This article has been accepted for publication in IEEE Transactions on Visualization and Computer Graphics. This is the author's version which has not been fully edited andcontent may change prior to final publication. Citation information: DOI 10.1109/TVCG.2024.3355413

JOURNAL OF LATEX CLASS FILES, VOL. XX, NO. X, AUGUST 2022

1

Elucidating Diurnal Patterns in Touch Desire using
Social Media Data toward Design of Haptic
Applications and Displays
Yusuke Ujitoko, Yuki Ban, and Takumi Yokosaka

Abstract—Advances in haptic technology have led researchersand engineers to seek out killer applications in which users canenjoy an experience of touch in AR/VR spaces. Such applications
will respond appropriately to human desire for haptic experiences(i.e., touch desire) and thus it is essential for researchers andengineers to understand the nature of people’s touch desires as
they arise in the course of daily life. In this study, we employed
Twitter data analysis to investigate a diurnal pattern in touch
desire. Our results showed that touch desire identiﬁed in andextracted from Twitter texts did reveal a diurnal pattern. Touchdesire tended to be at its lowest in the morning and increased as
the day progressed. The time at which it peaked varied with thespeciﬁc target of touch desire. Touch desire in relation to otherpeople and objects reached its peak at night, but touch desire
in relation to animals reached its peak at noon. These results
were conﬁrmed not only by our Twitter text analysis but also bydata from other social media and an online survey. In addition,
we found that the diurnal pattern of touch desire for each target
shows a strong correlation with that of visual desire for the sametarget. This suggests that the diurnal pattern of touch desire is
not limited to the sense of touch but is common to other sensory
desires for each target. Our ﬁndings suggest that researchers needto take the time of day into account when investigating touch
desire. Our ﬁndings also offer valuable insights for developers
into the design of haptic applications and displays that takes into
account the timing of daily peaks in touch desire.
Index Terms—Haptics, AR/VR, Touch Desire, Diurnal Pattern,
Touch, Psychology

I. I NTRODUCTION
Over the past few decades, with advances in visual andauditory technologies, the consumer market for augmented andvirtual reality (AR/VR) devices has grown signiﬁcantly. Haptic
technology is also a growing sector [1], [2] and is expected
to play a crucial role in providing multisensory feedback
to enhance the user experience in AR/VR. Although haptic
technology has been applied successfully in training (e.g.,
medical surgery) and assistance (e.g., walking navigation)
application domains, there is currently no killer application in
the entertainment domain [3]. Haptic researchers and engineers
are actively searching for potential applications in AR/VR in
which users can enjoy a touch experience.
Yusuke Ujitoko is with the NTT Communication Science Laboratories, Nippon Telegraph and Telephone Corporation, Atsugi, Japan. E-mail:yusuke.ujitoko@gmail.com
Yuki Ban is with the Graduate School of Frontier Sciences, the Universityof Tokyo, Chiba, Japan. E-mail: ban@edu.k.u-tokyo.ac.jp
Takumi Yokosaka is with the NTT Communication Science Laboratories, Nippon Telegraph and Telephone Corporation, Atsugi, Japan. E-mail:yokosaka.takumi@gmail.com
Manuscript received XXXXX XX, 2021

We focus on the human desire to touch a range of targets(e.g., other people, animals, and objects). By gaining a deeper
understanding of the nature of human desire to touch various
kinds of target (hereafter, we refer to this as “touch desire”),
haptic researchers and engineers can develop and design
experiences that can satisfy the fundamental human desire fortouch. This approach has the potential to spark the creation of
successful applications using haptic technologies in AR/VR.
While multiple studies have explored the targets of touch
desire [4], [5], [6], [7], [8], [9], the situational context of thetouch desire for different targets is also an important aspectthat must be understood to better support the design or development of haptic displays. For example, as the temporalityof the situational context in which we feel a desire for touchchanges, the requirements for haptic displays will also change.
For instance, haptic displays that satisfy night-time touch
desire should prioritize producing minimal noise to prevent
causing anti-social disturbance. In contrast, displays that areused during the day do not face this constraint, and may
utilize noisier components, such as air pressure compressors.
The situational context of touch desire can be explained withreference to the informational elements of the 5W1H (Who,
What, Where, When, How, and Why) method.
Among these elements, for the purposes of this study,
we focus on the “when” element to highlight the diurnal
pattern of touch desire. In recent years, the diurnal patternsof other sensory experiences have been reported in relation tofood consumption [10], listening to music [11], and watching
videos [12], [13]. In these studies, knowledge of such diurnal
patterns was expected to be applied to tasks such as content
recommendation or bandwidth control for online streaming
services. It is expected that touch desire also exhibits diurnal
patterns, and a deeper understanding of them will reveal targets
that fulﬁll and do not fulﬁll touch desire for each time ofday. However, previous studies on touch desire have been
conducted under the implicit assumption that the time factor
has no inﬂuence on touch desire, and thus the time factor was
not controlled in the experiments those studies describe.
We utilize Twitter’s long-term data on a large scale, on
which we can perform an hourly analysis. In recent years, social media has gained attention as a tool for studying people’s
daily lives [14], [15], [16], [17]. Among the various social
media platforms, Twitter [18] is the one that is most widelyused in Japan and provides a valuable resource for dataset. It
allows researchers to search tweet texts using queries and to
extract large amounts of data that cannot be obtained throughThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

This article has been accepted for publication in IEEE Transactions on Visualization and Computer Graphics. This is the author's version which has not been fully edited andcontent may change prior to final publication. Citation information: DOI 10.1109/TVCG.2024.3355413

