International Journal
Transactionsof JapanofSociety
Affectiveof Engineering
Kansei Engineering
J-STAGE Advance Published Date: 2015.11.09
doi: 10.5057/ijae.IJAE-D-15-00018

ORIGINAL ARTICLE

Specification of Individuality in Causal Relationships
among Texture-related Attributes, Emotions, and Preferences
Shogo OKAMOTO*, Hikaru NAGANO**, Kensuke KIDOMA* and Yoji YAMADA** Nagoya University, Furo-cho, Chikusa-ku, Nagoya-shi, Aichi 464-8601, Japan** Tohoku University, Aoba, Aramaki-aza, Aoba-ku, Sendai-shi, Miyagi 980-8578, Japan
Abstract: To effectively design tactile textures of product surfaces, it is essential to specify the semantically multilayered and multidimensional structure of human perceptual, emotional, and preferential expressions pertaining to touching materials. We implemented
a method to identify individual differences in the structure of these expressions, based on subjective reports of the mutual impacts
among 29 adjective dyads used to describe tactile experiences of material samples. Results showed that the 11 university students who
responded to 46 types of flat materials were clustered into three statistically different groups. The structures were largely composed ofthree layers of expressions: psychophysical (bottom), emotional or material attributes (middle), and preferential (top). All groups
selected the same adjective dyads encompassed in the bottom layer, relating to percepts of physical quantities, which indicates thesimilarity of psychophysical percepts among the participants; however, the middle and top layers significantly differed across individuals. These results indicate that under the tested conditions, there are individual differences in the semantic structures of emotional andpreferential experiences; however, such differences in psychophysical expressions are relatively minor.
Keywords: Tactile perception, Texture, Perceptual model, Individual difference, Perceptual layer

1. INTRODUCTION
Touching surfaces produces diverse types of subjective
experiences. Primitive experiences comprise psychophysical transformation to perceptual quantities, fromphysical percepts such as surface roughness and thermal
properties. Emotional and affective experiences aredescribed in terms of feelings, comprising simple-complex
and exciting–boring. Most personal experiences relate topreferences, such as like–dislike and good–bad. In order
to effectively design surface textures of products, it is
crucial to understand the causal relationships among thesedifferent types of texture-related experiences. Especiallybecause emotional and personal experiences significantly
influence the value of a product, many studies haveinvestigated experiences of pleasantness, comfort, andpreference for product design purposes [1-5].
Experiences pertaining to touch are sometimes categorized into semantically different layers [6-8], as shown in
Fig. 1. The bottom layer, tentatively named the psychophysical layer, corresponds to percepts of the physicalproperties of materials. The dimensionality of this layer
has attracted the interest of many researchers [9, 10], and istypically composed of factors related to perceived fine andcoarse (i.e., macroscopic) roughness, softness (or hardness), warmness (or coldness), and friction. The middle
layer comprises emotional and cognitive experiences.
Received: 2015.04.14 / Accepted: 2015.09.07

Chen et al. [6] suggested that this layer encompasses
two sublayers, which is also consistent with the reportof Nagano et al. [8], and indicates the complexity ofhuman affective perception. The top, or preferential,
layer indicates personal preferences about the item. It issemantically located above the other layers, and these
preferences are considered to be determined based on thephysical and affective attributes of the product.
Thus far, few research groups have attempted to specify
the layered structure of tactile perception. Chen et al. [6]Figure 1: Semantically layered structures of touch-related
adjective dyads.
The words in each layer were selected from the literature [6-8].
The structure of the lower level layer varies less across people,
whereas that of the higher layer is more dependent on
individual perceptions.
Copyright © 2015 Japan Society of Kansei Engineering.
All Rights Reserved.

