Haptic Invitation of Textures:
An Estimation of Human Touch Motions
Hikaru Nagano, Shogo Okamoto, and Yoji Yamada
Department of Mechanical Science and Engineering, Graduate School of Engineering,
Nagoya University, Japan

Abstract. Some textures invite human touch motions in daily life, but
studies on the methodology of designing such textures have just been
initiated [1,2,3]. However, there is still no method of identifying various
touch motions invited by these textures. For example, some textures arelikely to invite stroking, while others are likely to invite pushing. We
developed a Bayesian network model that represents the probabilistic
relationships between texture-invited touch motions and properties oftextures. We interpreted the constructed model and conﬁrmed that themodel is potentially useful for the estimation of human motions.
Keywords: Bayesian Network, Stochastic Reasoning.

1

Introduction

Some textures appeal to human touch. Examples of such textures are smooth andcomfortable surfaces such as silk, elastic sponges, and ﬁnely woven cloths. Occasionally, such textures invite human touch; that is, people feel compelled to touchthem. These textures are potentially useful for designing products that invite human touch motion and interfaces that stimulate human interest. Nevertheless,such textures and human responses have rarely been investigated. Nagano etal. [1,2], who investigated properties of textures that appeal to human touch,revealed that the linear combination of physical factors of textures describedthe degrees of haptic invitation with accuracies of 70–80%. Although glossinessand surface shape types strongly aﬀected the haptic invitation, surface colors
barely aﬀected them. Klatzky and Peck [3] also investigated relationships between objects’ properties and their appeal to human touch and reported that
simple objects invited human touch rather than complex objects, and that people
wanted to touch moderate objects more than rough objects. Based on the continuity of these studies, the methodologies for determining the best combinationsof physical factors in terms of appeal to human touch will be established.
The results of past studies mentioned above will enable us to design texturesand objects that appeal to human touch. However, exactly how people touchthese textures is still not known. For example, hard pushing and soft stroking
are just two of many touch motions. The touch motion for elastic rubber may be
diﬀerent from that of smooth fur. Some researchers have analyzed hand motions
P. Isokoski and J. Springare (Eds.): EuroHaptics 2012, Part I, LNCS 7282, pp. 338–348, 2012.
c Springer-Verlag Berlin Heidelberg 2012

Haptic Invitation of Textures: An Estimation of Human Touch Motions

339

of touch. For example, Lederman and Klatzky [4] investigated relationships between hand movements during haptic object exploration and desired propertiesof objects, such as heaviness and hardness, and Peine et al. [5] analyzed ﬁnger
motions during surgical palpation. However, touch motions invited by textures
have not yet been investigated. If the relationships between touch motions andproperties of textures are revealed, we can design not only textures that appeal
to human touch but also texture-invited touch motions. For example, we could
design textures that are more likely to invite speciﬁc touch motions such as
stroking or pushing.
The objective of this study was to develop a stochastic model that represents
the relationships between texture-invited touch motions and properties of textures. We observed such human touch motions through experiments; thereafter,
we developed a Bayesian network, which enabled us to estimate the propertiesof textures from observed touch motions and vice versa.

2

Experiments

We conducted two types of experiments. In Experiment 1, participants evaluated textures consecutively using a semantic diﬀerential (SD) method. Sensory
properties of textures acquired here were used in constructing the stochastic
model. In Experiment 2, participants touched textures that had a strong appeal
to human touch. Human touch motions were measured using a camera and a
six-axis dynamic force sensor. Features of touch motions were also applied to thestochastic model. The details of each experiment are described below:
2.1

