Vol.16 No.3, 2014

Original Paper

Multidimensional Matching of
Tactile Sensations of Materials and Vibrotactile Spectra
Yoichiro Matsuura, Shogo Okamoto, Hikaru Nagano, and Yoji Yamada∗1

Abstract– Specifying the relationship between the sensations perceived by material
surfaces and the tactile stimuli presented to human ﬁnger pad is often diﬃcult in tactile
texture studies. Both human texture perception and the physical stimuli presented tothe skin are expressed as multidimensional information spaces. We developed a computational technique for matching these texture and physical stimulus spaces based on
multivariate analysis approaches. The texture space is established via a semantic diﬀerential method. The physical space is based on vibrotactile spectrum information, one of
the most commonly used principles for the analysis and artiﬁcial presentation of textures.
The bases of the physical space were determined to ensure that the material allocations
for the two spaces were similar, and we obtained well-matched spaces for 18 material
samples. These successfully matched spaces will provide an analytic tool for material textures, and will help users of vibrotactile texture displays design virtual materials using
adjectives or the names of materials.

Keywords : Haptics, Texture, Perceptual dimension

1.

Introduction

suring and replaying the vibratory signals generated

A key challenge in tactile texture study is specifying the relationships between tactile sensations andupon contact between a tactile sensor and materials [9 ] . Similar approaches, i.e., the presentationof measured vibrotactile signals, have been widely

stimuli. We investigated the linkage between vibrotactile stimuli and textures, considering that vibro-

used [3], [5], [7], [10 ] . These studies showed that material sensations can be successfully delivered by pre-tactile texture displays have been extensively studied
by many researchers. Psychologists and neurophysiologists have also acknowledged the importance of

cisely replaying the previously measured vibrotactile
stimuli. Naturally, the record-and-play method is
unsuitable for creating or editing new textures.

vibrotactile signals in understanding human texture
percepts. In particular, several research groups re-

In contrast, Konyo et al. studied the qualitative
design of vibrotactile textures [4], [11 ] . They gener-

ported that the spectra of vibrotactile signals express
human percepts of material surfaces. In the present
study, we developed a computational technique toated such textures by tuning three perceptual axes:
roughness, hardness, and friction. This method enables users to create textures in accordance withmatch vibrotactile spectra and material textures.
Vibrotactile texture displays are the most com-human perception. However, the relationships between the perceptual axes and the vibrotactile stim-

mon tactile texture displays. These displays present
the tactile sensations of materials by controlling thespectra or frequency components of vibrotactile stim-

uli were obtained from psychophysical experiments,
rather than from the vibrotactile signals originatingfrom the material samples. Hence, this method wasuli [1]〜[8 ] . However, the link between textures andvibrotactile stimuli has yet to be established.
The most frequently adopted vibrotactile display

suitable for synthesizing textures but did not aim atanalyzing the vibrotactile signals generated by rub-approach is a record-and-play method, in which themeasured vibrotactile signals are replayed by texturebing material surfaces.
Some studies have indicated a close relationshipbetween the tactile sensations of material surfaces

displays after proper signal processing. For example, Yamamoto et al. transferred textures by mea-

and the spectra of ﬁnger pad vibrations or deformations. For example, tactile diﬀerences in textures or*1：Department of Mechanical Science and Engineering,
Nagoya University. This work was supported in part by
KAKENHI Shitsukan (23135514 & 25135717).

vibrotactile stimuli correlate with the diﬀerences in
the power or amplitude spectra of the skin vibrations
generated when exploring such surfaces [12]〜[14 ] . In

The Transactions of Human Interface Society Vol.16, No.3, 2014

2.