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mapped discrete layers of sensory and affective adjectives used to describe textures, based on correlations
among the scores assigned to the adjectives in a sensory
evaluation experiment. Nagano et al. [8] developed
a method to establish the semantically layered modelof psychophysical, affective, and preferential adjectives,on the basis of impact relationships among the words.
Guest et al. [7] and Ackerley et al. [11] projected textures
into both psychophysical and emotional spaces, witheach space being described by at least three individual
dimensions. The specified emotional space appears
to correspond to classical emotion models [12, 13],in which human emotions are expressed in the two- or
three-dimensional spaces. In summary, words relating to
textures are semantically layered, and each layer is multidimensional.
Naturally, these tactile experiences differ from person
to person. For example, Soufflet et al. [14] reported thatthose working in the clothing industry and those who donot, use different terminology when assessing fabrics.
Similarly, students majoring in fashion use adjectives
that are different from those of nonfashion majors [15].
Such individuality is also observed in the level of thepercepts of physical stimuli. For example, Hollins andBensmaïa [16] found that the dimensionality of perceptual
space varies among people, and concluded that theprominence of friction properties in texture perception
depends on individuals. According to Klöcker et al. [17],pleasantness experienced by touching materials isaffected by the humidity of the finger pad, which may
pertain to the individuality in importance of friction
percept when assessing textural dimensions. As found inthese studies, the perceptions and feelings caused bytouching materials depend on individuals. In principle,
the layered structures of texture-related experiences
should also exhibit individual differences; however, theexplicit establishment of such structures has begun only
recently [6, 8], so individuality in the structure has yet tobe reported thus far.
In this study, we computationally specified individual
differences in the structure of texture-related adjective
dyads. We constructed the layered structures of these adjective dyads based on Nagano et al.’s method and data [8].
However, where Nagano et al. [8] analyzed the average
results across all participants, we focused on individual
differences. In this study, the participants were clusteredinto statistically different groups to show how theirlayered structures vary. This method will help us to understand, for example, differences in customer values held
for products.

2. METHOD
The following experiment was originally conducted by
Nagano et al. [8] with the approval of the ethical committeeof the Engineering School at Nagoya University.
2.1 Participants
The experimental participants were 11 university students
who were recruited through an open advertisement. They
were free of self-reported neural and tactile disabilities.
2.2 Tasks
The rating task was not conducted for each material
sample, as is typical in the sensory evaluation task.
Instead, the causalities between each pair of adjectives werecomprehensively judged based on the set of specimens.
Using only one hand, the participants freely touchedeach of the 46 types of materials, which were randomly
lined up behind a curtain, such that they could not see thematerials or their hands. They were instructed not to lift thematerials, and were not informed of the type of materials
used. On the basis of the material textures, participants
then rated the causality between two adjective word dyads
from among 29 dyad options. They judged the causalities
for all permutations made by the 29 dyads in random order.
For example, they rated the impact from rough–smooth todelicate–bold using 6-grade scales that ranged from 0 (noeffect) to 5 (highly influential).
The adjective dyads were rough–smooth, uneven–flat,
hard–soft, warm–cold, sticky–slippery, wet–dry, simple–
complex, regular–irregular, delicate–bold, sharp–dull,
strange–usual, clean–dirty, clear–vague, concrete–abstract,
dangerous–safe, beautiful–ugly, interesting–uninteresting,exciting–boring, rich–poor, comfortable–uncomfortable,
good–bad, significant–insignificant, itchy–not itchy,
modern–classic, natural–artificial, friendly–unfriendly,
general–special, like–dislike, and happy–sad. These
adjectives were selected from among a pool of adjectives
used in earlier studies on tactile sensory evaluation, withsemantically overlapping words excluded. Some may
consider that the adjective dyad beautiful–ugly is largely
based on visual sensations; however, tactile or haptic
aesthetics are also recognized in this context [18].
2.3 Stimuli: Material specimens
As listed in Table 1, we used 46 types of material specimens, which were cut into square forms (15 × 15 cm),
including wood, cloth, fur, paper, fake leather, ceramictile, and machined metal plates. Thin materials, such as
paper or cloth, were pasted to a 15 cm square plate made

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Specification of Individuality in Causal Relationships among Texture-related Attributes, Emotions, and Preferences

Table 1: The 46 materials used for sensory evaluation
Magnolia wood

Oak wood

Sapelli wood

Cork board

Fine woven straw

Coarse woven straw

Woven linen

Woven rush grass

Soft fake fur

Short hair fake furLong hair fake fur

Fake boa

Fake cowhide

Fake alligator hide

Fake woven leather

Fake suede

Felt

Towel

Satin

Pyramid rubber
matting

Mirror plate

Iridescent sheet

Aluminum foil cloth

Mosaic tile

Glossy vinyl sheet

Glossless vinyl sheet

Cotton cloth

Denim

Fine Japanese paper

Crumped paper

Corrugated paper

Wall paper

Artificial grassSpongePerforated aluminum

Woven wire mesh

Glass beads(7 mm)

Glass beads(5 mm)

Glass beads(3.5 mm)

Glass beads(1.5 mm)