JOURNAL OF LATEX CLASS FILES, VOL. XX, NO. X, AUGUST 2022

laboratory experiments. Also, tweet texts are less sensitive
to experimenter effects and demand characteristics than dataobtained in laboratory experiments. On the other hand, thereis the possible drawback that the analysis results might beinﬂuenced by the characteristics of Twitter texts in general,such as a skewed user population. In this study, we took care
to validate the results of our Twitter analysis with other sourcesin order to enhance the reliability of our ﬁndings.
To understand the diurnal patterns of touch desire, we
conducted four analyses as follows:• In Analysis 1, we aimed to identify the common diurnal
pattern of touch desire in relation to a range of targets. To
this end, we analyzed tweet texts containing the phrase“want to touch ” (‘sawaritai’ in Japanese) posted in the
period from 2013 to 2019.
• In Analysis 2, we aimed to verify whether the common
diurnal pattern identiﬁed in Analysis 1 was speciﬁc to
Twitter. To this end, we analyzed posts on different social
media platforms. See details in Supplementary Note 2.
• In Analysis 3, we aimed to verify whether the diurnal
patterns found in Analysis 1 and veriﬁed in Analysis 2
were speciﬁc to social media. To address this question,
we conducted an online survey in which participants
were asked to report the timing of their touch desires fordifferent target categories. See details in Supplementary
Note 3.
• In Analysis 4, we examined whether the diurnal patternof touch desire was similar to that of desires in anothersensory modality: visual desire. To this end, we analyzed
tweet texts containing the phrase “want to see” (‘mitai’in Japanese).
Based on the results of the analyses, we discuss implications
for the design of haptic applications in AR/VR and haptic
displays.
II. R ELATED W ORKFirst, we will introduce previous studies investigating thedesire for touch. Then, we will introduce the concept ofcontent analysis using data from Twitter. Lastly, we will
introduce previous studies on diurnal patterns.
A. Touch Desire
Several studies have been conducted to clarify the targets
that activate people’s touch desire [4], [5], [6], [7], [8], [9],[19]. In our previous study, we utilized 150 days of Twitter
posts and identiﬁed popular targets of touch desire on averageover that period [4]. We found, for example, that people desire
to stroke the fur or hair of pet animals such as cats anddogs, and desire to hit musical instruments such as drums
and keyboards. Klatzky et al. [6] focused on the visual information of both texture and shape, and investigated how visual
information can encourage participants to touch an object. In
that study, “touch-ability” is deﬁned as the extent to which thevisual appearance of an object invites physical contact. Theirresults showed that objects with smooth surfaces and simple
shapes received higher touch-ability scores than those withrough surfaces and complex shapes. Nagano et al. [5] focused2

on textural properties and investigated how visual information
relating to textural properties can invite participant’s touch(haptic invitation). In that study, 24 artiﬁcial textures wereused, each with various differing features (surface colors,
gloss, shape type, and ridge/groove width). The results showedthat glossiness and surface shape affected the degree of haptic
invitation, while surface color did not.
While the above-mentioned experiments were conductedwithout any speciﬁc situational context, touch desire in thecontext of purchasing has also been investigated. Touch plays
an important role in increasing perceived quality as wellas purchase likelihood [20], [21], [22], [23]. It has also
been established that individuals differ in their need to touchproducts [24], [25]. Citrin et al. [25] created the “Need for
Tactile Input (NTI)” scale and found that higher levels of NTI
negatively affect the evaluation of an online shopping experience. Peck and Childers [24] have created another scale thatrecords individual preferences in terms of the need for touch(NFT). The scale consists of two subscales - the instrumental
scale, and the autotelic scale. Instrumental NFT corresponds
to the discriminative aspects of touch desire, originating fromgoal-directed motives to play a role in making a judgment
about whether to touch or not. In contrast, autotelic NFT
corresponds to the affective aspect of touch desire which isrelated to the hedonic feeling induced by touching a product.
Our previous study [4] suggests that the ratio of the affective
aspect to the discriminative aspect of the touch desire revealedin Twitter posts is usually dominated by affective touch, whichsuggests that Twitter users are somewhat autotelic.
B. Content Analysis using Twitter
Content analysis is an empirical research approach basedon existing content. Twitter as an information-sharing platform
records information shared by users and can reveal their everyday activities. There are a large number of Japanese-language
tweets on Twitter, posted by more than 46 million active users.
Recently, the text of Twitter posts has been widely used fora variety of purposes, from stock forecasts using sentiment
analysis [26] to an estimation of drug consumption [27].
As a content analysis technique, it is known that word
frequency lists provide an overview of the occurrence ofwords or phrases in a piece of text to identify the subject
focus of that text. In addition, triangulation of the results withdifferent analyses [28] helps us to strengthen the validity of
those results. Following the application of these methods to
the Twitter texts we used, we improved the reliability of ourresults by triangulating them with analyses using data fromother sources.
C. Diurnal Patterns
We conducted a comprehensive survey of previous studies
on the diurnal pattern of human touch behavior or feelings
related to touch, but only found a limited amount of relevant
content, related to thermal perception [29] and clinical matters [30], [31]. To the best of our knowledge, there has been
no study investigating the diurnal pattern of touch desire.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

This article has been accepted for publication in IEEE Transactions on Visualization and Computer Graphics. This is the author's version which has not been fully edited andcontent may change prior to final publication. Citation information: DOI 10.1109/TVCG.2024.3355413

JOURNAL OF LATEX CLASS FILES, VOL. XX, NO. X, AUGUST 2022

Recently, it has become possible to use data streamed online
to identify diurnal variation in human behavior related toother sensory modalities than touch. Music streaming eventson Spotify have been used for the identiﬁcation of diurnal
patterns in music content consumption [11], [32]. In the sameway, video streaming events on YouTube have been used
for the identiﬁcation of diurnal patterns in video content
consumption [12]. In contrast to these audio and visual senses,
however, there are no media that speciﬁcally serve the humansense of touch and thus, currently, it is difﬁcult to use datafrom streaming services to investigate the diurnal pattern oftouch-related behavior. Instead of streaming data, the present
study attempted to identify the diurnal pattern of touch desire
by analyzing the timestamps of the text data posted on Twitter.
III. A NALYSIS 1: D IURNAL PATTERN OF T OUCH D ESIRE
IN T WEET T EXTS
A. Extraction of Tweet Text
We used speciﬁc Japanese tweet texts posted between
January 1, 2013 and December 31, 2019. Since our earlier
study showed a signiﬁcant interference in the content of thesetweet texts due to the COVID-19 pandemic [33], we set theperiod to end just before the outbreak of COVID-19 at the endof 2019. Tweet texts containing the phrase “sawaritai (wantto touch)” were used. Retweets (messages re-posted verbatim
by a user that originated from someone else) and replies totweet texts were excluded. While we also attempted to use
other queries, such as “like to touch” or “feel good to touch”,
in order to indirectly capture the desire to touch, we foundthat the number of tweet texts extracted by these queries was
too small to yield useful results and we did not use thesetweet texts in the subsequent analysis. To exclude redundant
tweet texts posted by bots, we removed duplicate tweet texts.
We also excluded tweet texts that contained URLs to avoid
advertising and spam. At this stage, the number of tweet texts
was 1,037,119. The tweet texts were a subset of tweet texts
used in our previous study [33].
B. Preprocessing of Tweet Text
We performed preprocessing on the tweet texts for analysis.
First, hashtags and emojis were stripped from the tweet texts.
Next, we extracted words for touch targets from thetweet texts. Morphological analysis was performed using Juman++ [34], and dependency analysis was performed using
KNP [35] to identify the touch target words. In Japanese, thegrammatical subject or object of a sentence is often omitted, so
it is highly likely that touch target words could not always be
identiﬁed. In order to validate that the identiﬁed touch targetwords were nouns, we veriﬁed them against the word2vec
model, chive [36].
Among the tweet texts for which a touch target word was
successfully extracted by the aforementioned preprocessing,some did not express an actual desire for physical touch. For
example, “touching a mahjong tile” is an idiom in Japanese
expressing the intention to play mahjong, rather than a desire
to physically touch a mahjong tile. In order to exclude such
tweet texts, which could be regarded as noise for the purposes