ESTABLISHMENT OF TEXTURE
SPACEThe texture space is a multidimensional space
based on adjectives related to the physical characteristics of materials such as rough and soft. The tactile sensations of materials are located in this space.
Naturally, the perceptual space of the materials includes nonlinearity. However, a good starting point
would be to hypothesize that the perceptual spaceis expressed by a linear synthesis of each adjective
Fig. 1 Multidimensional space with tactile
sensations (adjectives) and vibrotactile signals (spectra) of materials being
matched.

base. Actually, many researchers have attempted to
identify the texture space using linear analysis techniques [17 ] .
2. 1

addition, it was shown that roughness percepts arewell represented by the weighted sum of the frequency spectra of the vibrotactile signals generated
by rubbing material surfaces [15], [16 ] . Furthermore,
Wiertlewski et al. showed that virtual materials canbe presented by maintaining the spectra of the friction forces between the ﬁnger pad and the materials [5 ] . These studies made clear the importanceof the spectra of vibrotactile signals for describing
material textures. However, although a human tactile texture is intrinsically composed of multiple dimensions such as the roughness, hardness, and friction [17 ] , few studies have investigated the relationships between the vibrotactile spectra and such textural multidimensionality.
In this study, we developed a method to match thequalitative representations of tactile sensation (texture space) and vibrotactile stimuli (physical space)in a multidimensional space, as shown in Fig. 1. The
texture space is composed of adjectives that represent the qualities of tactile sensation, such as roughsmooth and hard-soft. The physical space is formedby the spectra of the vibrotactile signals. Materials are located in the matched space according totheir vibrotactile signals and tactile sensations. Such
matching is eﬀective for the texture display and analysis of vibrotactile textures. For example, the vibrotactile stimulus for presenting a certain virtual texture can be known and speciﬁed without measuring
the vibrotactile signals of the material using tactile
sensors. Moreover, the tactile sensations of newly observed vibrotactile signals can be estimated by using
the matched texture and vibrotactile spaces.

Methods and Materials

To establish the texture space, we use the semantic
diﬀerential method, in which participants experience
many material samples and perform sensory evaluations using adjectives representing tactile sensations.
The materials are speciﬁed in an orthogonal space
after a factor analysis is applied to the acquired subjective data. The bases of the orthogonal space areexpressed by the synthesis of the adjectives. We constructed the texture space using the abovementioned
procedure.
The materials included walnut wood, woven wood,a fake leather sheet (DI-NOC Film LE-137, 3M),
oiled leather, embossed paper, jacquard, crinkled paper, Japanese paper, artiﬁcial grass, felt (long hair),felt (short hair), denim, a tablecloth, woven wool,
cotton, linen, ﬁne cloth, and uneven wallpaper, as
shown in Fig. 2. All materials were cut into 50 mm
× 50 mm pieces and attached to hard plastic plates
using glue.
Five blindfolded volunteers who were not awareof the objectives of this study touched eighteen
types of randomly presented materials and evaluated their textures using four types of paired bipolar
adjectives: rough-smooth, bulky-ﬂat, hard-soft, andsticky-slippery. These paired adjectives are considered to be the principal psychophysical dimensions
for the tactile percepts of material surfaces [17 ] . Eachpair was rated on a seven-point scale. The ratingsof individual participants were normalized for eachadjective pair. The averages among the participants
were treated as the ﬁnal values of the materials according to the following expression:
J = (j1 , j2 , · · · , ji , · · · , jp ),(1)

Multidimensional Matching ofTactile Sensations of Materials and Vibrotactile Spectra

2 mm
Walnut wood

2 mm
Cotton2 mm

2 mm

Oiled leather

Embossed paper

2 mm

2 mm

Felt (short hair)

2 mmJapanese paper

Denim

2 mm
Woven wood

2 mm
Linen

2 mm
Fine cloth

2 mm

Artiﬁcial grass

Felt (long hair)

2 mm

2 mm

Crinkled paper

Uneven wallpaper

2 mm
Tablecloth

Leather sheet (3M)

2 mm

2 mm
Jacquard

2 mm

2 mm
Woven wool

Fig. 2 Measured materials. Cross-sectional photos are also shown for the two
types of felt and the artiﬁcial grass.