CottonGoose feathers

Steel wool

Stainless steel
scrubber

Urethane resin

Tileof hard plastic. We attempted to prepare for a variety ofmaterials that are common in our daily lives, as opposedto specific products or categories of materials. However,
due to difficulties with controlling conditions among thetrials, we excluded fresh materials, such as vegetables andleaves, and oily and sticky materials. Some adjective dyads
may not be applicable to some materials. For example,
soft metal plates do not exist, and softness percepts do not
influence affective experiences for metal plates, eventhough the participants could not visually recognize thetype of materials they touched. Hence, to acquire a structure
model specified for a certain material category, only the

specimens belonging to that category should be used.
In this experiment, we acquired a more comprehensivemodel of average human responses toward various typesof materials.
3. ANALYSIS
3.1 Construction of the layered structure of adjective
dyads
Here, we briefly review the method used by Nagano etal. [8] to establish causal relationships among adjectives.
They used effect matrices [19, 20] to specify thesecausalities, given difficulties associated with using correlations among ratings assigned to words, namely,
covariance matrices or correlation matrices.
The procedure of computation is shown in Fig. 2, with
example values. For participant k, the causality ratingfrom adjective dyad i to another adjective dyad j was
defined as(1)
and was assigned a decimal value from 0 to 5 according to
the participant’s answer. To acquire the direct effect matrix
Z(k), the matrix X(k) of xij(k) was normalized for eachparticipant by the formula(2)(3)
where s(k) was the maximum value of column and row
sums of X(k). The total effect matrix of Z was defined by

Figure 2: Example of the computation process to produce the effect graph from the effect matrix.
Z and B correspond to (2) and (4), respectively. T is the binarized matrix of B withthe threshold value being 0.3, as an example.

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the formula(4)
An element of Z(k)l indicates the indirect effect from one
adjective dyad to another adjective dyad by way of otherl − 1 adjective dyads. Next, the causality graph was
drawn based on B, with the adjective dyads being nodes.
If bij(k), an element of B, is higher than a certain thresholdvalue, a directional arc is produced from i to j, whichindicates that i has an impact on j. The smaller thresholdvalues produce relatively detailed structures, while thehigher values can simplify the graph for visual clarity.
The acquired graph may include multiple paths from one
node to another. By removing such redundant paths
between nodes while remaining the reachability between
arbitrary two nodes [8], a layered structure of adjectives
was established.
3.2 Cluster analysis of individual layered structures
We wanted to group the participants with substantiallysimilar structures of adjectives; however, thus far, noresearchers have discussed the qualitative differences inthe structures even though some factors are apparently
qualitatively important, such as the number of layers andtypes of adjectives in single layers. Hence, we classified
the participants in a quantitative manner using statisticaltests. We clustered the total effect matrices (B(k), k = 1, 2,…, 11) of all participants using Ward’s method [21]. The
distance between two matrices of participants k′ and k″,B(k′) and B(k″), was defined by the formula(5)
where b(k) ∈ R841 was the vector that contained allelements in B(k). The distance can be also written as
follows(6)
Figure 3 illustrates the results of the clustering analysis.
In order to build statistically valid clusters, we tested thefollowing two clusters: For each element in two clustersof P = {b(p1), b(p2), … b(pr)} and Q = {b(q1), b(q2), …, b(qs)}
where r and s are, respectively, the number of participants
belonging to clusters P and Q, we computed the distancefrom the center of P. The sets of distances are denoted as
p and q(7)
(8)

Figure 3: Participant clusters in terms of similarities ofeffect matrices.

where bp is the center vector of P. bp is determined by(9)
We then compared p and q using Welch’s t-test. As aresult, clusters A and B (t0(9) = 3.67, p < 0.01) were
judged as statistically different. Further, two subclustersof A1 and A2 (t0(5) = 3.95, p < 0.05) were different.
Clusters A and B were composed of participants 1–7 and8–11, respectively, while clusters A1 and A2 werecomposed of participants 1–4 and 5–7, respectively.
4. RESULTS
4.1 Similarities and differences among the threeclusters
For each of the three statistically valid clusters
described in the previous section, we constructed thelayered structure of adjective words as shown in Figs. 4(group A1), 5 (group A2), and 6 (group B) by applying
the method described in Section 3.1 above. The thresholdvalue was set at 0.050. In general, larger threshold values
reduce the number of apparent paths in the graph andyield a more visually understandable graph; however,
the graph should not be simplified too much. We adopted
the value of 0.050 because the number of nodes in
the graph plummeted when the threshold value was set
above 0.051. Further, within the range of 0.036–0.050,
the number of nodes in each layer did not change.
The nodes in the highest layer (like–dislike, happy–sad,rich–poor, and good–bad) and those in the bottom layer(warm–cold, sticky–slippery, wet–dry, uneven–flat, andrough–smooth) were the same across all three groups. The
nodes in the highest layer were largely related to personal