3of this study, some kind of automatic ﬁlter would have been
optimal. However, it was difﬁcult to implement a system that
could accurately judge tweet content as noise. Conversely,
manually inspecting all the tweet texts would have been an
accurate but highly time-consuming procedure. Therefore, inthis study, we adopted a third option, using an approach
whereby a noisy word list was ﬁrst drawn up and tweet texts
containing words on that list were excluded from the dataset.
Touch target words were translated into English using
Google Translate. If a word was not suitable for academic
reporting, we retranslated it into a more suitable word. After
this series of preprocessing steps, the number of tweet texts
selected for analysis was 130,594.

C. Calculation of Tweet Proportion
For our analysis stage, we needed to analyze the occurrenceof tweet texts that expressed a touch desire. The challenge
was to remove the effect of unintended variations in the total
number of tweets posted on Twitter, which depends on several
factors such as the number of concurrent active users and thenumber of tweet texts posted per active user. This removal
operation would enable us to distinguish the temporal changes
in touch desire from unintended variations resulting from, forexample, the temporal change in the number of concurrent
active users.
To remove the effect of these unintended variations, we
calculated a “tweet proportion”, which represents the numberof tweet texts that reference touch desire as a proportion of an
estimated reference value of the total number of tweets posted
on Twitter. Though we would have liked to obtain an accurate
count of the total number of tweet texts to calculate this “tweet
proportion”, Twitter does not make data on the total numberof posted tweet texts publicly available. Therefore, following
the method adopted in our previous study [33], we chose toroughly estimate a reference value for the total number ofJapanese-language tweet texts posted on Twitter (hereinafter,
we refer to this estimate as the “pseudo-baseline”). To dothis, we counted tweet texts containing the ﬁrst letter in theJapanese hiragana syllabary, “あ (a)” posted within a randomly
selected period of 30 seconds for each hour of the day, andwe used these counts as the pseudo-baseline for that hour.
In total, the pseudo-baseline was measured 61344 times (=(6 years × 365 days + 1 year × 366 days) × 24 times).
See Supplementary Figure 1 for the hourly variations in thepseudo-baseline. We validated the accuracy of the pseudobaseline as an estimate of the total number of tweets by
conﬁrming a high correlation with three other sources (seeSupplementary Note 1, Supplementary Figures 2, 3, and 4),
obtaining Pearson correlations of at least 0.985 (p < 0.001).
Subsequently, the tweet proportion was calculated for analysis using the pseudo-baseline. For instance, the tweet proportion for the period from 1 am to 2 am on January 1st, 2013,
was calculated by dividing the number of tweets expressing
a touch desire posted during that period by the corresponding
pseudo-baseline value.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

This article has been accepted for publication in IEEE Transactions on Visualization and Computer Graphics. This is the author's version which has not been fully edited andcontent may change prior to final publication. Citation information: DOI 10.1109/TVCG.2024.3355413

JOURNAL OF LATEX CLASS FILES, VOL. XX, NO. X, AUGUST 2022

4

Fig. 1(A) shows the occurrence of the top 20 touch targets
as a proportion of all touch targets in tweet texts expressing
touch desire, and thus presents the most popular targets oftouch desire among Twitter users. The ranking of the mostpopular touch targets for the seven-year span of the data inthis study is consistent with that obtained from our previous
Twitter analysis conducted over a period of 150 days (seeFig. 4 in the previous study [4]).
We applied a target-wise normalization to the tweet proportion, obtaining a normalized tweet proportion for eachtouch target and each hour by dividing each tweet proportion
by its mean. Fig. 1(B) shows the diurnal pattern of thenormalized tweet proportion per target in terms of the hour ofthe day. Since the temporal variation of the normalized tweet
proportion for these targets appeared to show similar patterns,
we wanted to analyze the common diurnal pattern found across
many targets. To this end, we analyzed the mean diurnal
pattern, which is the diurnal pattern across all targets, as shownin Fig. 1(C) by the black dashed line. The number of samples
for each hour represented in the line in the graph was 7 years ×
12 months = 84. The graph shows a tendency in the variationof the mean normalized tweet proportion of touch desire. The
mean normalized tweet proportion between 10 pm and 2 am
was approximately three times larger than that between 5 am
and 6 am. To ﬁnd the statistically signiﬁcant differences in
the mean normalized tweet proportion between hour pairs,
we conducted a non-parametric bootstrap analysis [37]. We
used bootstrap sampling to calculate 10,000 mean differencesin the mean normalized tweet proportions. If the Bonferronicorrected 95% conﬁdence interval (CI) did not overlap zero,
we could conclude that the difference was signiﬁcant. See
Supplementary Figure 5 for the full statistical results. The
multiple comparison results show that the normalized tweet
proportion was signiﬁcantly smaller in the morning (5 am-7am) compared to other time periods. The normalized tweet
proportion exhibited a gradual increase from morning tomidnight. The normalized tweet proportion reached a local
maximum during the lunch-break period (12 noon-2 pm) anda global maximum around the middle of the night (11 pm-2
am).
In addition, we investigated the diurnal pattern of touch
desire in relation to the touch target category. Speciﬁcally,building on previous studies [4], [33], we manually categorized
the target words under six headings: “body part”, “animal”,“object”, “person”, “geometry”, and “temperature”. Table I
lists example words for each category. Our previous study [4]provides a detailed justiﬁcation for this classiﬁcation. Because
the number of tweet texts expressing touch desire towards
targets in the “temperature” and “geometry” categories wasinsufﬁcient (less than 0.5% of all tweet texts expressing
touch desire), we excluded these two categories from further
analysis.
We applied a target category-wise normalization to thetweet proportion, obtaining the normalized tweet proportionfor each target category and each hour by dividing each tweet
proportion by its mean. Fig. 1(C) shows the normalized tweet*(A) proportion of target in tweet texts indicating touch desire