(a) Texture space of factors 1 and 2(b) Texture space of factors 1 and 3

Fig. 3 Material distribution in texture space

The Transactions of Human Interface Society Vol.16, No.3, 2014

Table 1 Loadings of three factors
Factor 1 Factor 2 Factor 3
Rough/Smooth
0.90
0.29
-0.17
Bulky/Flat
0.76
0.49
-0.06
Hard/Soft
-0.61
0.77
0.31
Sticky/Slippery
0.62
-0.24
0.61
Contrbt. ratio
0.54
0.25
0.13

where i and p are the material number and numberof materials, respectively. ji is the column vector of
average adjective values for material i, and describedbyji = (ri , bi , hi , si )T ,(2)

where ri , bi , hi , and si are the scores of the roughsmooth, bulky-ﬂat, hard-soft, and sticky-slippery
sensations, respectively.
2. 2 Configured Texture Space
Figure 3 and Table 1 present the texture spaceand factor loadings conﬁgured by applying a factor
analysis to the acquired subjective data. The analysis exhibited a three-factor model because the sumof the contribution ratios of these axes reached 0.92.
The space contains four arrows that express each adjective pair. The tactile sensations vary with thesearrows. The lengths of the axes reﬂect the gravity of each adjective pair on a plane. In the ﬁrstsecond-dimensional plane, the axes corresponding tohard-soft, rough-smooth, and bulky-ﬂat are longer
than the sticky-slippery axis, which indicates thatthe plane captures these features of the textures. In
contrast, the third dimension clearly reﬂects the sensation of friction.
The locations or coordinates of materials in thethree-dimensional space are
Y = (y1 , y2 , · · · , yp ) = A+ · J ,(3)where yi = (yi1 , yi2 , yi3 )T and A indicate the coordinate of material i and a matrix of factor loadings
as speciﬁed in Table 1, respectively.
3. ESTABLISHMENT OF
VIBROTACTILE SPECTRUM SPACE

3. 1

Measurement of Vibration of Contactor with Sustained Pressure

In tactile exploration, the ﬁnger pad deformations depend on the surface proﬁle of the exploredmaterial. Some researchers have indirectly or directly measured such deformations. For example,
Bensmaı̈a and Hollins measured the ﬁngertip vibration when the ﬁnger pads rubbed materials byusing a Hall-eﬀect sensor and a magnet [16 ] , orDoppler velocimeter [18 ] . Other researchers utilized sounds [19], [20 ] , wave propagation through theskin [21 ] , or frictional forces [5], [22], [23 ] to analyze or
measure the ﬁnger pad vibrations caused by material
surfaces. Given that these studies adopted indirect
measurement approaches, clearly, directly measuring
a ﬁnger pad’s deformation due to a material surface isdiﬃcult. One successful direct measurement method
involved the observation of the contact area between
a ﬁnger pad and a glass surface using a high-speed
camera [24 ] ; however, this approach is available only
for transparent materials.
Instead of using real ﬁnger pad deformations, wemeasured the vibrations of a metal contactor witha load of 1.2 N, i.e., the typical human force during a tactile exploration. The contactor has a rigid
hemisphere with a radius of 4 mm, and it was moved
horizontally using a linear robotic arm by way of
a rigid link and rotational joint. Each material wasmeasured ﬁve times along randomly selected 100-mm
paths, with a scanning speed of 20 mm/s. The contactor displacements were measured using a laser displacement sensor (CD5-30, OPTEX FA Co., Kyoto,
Japan), with the spatial and temporal resolutions set
to 0.02 µm and 1250 Hz, respectively.
3. 2

Subbands of Spectra

As previously discussed, the vibrotactile spectrumof each material expresses its tactile sensation. Naturally, a single spectrum includes many wave components. We divided each spectrum into subbands

Here, we conﬁgure the space for the vibrotactile
signals that occur when a probe scans material sur-to reduce the number of such variables and facilitate eﬀective searching using a computer. Some researchers have shown that accurate spectrum rep-

faces. The measurement of the vibrations and thecomputation of the subbands of the vibrotactile spec-

resentations are unnecessary for vibrotactile textures, which allows a drastic decrease in the num-

tra are described in Sections 3. 1 and 3. 2, respectively. The computation of the vibrotactile spectrabases and their optimization appear in Sections 3. 3

ber of the variables [25 ] . For example, the sum of
the energies of frequency-shaped or weighted spectrum well represents the perceived roughness of aand 3. 4, respectively.