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Specification of Individuality in Causal Relationships among Texture-related Attributes, Emotions, and Preferences

Figure 4: Effect graph built from group A1.
This is nearly three layered except for the beautiful–ugly dyad.
Like/Dislike

Interesting/Uninteresting

Warm/Cold

Strange/UsualHappy/Sad

Comfortable/Uncomfortable

Sticky/Slippery

Good/Bad

Exciting/Boring

Wet/Dry

Delicate/Bold

Hard/Soft

Simple/Complex

Rich/Poor

Friendly/Unfriendly

Uneven/Flat

Beautiful/Ugly

Concrete/Abstract

Clear/VagueGeneral/Special

Rough/Smooth

Figure 5: Effect graph built from group A2.
This has a three-layered structure.
The rich–poor node cannot be judged in relation to whether it belongs to the middle or top layer.

Figure 6: Effect graph built from group B.
This has a four-layered structure with a complicated middle layer.
The dangerous–safe and itchy–not itchy nodes were not strongly influenced by the bottom, psychophysical layer.

Advance Publicationpreference, and those in the bottom layer pertained to
psychophysical percepts. The middle layer was composed of
attributes of materials, such as clear–vague, general–special,and simple–complex, and emotional properties including
comfortable–uncomfortable, interesting–uninteresting, andexciting–boring.
In Figs. 4, 5, and 6, although the bottom and highest
layers in all groups have the same or very similar nodes,
the structures of the middle layer varied between groups
A and B. In short, the middle layer in group B clearly
included an additional sublayer, whereas that in group
A was a single layer. The distinctions between groups
A1 and A2 were relatively minor in terms of the typesof nodes in the layer. In the structure of group A1,
the beautiful–ugly node was located above the other nodes
in the middle layer, while the middle layer in group A2
was completely flat. Nonetheless, most of the differences
between groups A1 and A2 lay in the paths spanning
the nodes. For example, in the structure of group A1,
happy–sad was influenced by interesting–uninteresting
and warm–cold nodes, whereas in structure A2, it
was directly influenced by interesting–uninteresting,comfortable–uncomfortable, and exciting–boring nodes.
Some participants appeared distant from the others
belonging to the same group for example, participants 6 or
7 and participant 5 although the topological differences intheir layered structures were minuscule.
4.2 Comparison of the layered structures with thosein a related study
The abovementioned general features were also
observed by Chen et al. [6], who built the structure basedon the correlation coefficients among ratings assigned
to adjectives. Further, to the best of our knowledge,
their study is the sole example of the structure of
texture-related adjectives with more than three layers.
In spite of several differences between their method
and ours, profound similarities were observed in these
two studies. Chen et al.’s stimuli were 15 flat wrapping
papers and films used for food packages, which were
cut into 50 × 80 mm pieces. Forty women aged 10–30s
rated each specimen in terms of 17 adjectives, using
7-graded scales in a blind condition. Unlike in our
experiment, Chen et al.’s participants knew that thematerials were used for food packaging. In their structure,
the bottom (i.e., psychophysical or sensorial) layer
consisted of the following terms: warm, rough, hard, dry,and sticky. These adjectives are very similar to thoselocated in the bottom layers of our Figs. 4, 5, and 6.
Some similarities can also be found between Chen et al.’s