D. Results

indicates*: mofu-mofu
something fluffy(B) diurnal pattern of normalized tweet proportion per target*

indicates*: mofu-mofu
something fluffy(C) diurnal pattern of normalized tweet proportion per target categorymean

body part
animalobjectpersonFig. 1. (A) Proportion of top 20 targets in tweet texts indicating touch desire.
(B) Diurnal pattern of normalized tweet proportion per target. (C) Diurnal
pattern of normalized tweet proportion of all targets (shown by black dashedline) and those of target category (shown by colored solid lines). Error bars
denote 95% CI.

proportions in terms of target category as colored lines. To
ﬁnd the statistically signiﬁcant differences between hour pairs
within each target category, we conducted a non-parametricbootstrap analysis. We used bootstrap sampling to calculate
10,000 mean differences in the mean normalized tweet proportions. If the Bonferroni-corrected 95% CI did not overlap
zero, we could conclude that the difference was signiﬁcant.
See full results in Supplementary Figures 6, 7, 8, and 9. The
multiple comparison results show the differing characteristics
between the target categories. In the case of the “body part”
and “person” categories, the diurnal patterns were similar toThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

This article has been accepted for publication in IEEE Transactions on Visualization and Computer Graphics. This is the author's version which has not been fully edited andcontent may change prior to final publication. Citation information: DOI 10.1109/TVCG.2024.3355413

JOURNAL OF LATEX CLASS FILES, VOL. XX, NO. X, AUGUST 2022

TABLE IE XAMPLE TARGETS FOR EACH CATEGORY.
categories

example words

body part

hand, hair, buttock, abdomen, ear, tongue, arm, ankle

animalcat, dog, animal, bird, tiger, sheep, carp, cow, hamster

objectbutton, keyboard, stamp, card, stroller, stone, iron, clock

personyou, people, them, child, female, character, daughter

geometryline, ditch, crack, border, edge, region, shape, surface

temperature

warmth, heat, temperature, hot air, body temperature

the mean diurnal pattern (as shown by the black dashed line in
Fig. 1(C)), and there was a peak in the middle of the night. In
the case of the “object” category, there was also a peak in the
middle of the night although the variation is less salient thanthat for the “body part” and “person” categories. In contrast to
the results in these three categories, night-time touch desire fortargets in the “animal” category was not signiﬁcantly higher
than it was at noon. In the case of targets in the “animal”
category, the normalized tweet proportion reached a peak value
around noon (12 pm to 1 pm).
In summary, our ﬁndings suggest that the expression oftouch desire in tweet texts exhibits a strong diurnal pattern,
potentially reﬂecting the rhythms of human behavior and daily
life. However, one might suspect that the observed variationin tweet proportion resulted from factors related to Twitter
rather than from genuine variations in touch desire. To further
examine this possibility, we conducted follow-up analyses (seeAnalysis 2 and Analysis 3 in Supplementary Notes 2 and
3). Through these analyses, it was shown that the results ofAnalysis 1 were not Twitter-speciﬁc but were common to othersocial media and online surveys.
IV. A NALYSIS 4: D IURNAL PATTERN OF T OUCH D ESIREAND V ISUAL D ESIRE
To better understand the diurnal pattern of touch desire, we
wanted to clarify whether, for the popular targets on Twitter,it was speciﬁc to the sense of touch or common with that ofother sensory desires for the target. Among the other sensory
modalities, we focused on visual desire for the comparison
because the touch targets extracted in Analysis 1 were allvisible. In terms of other sensory modalities, those targets were
not necessarily the ones that people encounter in their daily
lives. For example, “ears” usually do not make sounds or emit
scent, and are not a focus of the sense of taste.

A. Extraction of Tweet Texts Indicating Visual Desire
In this analysis, we compared the diurnal patterns of touch
desire and visual desire for the top 20 most popular targets
in terms of touch desire identiﬁed in the Twitter analysis (seeFig. 1(A) for the top 20 list). We extracted tweet texts thatindicated visual desire for the targets by using a query of“want to see [the target]” for each target. For example, we
extracted tweet texts indicating visual desire for a hand with
the query “want to see a hand (‘te wo mitai’ in Japanese)”.

5

B. Calculation of Tweet Proportion
We calculated the proportion of tweets mentioning visualdesire for each target using a pseudo-baseline, as we did for
tweet texts mentioning touch desire in Analysis 1.
C. Results
We calculated the normalized tweet proportion for eachtarget and each hour by dividing each tweet proportion by its
mean. Fig. 2 shows the normalized tweet proportion for touch
desire and visual desire for each target. Pearson correlation
coefﬁcient r of the normalized tweet proportion between touchand visual desires for each target is also shown in the same
ﬁgure. All the targets had high correlations (> 0.5) andthe correlation coefﬁcients were statistically signiﬁcant except
for the tail. This suggests that the diurnal pattern of touch
desire reﬂects the common nature of diurnal patterns in desires
independent of the type of sensory modality.
visual desire

touch desirer = .95

r = .94r = .86r = .84

r = .87

r = .92

r = .87

r = .95

r = .64

r = .81r = .90r = .86r = .90r = .63r = .85

r = .88r = .85

r = .80*r = .57(n.s.)

r = .76

indicates*: mofu-mofu
something fluffy

Fig. 2. The normalized tweet proportion of touch and visual desire foreach target. Pearson correlation coefﬁcient between touch and visual desire isshown on each graph.