Experiment 1: Sensory Properties of Textures

Participant Five laboratory students, excluding the authors, approximately
twenty years of age participated in Experiment 1.
Task: Sensory Evaluation. The participants evaluated the textures usingﬁve-point scales in terms of ﬁve adjective pairs: “rough-smooth,” “uneven-ﬂat,”“hard-soft,” “warm-cold,” and “sticky-slippery,” without touching them. The
evaluation sheets provided both English and Japanese terms.
As shown in Fig. 1a, a large white plate with an 80 mm × 80 mm squarewindow was placed on a texture so that participants could see only the surfaces
and not the sides of the samples. The participants were instructed to keep theirhead positions ﬁxed in order to retain the relative position between the head andtextures. The textures and adjective pairs were presented to each participant in
random order.
Stimuli. Preliminary experiments were conducted in order to measure the degrees of aﬃnity for various textures. For details of measurement method of degrees, refer to our articles [1,2]. The textures whose degrees of haptic invitation
varied signiﬁcantly depending on individuals were eliminated through experiments. This process led to the ﬁnal thirty textures used in Experiments 1 and 2.

340

H. Nagano, S. Okamoto, and Y. Yamadaa) Sample displayed in Experiment 1
Side view

White plastic plate

Sample texture
80 mm
80 mm

90 mm

b) Samples displayed in Experiment 2
White plastic plate
80 mm
80 mm 20 mm
Fig. 1. Sample textures and presentation method
Table 1. List of textures
Group

TextureGroup
TextureGroup
TextureFake alligator hide
Crumpled paper
Fine woven cotton
Cloth
Fake suede 1
Coarse woven cotton
Fine Japanese paper Leather
Coarse Japanese paper
Fake suede 2
Towel
Towel
Long hair fake fur
Paper Embossed paper 1
Felt
Felt
Embossed paper 2
Fur
Short hair fake fur
Boa
Fake boa
Fake paper
Soft fake fur
Artiﬁcial grass
Artiﬁcial grass
Fine woven straw
Wall paper
Metal
Woven wire mesh
Straw andFiberCoarse polyester ﬁber
Walnut wood
Coarse woven straw
rush grass
Cypress wood
Woven rush grass
Wood
Soft sponge
Chestnut wood
SpongeCork board
Hard sponge

These textures, which are listed in Table 1 and shown in Fig. 2, include a fake
fur with long hair and a ﬂat Japanese paper. The textures were 90 mm × 90
mm squares. Flexible textures, such as cloths, were attached to a plastic platewith double-sided tape.
Analysis: Adjective Ratings for Thirty Textures. Ratings of 1 to 5 wereassigned to the ﬁve-point adjective scales measured in Experiment 1. The ratingof each adjective pair was normalized within a single participant to reduce theinﬂuence of individual diﬀerences in criteria for judgment. Ratings were thenaveraged across all the participants.
2.2

Experiment 2: Observation of Touch Motions to Textures

Participant. Another ﬁve laboratory students, excluding the authors, aged
approximately twenty years, participated in Experiment 2.

Haptic Invitation of Textures: An Estimation of Human Touch Motions

10 mm
Crumpled paper

10 mm

10 mm

10 mm

10 mm

Fine
CoarseJapanese paper Japanese paper

Embossed
paper 1

Embossed
paper 2

10 mm

10 mm

10 mm

10 mm

10 mm

Fake paper

Wall paper

Walnut wood

10 mm

10 mm

10 mm

10 mm

10 mm

Cork board

Fakealligator hide

Fake suede 1

Fake suede 2Long hair
fake fur

10 mm

10 mm

10 mm

10 mm

Fine woven
straw

Coarse woven
straw

Short hair
fake furSoftfake furCypress wood Chestnut wood

10 mm
Woven
rush grass

10 mm

10 mm

10 mm

10 mm

Soft sponge

Hard sponge

Fine woven
cottonCoarse woven
cotton

10 mm

10 mm

10 mm

10 mm

10 mm

Fake boa

Artificial grass

Wovenwire mesh

Coarsepolyester fiber

Felt

Fig. 2. Thirty textures used in experiments

10 mm
Towel

341

342

H. Nagano, S. Okamoto, and Y. Yamada

X

CameraZ

Metal plate

Side View
TextureWhite plastic plate
MarkerX
Six-axis dynamic force sensor
Y Z
TexturesFig. 3. Measurement of touch motions to textures

