬁcant difference between the materials. Therefore, only
the frequency characteristics within 0―20 Hz are shown.
However, it should be noted that the model contains additional
high-frequency components.

6
4

2
0
0

20

5
10
15
Frequency (Hz)

20

Denim

Drawing paper

C. Auto-regressive model for high-frequency components

88This section describes the implementation procedure of theauto-regressive model for the texture components. First, we
extracted only the moments when the ﬁngers were clearly incontact with the material in the original force. Speciﬁcally,
only the contact force at the moment when the normal force
fn (t) > 0.05 N and shear force fs (t) > 0.05 N are valid wasanalyzed. Subsequently, we combined the time-series of thefrictional force when the material was touched by ﬁve people
who participated in the previous study [19] for each material
and generated the contact force time-series of approximately
40–60 s. The auto-regressive model is given by:

Power density (1/Hz)

Power density (1/Hz)

Fig. 4. Friction force data when the cork was traced. (a) Friction force caused when a ﬁnger slides over cork. (b) Low-frequency (< 2.5 Hz) friction force
component. (c) High-frequency (> 2.5 Hz) friction force component.

6
4

2
0
0

5
10
15
Frequency (Hz)

20

6
4
2

0
0

5
10
15
Frequency (Hz)20

Fig. 5. Frequency response analysis of high-frequency shear force components
using an auto-regressive model for each of the four natural materials. In thefrequency range above 20 Hz, there is no signiﬁcant difference between thematerials; therefore, frequency responses within 0–20 Hz are indicated.

xk =

p
∑

aj xk−j + vk .

(2)

j=1

Fig. 4 shows the frictional force before and after separation
when a cork is traced. (a) shows the original friction force(fs (t)) when the cork is touched. (b) represents the lowfrequency component below 2.5 Hz, and (c) represents thehigh-frequency component above 2.5 Hz.
The friction coefﬁcient (µ0 ) was determined by dividing
the average shear force by the average normal force over 5 s
measured when the material was touched. Table I lists thefriction coefﬁcients for each of the four natural materials. The
material with the highest friction coefﬁcient is leather (µ0 =0.51), and the material with the lowest friction coefﬁcient isdrawing paper (µ0 = 0.29).
The high-frequency friction coefﬁcient component was de-

The model predicts the current value xk from past values.
This expresses the value of xk by adding past p values.
vk represents the white noise. To determine the order p of
the auto-regressive model, we used the Akaike information
criterion (AIC):AIC = −2L + 2p.
(3)
The AIC is expressed in two terms: the maximum loglikelihood L and the model p order. The larger the maximum
log-likelihood, the smaller the AIC, and the easier it is toadopt as a model. Conversely, the second term, which grows
in proportion to the model order, is added. Hence, models
with large orders are less likely to be adopted. The p with theminimum AIC for each material is listed in Table II.

394

TABLE I
C OEFFICIENT OF KINETIC FRICTION FOR EACH MATERIAL .
Cork
0.45

Denim
0.38

0.9

Drawing paper
0.29

Proportion of correct answer

Leather
0.51

TABLE IIO RDER p OF THE AUTO - REGRESSIVE MODEL FOR EACH MATERIAL .
Leather
37

Cork
29

Denim
19

Coulomb friction + High-freq. texture
Coulomb friction

1

Drawing paper
32

0.8

0.7
0.6

0.5
0.4
0.3
0.2
0.1

IV. E XPERIMENT: D ISCRIMINATION OF VIRTUAL0TEXTURES

For the four materials shown in Fig. 3, we prepared two
conditions: One in which only Coulomb friction (low frequency) was output, and the other in which Coulomb and highfrequency friction components were simultaneously output.
The accuracy of natural material discrimination under thesetwo conditions was compared. To evaluate the realism of
virtual textures, it is desirable to investigate both the subjective
and behavioral performances; however, in the present study,
we only investigated the behavioral ones. Further, we did
not present only the high-frequency components. In order todo this, the tactile display needs to be able to decrease thecoefﬁcient of friction of the panel; however, the panel canonly increase the coefﬁcient.
A. Three-alternative-forced-choice task
We applied the forced n-choice task technique [22], [23],[24], [25] to each stimulus condition (n = 3). Two of thefour types of virtual texture were randomly selected. One
was presented twice, and the other was presented once. The
participant answered which of the three virtual textures wasdifferent from the others. There were six combinations of two
virtual textures among the four types of materials, and eachcombination was tested four times, resulting in 24 trials per
participant in each stimulus condition. There was no time
limit for the participants to touch the virtual textures. The
participants washed their hands before the experiment. During
the experiment, the ﬁngertip surfaces were lubricated with talc
to reduce the friction effect caused by ﬁnger moisture. During
the trial, participants wore headphones and listened to pinknoise.
B. Participants
Six university students (men in their 20s) participated in theexperiment. All participants were unaware of the purpose of
the experiment.

All

Leather

Cork

Denim

Drawing
paper

Fig. 6. Means and standard errors of correct answer proportions.