V. S UMMARY OF F INDINGS AND L IMITATIONS
For the ﬁrst time, we have analyzed the diurnal patternof touch desire in daily life and discovered a signiﬁcantdiurnal variation. Our analysis of Twitter data revealed thatthe frequency of touch desire can vary threefold between
morning and night. Even so, most of the earlier studies thathave investigated touch desire [4], [5], [6], [7], [8], [9] did not
focus on its diurnal variation due to an implicit assumption oftime invariance. Our results suggest that there may be room
for reconsidering this assumption. It would be beneﬁcial for
future researchers to control the time of day when conducting
experiments. In our previous study, we examined temporalvariations in touch desire, focusing on how the COVID-19
outbreak impacted this desire [33]. In that study, we reported

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

This article has been accepted for publication in IEEE Transactions on Visualization and Computer Graphics. This is the author's version which has not been fully edited andcontent may change prior to final publication. Citation information: DOI 10.1109/TVCG.2024.3355413

JOURNAL OF LATEX CLASS FILES, VOL. XX, NO. X, AUGUST 2022

that the number of tweet texts indicating touch desire toward
animate targets increased by a factor of approximately 1.5
after the start of the COVID-19 outbreak (see Fig. 3 in the
study [33]), which is a smaller change than that in the diurnal
variation we observed in the present study (see Fig. 1(C)).
This suggests how closely touch desire is connected with thedaily rhythms of our lives.
We found that the diurnal pattern of touch desire changed
depending on the target category, which is consistent witha previous study that showed different diurnal patterns inappetite depending on the food category [38]. That study foundcharacteristic diurnal patterns in appetite for high-calorie foods(sweet, salty, starchy foods, fruits, and meats/poultry) but did
not ﬁnd signiﬁcant diurnal variation in appetite for vegetables.
Regarding sensory desires other than appetite, music consumption patterns have been shown to vary depending on thecharacteristics of the music [32], which indicates that desire
related to hearing may also have diurnal patterns that varydepending on the target of hearing. Given these reports, it issuggested that diurnal patterns associated with speciﬁc targets
may be a common phenomenon for sensory desires.
In our own study, the peak time for animal touch targets
was at noon, while the peak time for other target categories,
such as objects or body parts, was at night. This highlighted
the distinctive nature of the diurnal pattern of touch desire
in relation to animals. One speculation as to the reason fora peak in the desire to touch animal targets around noon isthat individuals who have pets such as cats and dogs, but
are temporarily separated from them due to work or school,
may experience an increased touch desire towards their pets
during their spare time at noon. This is consistent with aprevious study that has noted that physical separation fromanimate targets can trigger touch desire, as observed during the
COVID-19 pandemic [33], although the duration of physicalseparation periods was different in that study.
We also found a high correlation between diurnal patterns
for touch and visual desire. Therefore, we propose that themechanism behind the diurnal variation of visual desire may
be relevant to the diurnal pattern of touch desire. However, tothe best of our knowledge, there has been no prior research
investigating the diurnal pattern of visual desire that can be referenced. The common factor that affects both of these desires
is unclear, but it might be related to something independentof the senses (e.g., environmental factors such as the lighting
environment or human factors such as mood [17]).
One limitation of our results is the possible dependencyof our data on Japanese culture. Our results were obtained
from analyzing Japanese-language social media posts andresponses from an online survey of Japanese respondents (seeSupplementary Note 3), which may have introduced biases
speciﬁc to Japanese culture. An earlier study reported that therole of touch varies across cultures [39]; for example, in Italy,
hugging and kissing on the cheeks are common and accepted
forms of greeting, while in Japan, a proper greeting involves
bowing and, generally, physical contact is avoided [40], [41].
Therefore, it is possible that the results of this study may not
be generalizable to other cultural contexts. Further research isneeded to understand the role of touch in a wider range of6

cultural contexts.
Another limitation is the experimental design of the online
survey (see Supplementary Note 3), which may have inﬂuenced the results due to memory bias. Since it was impossible
for the crowdsourcing research agency that recruited participants for the online survey to control the time of day thatthe participants responded to the survey questions, we chose adesign in which we asked participants to select the time period
during which they desired to touch. If we could develop amethod in future studies for conducting an online survey thatallowed us to control the time of day that the participants give
their responses, the results of the online survey would be morereliable.
The effect of the COVID-19 pandemic on the diurnal patternis also a matter of interest. A comparison of the results ofAnalysis 1 and Analysis 3 (see Supplementary Note 3) mightprovide insights into that effect. In order to rule out any effectof the pandemic for Analysis 1, we used Twitter data from aperiod prior to the start of the pandemic. The online survey for
Analysis 3, however, had to be conducted after the outbreakof the COVID-19. The results of this survey did not reveal
major discrepancies with the results of the Twitter analysis,suggesting that the diurnal patterns of touch desire observedin our study may not have been signiﬁcantly affected by the
pandemic.
VI. I MPLICATIONS FOR A PPLICATION D ESIGN
The ﬁndings of this study are relevant to engineering
matters, particularly to the design of entertainment applications
in which users can enjoy touching certain targets. There are
two approaches that developers might adopt in applying our
ﬁndings regarding diurnal patterns of touch desire to the designof applications:• A) an approach where developers view diurnal variations
favorably and take advantage of diurnal variation• B) an approach where developers view diurnal variations
unfavorably and mitigate the diurnal variations
As an example of the ﬁrst approach, developers might aim
to satisfy touch desire at its daily peak by providing touch
events at the most suitable time, which may lead to a reductionof potential stress for individuals in their daily lives. Thisidea ﬁnds support in consumer research [42]. It is known
that individuals with a high autotelic need for touch (NFT)
often experience stress when touch is unavailable, driven by
their hedonic desire to interact physically with a target. If
this knowledge is applicable to contexts other than thosepertinent to consumer purchasing behavior, causing touch
events in AR/VR applications when touch desire is at its peak
may effectively reduce users’ potential stress. For instance,
in social VR applications such as VRChat [43], where users
can navigate virtual spaces and engage with virtual avatars,
future developments in haptic technology may allow users toexperience touch sensations. To reduce potential user stress,
developers can strategically optimize touch events by adjusting
the placement of virtual touch targets, such as characters or
animals, within the social VR worlds.
As another example of the ﬁrst approach, developers might
aim to reﬁne an application’s speciﬁcations by predictingThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

This article has been accepted for publication in IEEE Transactions on Visualization and Computer Graphics. This is the author's version which has not been fully edited andcontent may change prior to final publication. Citation information: DOI 10.1109/TVCG.2024.3355413