texture [13], [15], [16 ] . In the light of these facts, theMultidimensional Matching ofTactile Sensations of Materials and Vibrotactile Spectrapresent study treated the spectra averaged in a limited frequency band. Such subbands of the spectra
are then regarded as the base vectors of the vibrotactile spectrum space.
The subband vectors were calculated as follows.
The amplitude spectra were computed from the vibrotactile signals of each material obtained in Section 3. 1. For each material, the spectra from ﬁvemeasurements were averaged. The frequency bandsof 0–500 Hz were divided into ten subbands, andthe average of the amplitude spectra was calculated
within each subband. The average of the amplitudes
in the jth subband for material i was determined asfollows:∫ fj
ai (f )
aij =
df
(4)
fj−1 fj − fj−1
(j = 1, · · · , 10; fj < fj+1 , f0 = 0, f10 = 500),
where f and ai (f ) are the frequency and amplitudeof f , respectively. Then, aij are standardized among
materials for each j. The subband vector of material
i is expressed as follows:
si = (ai1 , ai2 , · · · , ai10 )T .

(5)

The subband vectors of all the materials are expressed in the form of a matrix:
S = (s1 , s2 , · · · , sp ).

(6)

A search is made for subband widths (f0 –f10 ) to ﬁnd
a match between the texture and vibrotactile spaces,
as mentioned in Section 3. 4.
3. 3 Computation of Vibrotactile Spectrum Bases
We determine the vibrotactile spectrum space toexpress the tactile sensations of materials. In particular, we compute the bases of the spectrum space(B) such that the spectra of the materials (S) well
express the allocations of the materials in the texture
space (Y ).
The bases of the vibrotactile space are computedby
B = Y · S+.

(7)

where B is a 3 (number of dimensions of texture
space) × 10 (number of subbands) matrix. The row
vectors of this matrix represent the bases of the vi-

3. 4

SearchBasesfor

Vibrotactile

Spectrum

Using the following procedures, we optimize thesubband widths f0 –f10 such that the vibrotactile
spectra specify the tactile sensations of materials.
We exhaustively set the widths of ten subbands.
Narrow bandwidths are set for the lower frequency
bands, whereas wide bandwidths are used for higherfrequency bands, considering the frequency response
characteristics of the mechanical receptors. Some researchers have used receptor ﬁlters to establish thevalidity of setting bandwidths according to receptor
characteristics [16], [26], [27 ] . One of the major bases ofsuch ideas is that the roles and information processesof each receptor class are independent in the perception of texture or vibrotactile stimuli [28]〜[31 ] . In the
low frequency bands, the sensitivities of Meissner’sand Merkel’s corpuscles to vibrotactile stimuli are
better than those of the other corpuscles. The active
bandwidths of these corpuscles are relatively narrow.
In the high frequency bands (more than 100 Hz), thesensitivity of Pacinian corpuscles is extremely high.
1. Here, f0 = 0, f10 = 500, and fi values are natural numbers between 0 and 500.
f1 = f0 + {1, 2, ..., 10},
f2 = f1 + {1, 2, ..., 20},
f3 = f2 + {1, 2, ..., 20},
f4 = f3 + {5, 10, ..., 50},
f5 = f4 + {5, 10, ..., 50},
f6 = f5 + {5, 10, ..., 50},
f7 = f6 + {10, 20, ..., 100},
f8 = f7 + {10, 20, ..., 100},and f9 = f8 + {10, 20, ..., 100}.
2. Using the ten subbands determined in the above
step, the subband vectors S of the materials arecomputed using (4), (5), and (6).
3. Using S, (3), and (7), the coordinates of thematerials in the vibrotactile spectrum space aredetermined byŶ = B · S.