study and ours in relation to the middle and upper layers.
Their middle layer included delicate and simple adjectives that also appeared in the middle layer of our model.
In addition, indulgent and relaxing adjectives in Chen etal.’s model and the friendly–unfriendly and comfortable–
uncomfortable nodes in the middle layer of our model
may be considerably similar. The highest layer in Chenet al.’s model included exciting, pleasurable, playful,premium, and precious adjectives. Our structures have
similar adjectives, i.e., happy–sad, good–bad, and rich–poor located in the highest layer, and exciting–boring inthe second highest layer in the structure of group B.
Overall, eight of the 17 adjectives (warm, rough, hard,dry, sticky, delicate, simple, and exciting) in Chen et al.’smodel are consistent with our model in terms of theliteral consistency of words and their locations in thelayered structure. Considering the similarities in themeanings of words, an additional five words (relaxing,
precious, premium, indulgent, and playful) in Chen et al.’smodel are also regarded as consistent with our model.
We did not offer the exact adjectives used by Chen et al.;however, these abovementioned analogies suggest thatthe correlation-based method and our method, based on
the effect matrix, would produce very similar semantic
structures of texture-related adjectives. These structures
were robustly established despite differences in material
sets and demographics between the two studies, which
supports the general validity of the structures used inthese studies.
On the other hand, the natural node was included in
the layer above the psychophysical one in Chen et al.’smodel, whereas our natural–artificial node was filteredout owing to its weak impacts on the other nodes. Thisdifference in the natural–artificial node may be attributed
to differences in the analyses. A correlation-based
method, which was used by Chen et al. [6], does not
specify the causal relationships among adjectives; hence,
adjectives can be coincidently connected without regard
to actual causalities, especially when the number andcategories of material samples are limited. In contrast,
our method was based on effect matrixes and can beused to establish causality. Further, the material samples
were designated as being used for food packaging in
Chen et al.’s study [6], whereas the usage of materials
was not specified in our study. Thus, Chen et al.’s
participants might have judged whether the material
samples were natural or artificial for food packaging
purposes, whereas such judgment criteria were notprovided in our study.

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Specification of Individuality in Causal Relationships among Texture-related Attributes, Emotions, and Preferences

5. DISCUSSION
5.1 Individuality tends to lie in the middle (attribute/
emotion) and higher (preference) layers
It should be noted that our results are based on a small
sample group of participants from the same generationand nationality. A larger variety of experimental participants may lead to a larger number of subgroups than thethree found in this study. All three groups tested in thisstudy included the same types of nodes in the bottom—or
psychophysical—layer, indicating that the variance acrosspeople at this basic sensory level is minimal. Nonetheless,as suggested in earlier studies [1, 16, 22], individuality
should not be neglected even in this layer. Comparedwith the bottom layer, the contents of the middle andhigher layers varied significantly between groups A and
B. The middle layer in group A was flat, and the causal
relationships among the nodes within this layer were notclearly shown. In contrast, the middle layer in group B
encompassed a larger number of nodes and an additional
sublayer, suggesting that some of these nodes influenced
others in the middle layer. The types of node in the highest
layer were the same in all groups; however, they were very
differently affected by the nodes in the lower levels.
Therefore, this study computationally demonstrated that
individual variations in perceptual experiences tend to
apply at the affective and preferential levels, rather than at
the psychophysical level.
5.2 Graphical structures are based on the relative
importance of relationships
The individual differences specified in this study
originate from the magnitudes of reported causalities
among the 29 adjective dyads. Paths between relatively
weakly connected adjectives were filtered out; hence, it isa fallacy that the paths and nodes that did not appear in thegraphs were not felt by the participants. In fact, they were
just apparently ignored because they were perceived as
relatively unimportant.
In the graphical modeling with effect matrices, theapparent complexity of a depicted structure change withthe threshold value [23]; however, the general trends ofeach group in this study held true for a wide range ofthreshold values. Specifically, the types of nodes in eachlayer did not vary when the value ranged from 0.050 to
0.036, while when the threshold was smaller than 0.035,
the middle layer in each group yielded another sublayer.
In other words, more detailed structures of the middle
layer could be seen by considering minor effects between
the nodes. Even with such small threshold values, group B

generally indicated a structure that was more complicatedthan that of group A.
5.3 Caution regarding the use of the specified
individualities
The experimental participants touched flat materials
cut into square forms, and the established structures ofadjective dyads used in this study relied on these material
specimens. The higher levels, in particular, may havebeen more affected by the set of specimens because
emotional and preferential layers are determined not onlyby physical stimuli, but also by prior knowledge about
what the participant is touching. For example, the material
percepts of wood, ceramic, and metal are definitively
identified at the psychophysical level. However, theresponses at higher levels depend on the type of productor object. Ceramic dishes are widely used, whereas metal
ones are not so common in some cultures. On the other
hand, metal spoons and forks are widely familiar, whileceramic versions of these utensils are not. These types ofknowledge about products influence the layered structureof adjectives, so the structures acquired in this study may
change according to the type of product. To explain suchchanges in the structure, we need to introduce factors
relating to cultural background or properties of items
tested in the experiment. In the field of marketing, thedependence of preferences on multiple aspects of humanlife styles and products is widely acknowledged [24, 25].
Further, it should be noted that the established structures
were grounded on the meanings of adjectives. Hence, thestructure is semantic and should not be related to theneural processing of touch-related information.
Another problem in the construction of the layered
model is the difficulty of its validation because there does
not exist a reference model, known as a golden standard,
for the purpose of comparison. In general, the validationof the causal model of social and industrial problems isbased on agreement and discussion among experts [23].
However, such validation is not the case for the texturerelated perception and feelings model, for which experts
are unlikely to exist. One effective method to compensate
for this absence would be comparison of models established by different approaches. As previously mentioned,
the structural model acquired in Chen et al.’s study [6]and our model largely agreed with each other, supporting
the validity of the models. Another possible approachis comparison of the models established from the same
participants and tasks but with different material sets,
which would allow us to test the robustness of the model
against the material sets. Further, more direct comparison