JOURNAL OF LATEX CLASS FILES, VOL. XX, NO. X, AUGUST 2022

the peak times of anticipated application usage based on
the diurnal patterns of touch desire. Consider, for example,
applications where users can enjoy touching virtual animals.
Our ﬁndings indicate that touch desires for animals tend to behigher during lunchtime breaks when people are more likely
to be outside, such as at school or work. This suggests thesigniﬁcance of tailoring the application design to reﬂect thepredicted situational context, i.e., outside at lunchtime. In such
scenarios, where the user is likely to be in a public space,
it would be desirable to create applications that are easilyand quickly accessible. Augmented reality (AR) applications
may be more suitable than virtual reality (VR) applications
since AR allows users to interact with virtual elements whileremaining aware of their physical surroundings. For example,
an application that enables pet owners to touch and interact
with a virtual pet displayed on their desk through AR glasses
during lunch breaks would be a user-friendly and appealing
solution. By predicting the times when applications will be
used, developers can consider speciﬁcations that enhancethe overall user experience and make the applications more
appealing.
In contrast to the ﬁrst approach, developers adopting thesecond approach regard the diurnal pattern of touch desire unfavorably. An example might be a developer aiming to equalize
the level of touch desire, even though it varies throughout
the day, in order to enhance the consistent reproducibilityof user experiences. In the context of consumer research, it
is known that levels of autotelic NFT, the degree of desire
for hedonic touch, inﬂuence the purchasing experience in
AR applications [44]. Accordingly, there is a possibility thatthe level of touch desire can inﬂuence the user experiencein AR/VR applications even if the same haptic stimuli areapplied. The consideration of this possibility is particularly
important in scenarios where the reproducibility of haptic
experiences is crucial, such as the following: applications
that let users revisit and relive cherished past moments, evenphysically interacting with past memories, such as touching
their child when they were younger. In such a scenario, if
the timing of the user’s touch interaction does not align withthat in their memory of the original touch event, the level ofdesire to touch may differ, resulting in varying experiences.
It is important to be able to replicate the timing of the user’s
touch interaction as closely as possible. Without addressing
this aspect, there is a possibility that the experience may nothave the intended effect.
VII. I MPLICATIONS FOR H APTIC D ISPLAY D ESIGN
Our ﬁndings also have signiﬁcant implications for haptic
display design, where numerous design aspects must be considered. According to a book that lists design aspects for
haptic displays [45] (see ﬁrst columns in Table VII), thesecan be broadly categorized into haptic presentation properties(e.g., degree of freedom, spatial resolution, and temporal
resolution) and logistic properties (e.g., portability, environmental requirements, and compatibility with other sensory
displays). Design aspects are related to each other and are
not independent. For example, “size” and “portability” would

7

be correlated. Also, it is important to note that there are tradeoffs between these design aspects. For example, increasing
the “degree of freedom” often results in a more complex
and bulkier display, potentially compromising its “portability”.
Requirement deﬁnition for design aspects that gives properconsideration to trade-offs between them is essential in theprocess of designing haptic displays.
If the situational context of a user’s interaction with adisplay, especially the timing of that interaction, is known,
developers can gain insights into the requirement deﬁnition
for design aspects. We comprehensively analyzed all design
aspects to determine whether and how the timing of a user’s
interaction with a display can provide insights for deﬁning
requirements (see Table VII). For example, regarding the“Grounding” design aspect, self-grounded displays are preferable if it is expected they will be used outdoors during theday. In such cases, hand-held, exoskeleton, or wearable display
types may be more suitable. Conversely, if the displays are
expected to be used at home late at night, then world-groundeddisplays will be acceptable. To give another example, thistime relating to “environmental requirements”, displays that
produce minimal noise to avoid disturbing neighbors will be
preferable if they are expected to be used at night. If they are
expected to be used during the day, displays generating loudsounds, such as those utilizing air pressure compressors, will
be acceptable.
Based on our ﬁndings, it is possible to estimate when users
are most likely to want to interact with displays that provide
a touch experience for certain speciﬁc targets. This estimation
is grounded in the idea that these displays are more likely
to be used during periods when touch desire is heightened.
For example, displays that simulate the sensation of touchinganother person may be more frequently used at night, whiledisplays that simulate the sensation of touching animals may
be more commonly used during the day. Some might beconcerned that this estimation is made under the assumption of
specialized haptic displays for certain targets. We consider thisassumption valid on the basis of the following rationale. Since
the set of haptic properties to be represented varies dependingon the touch target [46], and since current haptic displays
can only convey a limited number of haptic properties [47],haptic displays representing speciﬁc targets will have to be
specialized to those targets. The usage timing of a displaythat is specialized to a speciﬁc target can be estimated basedon the diurnal pattern of touch desire for that target.
The combination of the estimated usage timing and the insights for speciﬁc design aspects gained from a foreknowledgeof the usage timing (as shown in Table VII) helps developers deﬁne requirements. Developers can deﬁne requirements
systematically by following the example process below.
1) Deﬁne the target to touch through the display.
2) Gather information regarding the diurnal pattern of touch
desire toward the target.
3) Estimate the usage timing of the display based on thediurnal pattern.
4) Utilize Table VII to deﬁne requirements for speciﬁcdesign aspects.
5) Deﬁne requirements regarding other design aspects.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

This article has been accepted for publication in IEEE Transactions on Visualization and Computer Graphics. This is the author's version which has not been fully edited andcontent may change prior to final publication. Citation information: DOI 10.1109/TVCG.2024.3355413

JOURNAL OF LATEX CLASS FILES, VOL. XX, NO. X, AUGUST 2022

8

TABLE II
S UMMARY OF INSIGHTS INTO REQUIREMENTS FOR EACH DESIGN ASPECT [45] OF HAPTIC DISPLAY BASED ON USAGE TIMING OF THE DISPLAY.
Haptic Presentation Properties
Design aspect

Insights into requirements for each design aspect based on the usage timing

Kinesthetic cues

N/A

Tactile cues

N/A

Grounding(self-grounded or world-grounded)

If displays are used during the daytime (outdoors), self-grounded displays (e.g., handheld, exoskeleton, or wearable types displays) are preferable. If the displays are usedlate at night (indoors), the world-grounded displays are acceptable.

Number of display channels(how many points of contact with the user’s body)

N/A

Degrees of freedom

N/A

Form (shape)

N/A

Fidelity

N/A

Spatial resolution

N/A

Temporal resolution

N/A

Latency tolerance

N/A

Size (size of the display)

If displays are used during the daytime (outdoors), smaller display sizes are preferable.
This is related to other aspects such as portability, If the displays are used late at night(indoors), the grounded-type displays are acceptable.

Logistic Properties
Design aspect

Insights into requirements for each design aspect based on the usage timing

User mobility

N/AInterface with tracking methods

N/A

Environment requirements

If displays are used late at night, displays producing a loud noise are not preferred. For
example, displays with pneumatic actuators require air compressors that often make aloud noise, and these would not be suitable in this scenario. If displays are used during
the day, displays producing a loud noise may be acceptable.