(8)

4. The error between Y and Ŷ is computed using
the following expression:∑

brotactile spectrum space. In other words, B is a
projection from S (vibrotactile signals of materials)
to Y (their locations in the textural space) because

After repeating these procedures, we adopted the

Y = B · S.

subband widths with the minimum error value.

||yi − ŷi ||.

(9)The Transactions of Human Interface Society Vol.16, No.3, 2014(a) Physical space of spectra 1 and 2(b) Physical space of spectra 1 and 3

Fig. 4 Material distribution in vibrotactile space(a) Weights of subbands: Spectrum 1.

(b) Weights of subbands: Spectrum 2.

(c) Weights of subbands: Spectrum 3.

Fig. 5 Absolute components of B matrix. Ensembles of frequency subbands
for each vibrotactile spectrum basis.

Frequencies for subbands
[Hz]

4.
4. 1

Table 2 Subband frequencies
f0 f1 f2 f3 f4 f5 f6
0 10 30 32 37 87 112

MATCHED VIBROTACTILE SPACEAND ITS VALIDATION
Vibrotactile Space Matched with Tex-

ture Space
Figure 4 shows the vibrotactile space established in
Section 3. Eighteen types of materials and four axes
for adjectives are also located in this space. This
space connects the tactile sensations that humans
experience and the vibrotactile signals acquired byrubbing materials.
Figure 5 shows B, the structures of the individual
vibrotactile spectrum axes. The structures of theaxes are expressed as the weights of the vibrotactile
spectra’s subbands, of which the widths are summarized in Table 2. On the ﬁrst axis, the 10–90 Hz
and 182–500 Hz frequency ranges were highly loaded.
On the second axis, the 10–232 Hz frequencies were

f7
182

f8
222

f9
232

f10
500

loaded. The third axis weighted the band up to 10 Hz
and at 30–232 Hz. However, because the orthogonalrotation of the vibrotactile space is indeterminant, it
should be noted that the subband structures do notalways show proﬁles similar to those acquired here.
4. 2

Validation: Comparison of Estimatedand Observed Adjective Scores

We investigated the extent to which the estimated adjective scores described the observed adjective scores. In other words, we tested how accurately
the vibrotactile signals of the materials could estimate their sensory properties. The adjective scores
estimated from the vibrotactile signals are expressed
as follows:Jˆ = A · Ŷ .

(10)

Multidimensional Matching ofTactile Sensations of Materials and Vibrotactile Spectra

scores. These results indicate that the vibrotactile
approach of the present study is limited in terms ofpresenting the perceived softness or hardness of materials.
These comparisons showed that the texture andvibrotactile spectra spaces were fairly matched using the method considered in the present study. The
variations in the adjective scores were represented by
the vibrotactile spectra with accuracies higher thanFig. 6 Variances of observed and estimatedadjective scores of 18 materials.

We then compared J and Jˆ, which are the observed
and estimated adjective scores of materials, respectively.
Figure 6 shows the comparison of the observed andestimated adjective scores for each of the eighteenmaterials. For the adjective pairs of rough-smooth,bulky-ﬂat, hard-soft, and sticky-slippery, the R2 values of J and Jˆ were 0.94, 0.95, 0.89, and 0.92, respec-

90% with the hard-soft scores being an exception. As
discussed in Section 5. 1, the hard-soft properties ofthe materials were represented poorly compared withthe other sensory properties as a result of the speciﬁcations of the vibrotactile signals. Furthermore, forsome materials, the errors in their roughness, bulkiness, and stickiness scores were substantial. Sucherrors may stem from limitations of the vibrotactile signal measurement setup, as discussed in Section 5. 2.
5.
5. 1

tively. Approximately, 90% of the adjective scoresof the materials were expressed by their vibrotactile
spectra.
We investigated the errors between the estimatedand observed adjective scores for each material. We
computed Jˆ − J , and then determined the errors

DISCUSSION

Vibrotactile Stimuli are Weak in Presentation of Hardness and Softness ofMaterials