Task. In Experiment 2, participants touched one texture among three texturesthat most strongly appealed to them. These textures were placed under thewhite plastic plate, as shown in Fig. 1b. If only one texture was shown in theexperiments, participants were forced to touch the texture. In order to avoid thisunnatural situation, three textures were shown to the participants. Participants
were instructed to keep their eyes closed until they heard a beep sound, and thenthey were free to touch one of three textures.
Stimuli. Three textures were selected through preliminary experiments thatwere described in Sec. 2.1. The preliminary experiments revealed the rankingof thirty textures in terms of measured degrees of haptic invitation. One of
the three textures was selected from ten textures that exhibited the highest
degrees of haptic invitation. Another texture was selected from the lowest ten
textures, and the last was selected from the remaining ten textures. In total, ten
combinations were presented to each participant in random order. Each texture
was used only once, and each participant received a unique set of combinations.
Measurement Method for Touch Motions. Human touch motions to textures were measured in Experiment 2. Tip positions of the index ﬁnger were measured using a camera (Playstation Eye, Sony co., Tokyo, Japan), as depicted inFig. 3. The position was detected using a red marker ﬁxed on the nail of the index
ﬁnger. The camera resolution was 320 × 240 pix, which corresponded to a position resolution of 1.03 mm, and the frame rate was 30 fps. Contact forces were
measured by a six-axis dynamic force sensor (MINI 2/10, BL AUTOTEC. LTD.,
Kobe, Japan), as shown in Fig. 3. The sensor was ﬁxed under a metal plate on
which textures were placed. Contact force was recorded at a sampling frequencyof 50 Hz.

Haptic Invitation of Textures: An Estimation of Human Touch Motions

3

343

Construction of Bayesian Network Model

In order to estimate touch motions from the properties of textures, we constructed a model that represents relationships among them. Considering thata certain texture frequently invites stroking while another may not, the probabilistic relationships are appropriate for representing connections. In addition,
the causal connections are not previously established in relationships. Therefore,
we adopted a Bayesian network to represent relationships between human touch
motions and sensory properties of textures. Bayesian networks enable us to estimate unobserved states of variables using observed variables. In our case, weestimated properties of textures that are likely to invite speciﬁc touch motions.
First, we extracted feature variables that constituted a network from sensory
properties of textures and measured touch motions. Second, a Bayesian networkmodel was constructed using these variables as nodes. Finally, the constructed
model was interpreted.
3.1

Feature Extraction

The feature variables that became nodes of the Bayesian network are described,
and all nodes were discrete variables.
Material Type. Thirty textures were classiﬁed into thirteen groups on thebasis of their materials, as shown in Table 1. As a result, the node representing
material types was quantized into thirteen levels in the following manner:
Material Type: Paper, wood, leather, fur, straw and rush grass, sponge, cloth,
towel, felt, boa, artiﬁcial grass, metal, or ﬁber.
Sensory Properties of Textures. We produced ﬁve nodes on the basis ofadjective ratings “rough-smooth,” “uneven-ﬂat,” “hard-soft,” “warm-cold,” and“sticky-slippery,” which were acquired in Experiment 1. These ratings were quantized into two levels across their averages. For example, the standardized “roughsmooth” rating of short-hair fake fur was -1.8 (+: rough; -: smooth), which was
lower than the average 0. Therefore, the Micro Roughness node of short-hairfake fur was labeled “smooth.” On the other hand, the standardized “hard-soft”
rating of walnut wood was 1.3 (+: hard; -: soft), which was higher than the
average 0. Thus, the Hardness node of walnut wood was assigned the “hard”
label. The ﬁve adjective ratings were quantized in the following manner:
Macro Roughness: Flat or bulky
Micro Roughness: Smooth or roughHardness: Soft or hard
Warmness: Cold or warm
Friction: Sticky or slippery