Associability with other sense displays

N/A

Portability

If displays are used during the day (outdoors), portable displays are preferable. If thedisplays are used late at night (indoors), unportable displays are acceptable.

Throughput(time required for display attachment/detachment)

If displays are used during a short break from other activities, such as at lunchtime on awork day, displays should be attachable and detachable quickly.

Encumbrance

N/A

Safety

N/A

Cost

N/A

One limitation of this process is the absence of explicit
knowledge regarding the location in which the touch desire
occurs. We had to infer the location from timing informationin order to gain insights into design requirements. Identifying
the location of touch desire was a particular challenge in ourstudy because location data was missing from most of theTwitter text data. If future studies can investigate the locational
aspect of touch desire, it could be possible to more accurately
identify usage situations.
VIII. C ONCLUSION
In this study, we used Twitter data analysis to examine thediurnal pattern in touch desire. We found a common diurnal
pattern of touch desire toward all targets as well as speciﬁc
patterns that depended on the target of touch. We obtained
similar results using data from other social media platforms
and from participants in an online survey. In addition, ourﬁndings have engineering implications for haptic applicationsin AR/VR and haptic display. One potential direction for future
research is to determine whether these results can be replicatedin other cultural contexts.
R EFERENCES[1] T.-H. Yang, J. R. Kim, H. Jin, H. Gil, J.-H. Koo, and H. J. Kim, “Recent
advances and opportunities of active materials for haptic technologies invirtual and augmented reality,” Advanced Functional Materials, vol. 31,
no. 39, p. 2008831, 2021.
[2] H. Culbertson, S. B. Schorr, and A. M. Okamura, “Haptics: The present
and future of artiﬁcial touch sensation,” Annual Review of Control,
Robotics, and Autonomous Systems, vol. 1, pp. 385–409, 2018.
[3] A. G. Rodrı́guez Ramı́rez, F. J. Garcı́a Luna, O. O. Vergara Villegas, and
M. Nandayapa, “Applications of haptic systems in virtual environments:
A brief review,” Advanced Topics on Computer Vision, Control andRobotics in Mechatronics, pp. 349–377, 2018.
[4] Y. Ujitoko, Y. Ban, and T. Yokosaka, “Getting insights from twitter:what people want to touch in daily life,” IEEE Transactions on Haptics,
vol. 15, no. 1, pp. 142–153, 2021.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

This article has been accepted for publication in IEEE Transactions on Visualization and Computer Graphics. This is the author's version which has not been fully edited andcontent may change prior to final publication. Citation information: DOI 10.1109/TVCG.2024.3355413

JOURNAL OF LATEX CLASS FILES, VOL. XX, NO. X, AUGUST 2022

[5] H. Nagano, S. Okamoto, and Y. Yamada, “Visual and sensory propertiesof textures that appeal to human touch,” In Proceedings of International
Journal of Affective Engineering, vol. 12, no. 3, pp. 375–384, 2013.
[6] R. L. Klatzky and J. Peck, “Please touch: Object properties that invite
touch,” IEEE Transactions on Haptics, vol. 5, no. 2, pp. 139–147, 2012.
[7] H. Nagano, S. Okamoto, and Y. Yamada, “Haptic invitation of textures:
Perceptually prominent properties of materials determine human touch
motions,” IEEE Transactions on Haptics, vol. 7, no. 3, pp. 345–355,
2014.
[8] A. Metzger, M. Toscani, A. Akbarinia, M. Valsecchi, and K. Drewing,“Deep neural network model of haptic saliency,” Scientiﬁc reports,
vol. 11, no. 1, pp. 1–14, 2021.
[9] M. Lau, K. Dev, W. Shi, J. Dorsey, and H. Rushmeier, “Tactile meshsaliency,” ACM Transactions on Graphics, vol. 35, no. 4, pp. 1–11, 2016.
[10] C. Spence, “Explaining diurnal patterns of food consumption,” Food
Quality and Preference, vol. 91, p. 104198, 2021.
[11] M. Park, J. Thom, S. Mennicken, H. Cramer, and M. Macy, “Global
music streaming data reveal diurnal and seasonal patterns of affective
preference,” Nature human behaviour, vol. 3, no. 3, pp. 230–236, 2019.
[12] K. Pires and G. Simon, “Youtube live and twitch: a tour of usergenerated live streaming systems,” in Proceedings of the 6th ACM
multimedia systems conference, 2015, pp. 225–230.
[13] C. Hansen, C. Hansen, L. Maystre, R. Mehrotra, B. Brost, F. Tomasi, and
M. Lalmas, “Contextual and sequential user embeddings for large-scalemusic recommendation,” in Proceedings of the 14th ACM Conference
on Recommender Systems, 2020, pp. 53–62.
[14] E. Kouloumpis, T. Wilson, and J. Moore, “Twitter sentiment analysis:
The good the bad and the omg!” In Proceedings of the International
AAAI Conference on Web and Social Media, vol. 5, no. 1, July 2011.
[15] E. D’Andrea, P. Ducange, B. Lazzerini, and F. Marcelloni, “Real-time
detection of trafﬁc from twitter stream analysis,” IEEE Transactions onIntelligent Transportation Systems, vol. 16, no. 4, pp. 2269–2283, 2015.
[16] S. B. Park, C. M. Ok, and B. K. Chae, “Using twitter data for
cruise tourism marketing and research,” Journal of Travel & Tourism
Marketing, vol. 33, no. 6, pp. 885–898, 2016.
[17] S. A. Golder and M. W. Macy, “Diurnal and seasonal mood vary withwork, sleep, and daylength across diverse cultures,” Science, vol. 333,
no. 6051, pp. 1878–1881, 2011.
[18] “Twitter,” https://twitter.com, (Accessed on 11/24/2020).
[19] R. Etzi, C. Spence, and A. Gallace, “Textures that we like to touch:An experimental study of aesthetic preferences for tactile stimuli,”
Consciousness and Cognition, vol. 29, pp. 178–188, 2014.
[20] B. Grohmann, E. R. Spangenberg, and D. E. Sprott, “The inﬂuence oftactile input on the evaluation of retail product offerings,” Journal of
Retailing, vol. 83, no. 2, pp. 237–245, 2007.
[21] D. B. McCabe and S. M. Nowlis, “The effect of examining actual
products or product descriptions on consumer preference,” Journal ofConsumer Psychology, vol. 13, no. 4, pp. 431–439, 2003.
[22] A. Krishna and M. Morrin, “Does touch affect taste? the perceptual
transfer of product container haptic cues,” Journal of Consumer Research, vol. 34, no. 6, pp. 807–818, 2008.
[23] J. Peck and S. B. Shu, “The effect of mere touch on perceived
ownership,” Journal of Consumer Research, vol. 36, no. 3, pp. 434–
447, 2009.
[24] J. Peck and T. L. Childers, “Individual differences in haptic information
processing: The need for touch scale,” Journal of Consumer Research,
vol. 30, no. 3, pp. 430–442, 2003.
[25] A. V. Citrin, D. E. Stem Jr, E. R. Spangenberg, and M. J. Clark,“Consumer need for tactile input: An internet retailing challenge,”
Journal of Business Research, vol. 56, no. 11, pp. 915–922, 2003.
[26] A. Mittal and A. Goel, “Stock prediction using twitter
sentiment
analysis,”Standford
University,
CS229
(2011
http://cs229.stanford.edu/proj2011/GoelMittalStockMarketPredictionUsingTwitterSentimentAnalysis.pdf), vol. 15,
2012.
[27] R. Daniulaityte, R. W. Nahhas, S. Wijeratne, R. G. Carlson, F. R. Lamy,
S. S. Martins, E. W. Boyer, G. A. Smith, and A. Sheth, “ time for dabs :Analyzing twitter data on marijuana concentrates across the us,” Drug
and Alcohol Dependence, vol. 155, pp. 307–311, 2015.
[28] S. Stemler, “An overview of content analysis,” Practical Assessment,
Research, and Evaluation, vol. 7, no. 1, p. 17, 2000.
[29] M. Vellei, G. Chinazzo, K.-M. Zitting, and J. Hubbard, “Human thermal
perception and time of day: A review,” Temperature, vol. 8, no. 4, pp.
320–341, 2021.
[30] M. Millodot, “Diurnal variation of corneal sensitivity.” The British
Journal of Ophthalmology, vol. 56, no. 11, p. 844, 1972.