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between our approach and that of Chen et al., [6] involving the same participants and material sets, would beinteresting to further test the characteristics of the layered
models based on the two different approaches.
6. CONCLUSION
For product design, it is invaluable to specify thecausal relationships among the material perception andvarious customer values, such as affective, emotional,
and preferential factors that are experienced by interactingwith the material. However, there exist a limited numberof reports and methods to build the semantic structure ofsuch intrinsically multilayered and multidimensional
human experiences. Furthermore, thus far, individual
differences in the structure have not been computationallyidentified. We extended a method using effect matrices
to define the causality between the adjective words, inorder to establish the layered structure of touch-related
experiences and realized the analysis of individuality.
Furthermore, to specify meaningful differences, weclustered the matrices acquired from all the participants
into statistically different subgroups. To test these methods,
we investigated the layered structures of the 11 university
students and classified them into three groups, withadjective words placed into three or four layers. The
bottom layer, which is semantically the cause of otherpersonal experiences, was composed of words pertaining
to the percepts of physical quantities of materials, such as
rough–smooth, uneven–flat, hard–soft, warm–cold,
sticky–slippery, and wet–dry. The structure of this layer,
which was named the psychophysical layer, was very
similar across all participant groups, indicating that
relatively little individuality exists in this layer. On theother hand, groups A and B differed substantially in termsof the middle layers, which related to the perceived
attributes of material and emotional experiences, as
described by dyads such as strange–usual, delicate–bold,
simple–complex, general–special, beautiful–ugly, andexciting–boring. The middle layer of one participantgroup was flatly configured, suggesting that the adjectives in this layer weakly impacted on each other;
however, the other group constructed an additional
sublayer within the middle layer, meaning that strongcausalities between the words exist within this layer.
All groups listed the same adjective dyads in the highest,or resultant, layer, as the terminal nodes of the causality
graph. They were happy–sad, good–bad, like–dislike, andrich–poor, and were expressions that largely related topersonal preference. Although these four dyads weretypically included in the highest layer, they were very
differently impacted on by the lower layers between thegroups. These results indicate that the middle and top
layers yielded individual differences in touch-related
experiences across the tested 11 university students.
Such differences were computationally specified inthis study for the first time. Modeling the personal characteristics of experiences will aid comprehension of diverse
responses across people toward the same material, andestablishment of marketing strategies for products.
ACKNOWLEDGEMENTS
This study was in part supported by MIC SCOPE(142106003).

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Shogo OKAMOTO
Shogo Okamoto received MS and PhD degrees in
information sciences in 2007 and 2010, respectively,from the Graduate School of Information Sciences,
Tohoku University. Since 2010, he has been with the
Department of Mechanical Science and Engineering,
Nagoya University, Japan. His research interests
comprise haptics, human assistive technology, and wearable robots.

Hikaru NAGANO (Member)
Hikaru Nagano received B.S., M.S. and Ph.D
degrees in engineering from Nagoya University in2010, 2012, and 2015, respectively. Currently, he isan Assistant Professor in the Graduate School ofInformation Sciences, Tohoku University, Japan.
His research interests comprise human perception
and man-machine interfaces.

Kensuke KIDOMA
Kensuke Kidoma received a B.S. degree from
Nagoya University in 2015. His research interests
comprise multivariate analysis.

Yoji YAMADA
Yoji Yamada received a Ph.D. degree from theTokyo Institute of Technology in 1990. He had been
an associate professor at the Toyota Technological
Institute, Japan since 1983. In 2004, he joined the
National Institute of Advanced Industrial andScience Technology (AIST), as a group leader of the
Safety Intelligence Research Group at the Intelligent Systems ResearchInstitute. In 2008, he moved to the Graduate School of Engineering,
Nagoya University, as a professor.