The present study suggested that the vibrotactile
signals are limited in terms of presenting the softness
percepts of materials. These aspects of the hard-for each adjective pair. In light of the eﬀect size insoft scores reﬂect the fact that the perception of material compliance is not generally based on the vi-

statistics [32 ] , the substantial eﬀect of the errors was
regarded small when these errors were at most 30%of the standard deviation (1.0) of the observed ad-

brotactile signals, although the perceptions of thesurface roughness [15], [28 ] and frictional characteristics of materials [33], [34 ] are linked with the vibro-

jective scores.
The Jˆ − J values are listed in Table 3. The graytactile signals of ﬁnger pads. The perception of surface compliance is attributed to the pressure distri-

entries indicate that the errors between the observed
and the estimated adjective scores are more than
30% of the standard deviation of the observed scores.

bution within the contact area between the ﬁnger
pad and the material during pushing motion [35], [36 ] .
The vibrotactile stimuli do not capture information

The adjective scores of the walnut wood, cotton,
leather sheet, embossed paper, jacquard, felt (shortsuch as the pressure distributions and contact areas.
However, the goodness of ﬁt of the hard-soft scoreshair), tablecloth, and uneven wallpaper were well estimated. However, the adjective scores of the ﬁne
cloth, artiﬁcial grass, felt (long hair), woven wood,

was as high as 0.89, which indicates that the softness percepts were partly presented by the vibrotactile signals. Very recently, some studies showed theand woven wool were not well estimated. In particular, the estimation errors in the cases of these ma-connections between the vibrotactile stimuli and thesoftness percept of materials [4], [37]〜[40 ] . The mecha-

terials were greater than 30% for the adjective pairhard-soft. For eight of the 18 materials, the errors in
the hard-soft scores were non-negligible, whereas justnism of the vibrotactile softness display phenomenon
remains to be clariﬁed, but the results of the present
study are in harmony with those of other recent re-a few materials exhibited high errors for the otherports.

The Transactions of Human Interface Society Vol.16, No.3, 2014

Table 3 Errors between estimated and observed adjective scores: Jˆ − J. Graycells indicate substantial errors. Positive values indicate that the materials were estimated rougher, bulkier, harder, or stickier than reported
in sensory evaluation.
Rough/Smooth Bulky/Flat
Hard/Soft Sticky/Slippery
Walnut wood
-0.2494
0.1174-0.1501
0.2642
Cotton-0.2200
0.21680.1051-0.1200Linen-0.3215-0.0449
0.1426-0.1227-0.3274
0.3680Fine cloth-0.28310.0565
Leather sheet-0.1253
0.0907
0.0918-0.0887
Oiled leather
0.1603-0.15200.3236-0.1242
Embossed paper-0.22910.02930.1922-0.0277
Jacquard-0.1503-0.0482-0.17080.2501
Artiﬁcial grass-0.47140.26820.3889-0.3312
Felt (long hair)-0.3104
0.20100.76880.0623
Felt (short hair)
0.16340.0332-0.1173-0.2611
Denim
0.09180.31710.01470.0590
Tablecloth0.1041-0.14290.0322-0.0488
Crinkled paper
0.1875-0.27400.5300-0.1231
Japanese paper-0.18130.2747-0.4044-0.2767
Woven wood
-0.2405-0.4632-0.5761
0.6946
Woven wool
-0.11620.33380.4624-0.1820
Uneven wallpaper-0.0733-0.0926-0.00190.0807

5. 2

Limitations of
Measurements

Vibrotactile

SignalThe rough-smooth, bulky-ﬂat, and sticky-slippery
properties were well represented by the vibrotactile
spectra, but the errors of these properties were nonnegligible for a few materials. This is potentially due
to limitations in measurement setup for the vibrotactile signals.
For example, the error between the estimated andobserved rough-smooth scores for artiﬁcial grass was-0.47, which indicated that it was estimated to bemuch smoother than it actually was. Artiﬁcial grasshas ﬁne cilia on its surface, for which epidermal
ridges or ﬁngerprints may play an important rolein increasing the perception of roughness [23], [41], [42 ] .
On the other hand, under our measurement setup,the surface of the measurement contactor was hardand smooth, and could not capture the roughness