344

H. Nagano, S. Okamoto, and Y. Yamada

AT: Arrival Time

CT: Contact Timea) An example of Z-axial force (One touch mode)
AT: Arrival Time

CT: Contact Time

Stroking

Pushingb) An example of Z-axial force (Two touch modes)
FF: Frequency of Fzc) An example of force spectra

Fig. 4. Examples of Z-axial force and force spectra

Properties of Touch Motions. Human touch motions were measured using
the camera and force sensor. From measured data, we produced eight feature
variables. The ratings of each feature variable were normalized within a single participant and were then averaged across all participants. All nodes werediscretized into two qualitative states across each average.
Two examples of Z-axial force and an example of force spectra are presented
in Figs. 4a, b, and c, respectively. An example of distribution of ﬁngertip position
in a two-dimensional space (X-Y space) is depicted in Fig. 5. The following nodes
were extracted from these force and position data:
Arrival Time: Short or long

Haptic Invitation of Textures: An Estimation of Human Touch Motions

345

MD: Movement DistanceFig. 5. An example of distribution of ﬁnger position

As depicted in Fig. 4a, the Arrival Time node was determined from a period
between the starting time of the experiment (t = 0) at which a sound cue
was presented to participants and the time at which the force signal beganchanging. The Arrival Time node was quantized into two levels across its averageof normalized values 0. For example, the normalized value of Arrival Time forcoarse Japanese paper was 2.0 (+: long; -: short), which was higher than zero.
Therefore, the Arrival Time node of coarse Japanese paper was assigned the“long” label.
Contact Time: Short or long
As depicted in Fig. 4a, the Contact Time node was determined as the period during which the texture was touched. The time was quantized into twolevels: long and short. For example, for cypress wood, the standardized contact
time of -1.2 (+:long; -:short) was assigned the “short” label.
Average Z-axial Force: Weak or strong
The Average Z-axial Force node was determined from the average Z-axial force
while in contact with the textures.
Maximum Z-axial Force: Weak or strong
The maximum Z-axial force during contact determined the Maximum Z-axial
Force node.
Frequency of Z-axial Force: Low or high
The Frequency of Z-axial Force node was determined from a peak frequencyof force spectra, as depicted in Fig. 4c.
Movement Distance: Short or long
As depicted in Fig. 5, we produced a minimum rectangle area surrounding a
distribution of the ﬁngertip position. The Movement Distance node was determined from a diagonal of the area.

346

H. Nagano, S. Okamoto, and Y. Yamada

Average Hand Velocity: Slow or fast
We calculated ﬁngertip velocities from time series data of the ﬁngertip position. The Average Hand Velocity node was determined from the average ﬁngertip
velocity while in contact with the textures.
Maximum Hand Velocity: Slow or fast
The maximum ﬁngertip velocity while in contact with each texture determined
the Maximum Hand Velocity node.
Mode of Touching Textures
Touch Mode: Soft touch, stroke, push, or scrub
We produced the Touch Mode node in order to quantize the modes of touch
into four levels. This node was determined from combinations of the Maximum
Z-axial Force and Movement Distance nodes. If the Maximum Z-axial Forcenode was strong and the Movement Distance node was long, we determined that
the Touch Mode node was “scrub.” Similarly, we determined three modes oftouching: “push” (Maximum Z-axial Force: strong, Movement Distance: short),“stroke” (Maximum Z-axial Force: weak, Movement Distance: long), and “softtouch” (Maximum Z-axial Force: weak, Movement Distance: short).
Change in Touch Mode: Not changed or changed
As depicted in Fig. 4b, the touch mode changed during exploration for some
textures and participants. We produced the Change in Touch Mode node in order
to diﬀerentiate examples in which a touch mode changed from those in which themode did not change. This node was quantized into two levels by the majority
mode.
3.2

Structure Learning

We developed a Bayesian network model using the measured data in Experiments 1 and 2. For learning the network model, K2 algorithm and Bayesian
information criteria were used. The constructed network is presented in Fig. 6.
3.3

Interpretation of Structure

We brieﬂy interpreted the constructed network on the basis of some examples.
The network that obtained the evidence of stroke touch mode estimated theprobability of the Friction node. The probability of the Friction node being
slippery was 0.75. The examples of corresponding textures are the short-hair fake
fur and felt, which are likely to invite the stroking motion with weak Maximum
Z-axial Force and long Movement Distance.
The network that obtained the Touch Mode node of “scrub” estimated thatthe probability of the Micro Roughness being rough was 0.88. The corresponding
textures, such as coarse woven straw, are likely to invite the scrubbing motion
with strong Maximum Z-axial Force and long Movement Distance.