C. Electrostatic friction stimuli for virtual material stimuli
The relationship between the frictional force fs (t) and theapplied voltage Ve (t) is determined by the law of electrostaticforce and Coulomb friction as,fs (t) =µ{fn (t) + kVe (t)2 },(4)

where µ and k are the friction coefﬁcient and constant related
to the electrostatic force of the panel, respectively. Hence, topresent the tangential force generated when tracing the panel
with a ﬁnger, the applied voltage Ve (t) is,√
Ve (t) = ±α µ0 fn (t) + βµt fn (t),(5)
where α represents the gain to adjust the stimulus to therecognizable intensity for each participant. β is the gain that
ampliﬁes the high-frequency component features. The applied
positive and negative voltages were switched by amplitude
modulation at 20 kHz to alleviate the difﬁculty in feelingthe stimulus owing to residual electric charges [26]. The
virtual texture intensity α was adjusted by the participant
before the three-alternative-forced-choice task such that s/he
could recognize the frictional stimuli. The high-frequency
component intensity, β, was adjusted in advance by the authors
to the intensity where the difference among the four material
types was most perceptible (β = 1.8).
V. R ESULTS
Fig. 6 and Table III show the results of the three-alternativeforced-choice task. In Table III, (a) indicates Coulomb friction plus high-frequency texture condition and (b) indicates
Coulomb friction only condition. The proportion of correct
answers was calculated by dividing the total number of correct
answers by the total number of trials. When a participant could
select one different virtual stimulus among the three in a trial,
the answer was categorized as correct. The error bars are the395friction condition, as in Table III.
In the case of drawing paper, the proportion of correct answers when both Coulomb friction and high-frequency texture
components were output was 0.89 ± 0.05, and when only the
Coulomb friction component was output, the proportion was
0.78 ± 0.09. There was no signiﬁcant difference between thetwo stimulus conditions (t = 0.95, p = 0.18). Drawing paper
was frequently misclassiﬁed to be denim, as in Table III.

TABLE IIIM EANS AND STANDARD ERRORS OF ANSWER PROPORTIONS . ( A )INDICATES C OULOMB FRICTION PLUS HIGH - FREQUENCY TEXTURE
CONDITION . ( B ) INDICATES C OULOMB FRICTION ONLY CONDITION .
(a)
Leather
Leather 0.81±0.15
Cork
Denim
Drawing
paper

Actual natural materials
Cork
Denim
0.13±0.04
0.75±0.16

0.03±0.01
0.14±0.07
0.67±0.18

Drawing
paper
0.01±0.01
0.01±0.01
0.13±0.05
0.89±0.15

VI. D ISCUSSION(b)
Leather
Leather 0.64±0.17
Cork
Denim
Drawing
paper

Actual natural materials
Cork
Denim
0.17±0.04
0.64±0.15

0.08±0.03
0.14±0.06
0.67±0.18

Drawingpaper
0.04±0.03
0.11±0.05
0.10±0.05
0.78±0.15

standard errors among the participants. A t-test was applied
between the two stimulus conditions.
When all four materials were grouped together, the proportion of correct answers when both Coulomb friction and highfrequency texture components were output was 0.78 ± 0.08,
and when only the Coulomb friction component was output,
the proportion was 0.68 ± 0.11. There was no signiﬁcantdifference between the two stimulus conditions (t = 0.63, p =0.27).
In the case of leather, the proportion of correct answers
when both Coulomb friction and high-frequency texture components were output was 0.81 ± 0.09, and when only the
Coulomb friction component was output, the proportion was
0.64 ± 0.13. There was no signiﬁcant difference between thetwo stimulus conditions (t = 0.94, p = 0.18). As in Table III,
leather was frequently confused with cork.
In the case of cork, the proportion of correct answers whenboth Coulomb friction and high-frequency texture components
were output was 0.75 ± 0.11, and when only the Coulomb
friction component was output, the proportion was 0.64±0.11.
There was no signiﬁcant difference between the two stimulus
conditions (t = 0.64, p = 0.27). As in Table III, cork tended
to be misrecognized as leather and denim in Coulomb friction
plus high-frequency texture condition.
In the case of denim, the proportion of correct answers whenboth Coulomb friction and high-frequency texture components
were output was 0.67 ± 0.14, and when only the Coulomb
friction component was output, the proportion was 0.67±0.15.
There was no difference in the percentage of correct responses
between the two stimulus conditions (t = 0, p = 0.5). Denim
was confused with cork and drawing paper in the Coulomb
friction plus high frequency texture condition. Further, denim
tended to be incorrectly answered as cork in the Coulomb

We expected that the discrimination accuracy of virtual
textures presented with electrostatic friction stimuli would be
improved by combining the low-frequency component basedon the physical model and the high-frequency component
generated by the auto-regressive model, rather than only
the low-frequency component. Overall, the former method
improved the material discrimination accuracy, but the increase
was not statistically signiﬁcant. The primary reason for thiswas that the number of participants in the experiment was
small. Contrary to our expectations, the participants were able
to discriminate materials to some extent only by the lowfrequency component of friction. Therefore, it is consideredthat the addition of the texture component did not signiﬁcantly
change the proportion of correct answers.
The combination of low- and high-frequency texture components increased the amount of information conveyed to theﬁngertips. Therefore there were more useful features for theparticipants to distinguish the materials. In addition to the lowfrequency friction stimulus based on the physical model, thehigh-frequency friction stimulus based on the auto-regressivemodel may have made it easier to discriminate virtual textures.
In this experiment, Coulomb’s law of friction was used as
the physical model. However, it does not fully correspondto actual friction phenomena because it is a very simple
model. A more advanced physical model based on skin contact
mechanics must be developed in the future. For example,
a model in which the coefﬁcient of friction varies with thevelocity and ﬁnger load [27] can be considered.
VII. C ONCLUSION
We developed a method for presenting the tactile texture ofnatural materials through an electrostatic friction display andexperimentally investigated its effectiveness. We investigatedwhether the combination of low-frequency friction components based on Coulomb’s law and high-frequency texture
components could enhance the discrimination of the four
virtual material types. Although the results could not be clearlystated in statistical terms, the combination of the two resulted
in correct discrimination with a probability of approximately
80%. Adopting a more physically valid model than that usedin the current study may improve the quality of the stimuli.
ACKNOWLEDGMENTS
This study was in part supported by MEXT KAKENHI(#20H04263).

396

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