wood were not fully described. Furthermore, the vibrotactile signals included frictional vibrations thatwere not diﬀerentiated from those caused by surface
roughness. This might have led to the error in theestimated stickiness of the material.
Some may speculate that the use of tactile sensors mimicking a human ﬁnger would improve the vibrotactile measurement. Artiﬁcial ﬁnger sensors canhave several transducers spread within a soft superﬁcial layer. However, one of the major ﬂaws of suchsensors is their frictional properties. Even generating
their comprehensive friction properties, which can be
represented by friction coeﬃcients, similar to thoseof human ﬁngers, is highly demanding [43], [44 ] . It
is more diﬃcult to develop tactile sensors that reproduce frictional properties dynamically, similar to
those of human ﬁngers.
6.

CONCLUSION

percepts caused by these cilia.
Similarly, woven wood was estimated to be ﬂat-

This study developed a technique for matching the
textures and vibrotactile signals of materials in a

ter and stickier than the corresponding human sensory evaluation. This material was a woven sheet ofthin wood strips. When this material was touchedmultidimensional space. Such a space can be helpful
for the analysis and display of vibrotactile texturesconsidering that human textural percepts are multi-

with a ﬁnger pad, an uneven pressure or deformation occurred at the contact area. This unevenness

dimensional. In contrast, earlier studies mainly dealt
with unidimensional matching.

produced macroscopic roughness percepts expressed
as “bulky.” The measurement setup that was used
did not ﬁnd the stimulus distribution on the con-

The texture space was established based on thesensory evaluation of material samples and factor
analysis. The vibrotactile space was constructed

tact area, and the bulkiness properties of the woven

based on the subbands of the spectra of vibrotac-

Multidimensional Matching ofTactile Sensations of Materials and Vibrotactile Spectra

tile signals that were generated as a result of explorative contact between material surfaces and a spherical contactor. The bases of the vibrotactile space
were determined based on the similarity between thelocations of the materials in the two spaces. Therefore, the established vibrotactile spectra space described the sensory scores of materials with accuracies of approximately 90%, although limitations were
found in the representation of the perceived hardness
or softness of materials. In addition, the method
to measure vibrotactile signals should be improvedfor better representation of textures. The developed
method of connecting textures and vibrotactile stimuli is generally applicable to vibrotactile texture displays with a single vibratory source or contactor,
which are likely to be commercially available, andwill help in the analysis of human percepts of vibro-

[9]

[10]

[11]

[12]

[13]

tactile textures.

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（received Jan. 15, 2014）

Biography
Yoichiro Matsuura
is a mechanical engineer who received B.S. and M.S. degrees inengineering from Nagoya University in 2012 and 2014, respectively.
His study interests include humancomputer interfaces.

Shogo Okamoto （Member）
received M.S. and Ph.D. degrees in
information sciences in 2007 and2010, respectively, from the Graduate School of Information Sciences,
Tohoku University. Since 2010, he
has been an assistant professor at
the Graduate School of Engineering,
Nagoya University. His research interests include haptic interfaces andhuman-assistive technology.

Hikaru Nagano

Multidimensional Matching ofTactile Sensations of Materials and Vibrotactile Spectra

received B.S. and M.S. degrees in engineering from Nagoya University in2010 and 2012, respectively. Currently, he is a Ph.D. candidate in the
Department of Mechanical Scienceand Engineering, Nagoya University.
He has also received the JSPS fellowship for young researchers since 2013.
His research interests include humanperception.

Yoji Yamada
received a Ph.D. degree from theTokyo Institute of Technology in1990. He has been an associate
professor at the Toyota Technological Institute, Japan since 1983. In
2004, he joined the National Institute of Advanced Industrial and Science Technology (AIST), as a groupleader of the Safety Intelligence Research Group at the Intelligent Systems Research Institute. In 2008,
he moved to the Graduate School ofEngineering, Nagoya University, as aprofessor.

（C）NPO法人ヒューマンインタフェース学会

The Transactions of Human Interface Society Vol.16, No.3, 2014