Haptic Invitation of Textures: An Estimation of Human Touch Motions
Macro
Roughness

Material
Type

Micro
Roughness

Hardness

Warmness

347

Propertiesof Textures

Friction

ArrivalTime

Movement
Distance

Maximum
Z-axial Force

Maximum
Hand Velocity

Touch Mode

Contact
Time

Average
Z-axial Force

Frequency ofZ-axial Force

Change inTouch Mode

Average
Hand Velocity

Touch
MotionsFig. 6. Bayesian network model

The network that obtained the Frequency of Z-axial Force node of “high”
estimated that the probability of the Macro Roughness being ﬂat was 0.70. The
walnut wood and fake suede 1 are examples of corresponding textures, which are
likely to invite the high Frequency of Z-axial Force.
The network that obtained the Maximum Hand Velocity node of “fast” estimated that the probability of the Macro Roughness being ﬂat was 0.69. The
corresponding textures, such as cypress wood and artiﬁcial grass, are likely toinvite the fast Maximum Hand Velocity.
When the network obtained the Hardness node of “soft” and the Warmness
node of “cold,” the probability of the Touch Mode being a soft touch mode wasestimated to be 0.57. The crumpled paper is an example of a corresponding
texture.
The above estimations suggested that the network is potentially useful for
designing textures that invite speciﬁc human touch motions. However, a detailed
validation of the networks is the next challenge.
In this constructed network, the Material Type node was isolated perhaps
because the even textures belonging to the same material category are very
diﬀerent. For example, although the crumpled paper and embossed paper 2 areboth in the paper category, their four sensory property nodes excluding the Micro
Roughness node were very diﬀerent. Further, the Contact Time and Average Zaxial Force nodes were not connected with the other nodes. These nodes havethe potential to connect with the nodes we did not use in this study.

4

Conclusion

We investigated the stochastic relationships between touch motions and theproperties of thirty textures that invite human touch. The sensory properties of348

H. Nagano, S. Okamoto, and Y. Yamada

textures were measured through sensory evaluation, and the features of humantouch motions were determined from experiments in which participants freely
touched the textures they felt compelled to touch. The Bayesian network model
was constructed using these features. A brief interpretation suggested that the
network is potentially useful for designing textures that invite speciﬁc humantouch motions, such as a stroke or push. A re-construction of networks with asuﬃcient number of data and a detailed validation of the networks are the nextchallenges.

References
1. Nagano, H., Okamoto, S., Yamada, Y.: Physical and sensory factors of texturesthat appeal to human touch. In: Proceedings of the 4th International Conference on
Human System Interactions, pp. 324–329 (2011)
2. Nagano, H., Okamoto, S., Yamada, Y.: What appeals to human touch? eﬀects oftactual factors and predictability of textures on aﬃnity to textures. In: Proceedingsof the IEEE 2011 World Haptics Conference, pp. 203–208 (2011)
3. Klatzky, R.L., Peck, J.: Please touch: Object properties that invite touch. IEEE
Transactions on Haptics (to appear, 2012)
4. Lederman, S.J., Klatzky, R.L.: Hand movements: A window into haptic object recognition. Cognitive Psychology 19, 342–368 (1987)
5. Peine, W.J., Wellman, P.S., Howe, R.D.: Temporal bandwidth requirements fortactile shape displays. In: Proceedings of the IMECE Haptics Symposium, pp.
107–113 (1997)