9[31] M. Odrcich, J. M. Bailey, C. M. Cahill, and I. Gilron, “Chronobiologicalcharacteristics of painful diabetic neuropathy and postherpetic neuralgia:
diurnal pain variation and effects of analgesic therapy,” Pain, vol. 120,
no. 1-2, pp. 207–212, 2006.
[32] O. A. Heggli, J. Stupacher, and P. Vuust, “Diurnal ﬂuctuations in musicalpreference,” Royal Society open science, vol. 8, no. 11, p. 210885, 2021.
[33] Y. Ujitoko, T. Yokosaka, Y. Ban, and H.-N. Ho, “Tracking changes
in touch desire and touch avoidance before and after the covid-19
outbreak,” Frontiers in Psychology, p. 7649, 2021.
[34] A. Tolmachev, D. Kawahara, and S. Kurohashi, “Juman++: A morphological analysis toolkit for scriptio continua,” In Proceedings of the 2018
Conference on Empirical Methods in Natural Language Processing:
System Demonstrations, pp. 54–59, 2018.
[35] D. Kawahara and S. Kurohashi, “A fully-lexicalized probabilistic model
for japanese syntactic and case structure analysis,” In Proceedings ofthe Human Language Technology Conference of the NAACL, Main
Conference, pp. 176–183, 2006.
[36] “chive: Japanese word embedding with sudachi & nwjc,”
https://github.com/WorksApplications/chiVe, (Accessed on 11/24/2020).
[37] B. Efron and R. J. Tibshirani, An introduction to the bootstrap. CRC
press, 1994.
[38] F. A. Scheer, C. J. Morris, and S. A. Shea, “The internal circadian clockincreases hunger and appetite in the evening independent of food intake
and other behaviors,” Obesity, vol. 21, no. 3, pp. 421–423, 2013.
[39] P. Duarte and S. C. e Silva, “Need-for-touch and online
purchase propensity: A comparative study of portugueseand chinese consumers,” Journal of Retailing and Consumer
Services, vol. 55, p. 102122, 2020. [Online]. Available:
https://www.sciencedirect.com/science/article/pii/S0969698919314389[40] R. H. Finnegan, Communicating: The multiple modes of human interconnection. Psychology Press, 2002.
[41] E. McDaniel and P. A. Andersen, “International patterns of interpersonal
tactile communication: A ﬁeld study,” Journal of Nonverbal Behavior,
vol. 22, no. 1, pp. 59–75, 1998.
[42] J. Peck and T. L. Childers, “To have and to hold: The inﬂuence of haptic
information on product judgments,” Journal of Marketing, vol. 67, no. 2,
pp. 35–48, 2003.
[43] “Vrchat,” https://hello.vrchat.com/, (Accessed on 03/29/2023).
[44] S. Gatter, V. Hüttl-Maack, and P. A. Rauschnabel, “Can augmented
reality satisfy consumers’ need for touch?” Psychology & Marketing,vol. 39, no. 3, pp. 508–523, 2022.
[45] W. R. Sherman and A. B. Craig, Understanding virtual reality: Interface,application, and design. Morgan Kaufmann, 2018.
[46] Y. Ujitoko and Y. Ban, “Toward designing haptic displays for desired
touch targets: A study of user expectation for haptic properties via
crowdsourcing,” IEEE Transactions on Haptics, 2023.
[47] D. Wang, K. Ohnishi, and W. Xu, “Multimodal haptic display for virtual
reality: A survey,” IEEE Transactions on Industrial Electronics, vol. 67,
no. 1, pp. 610–623, 2019.

Yusuke Ujitoko has been a researcher at NTT
Communication Science Laboratories since 2020.
He was a member of the R&D Group in Hitachi,Ltd. from 2016 to 2020. He received his PhD fromthe University of Electro-Communications, Japan,
in 2020. He received his BE degree in mechanicalengineering and MAE degree in interdisciplinary
information studies from the University of Tokyo,
Japan, in 2014 and 2016, respectively. His researchinterests include applied haptic perception and interfaces.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

This article has been accepted for publication in IEEE Transactions on Visualization and Computer Graphics. This is the author's version which has not been fully edited andcontent may change prior to final publication. Citation information: DOI 10.1109/TVCG.2024.3355413

JOURNAL OF LATEX CLASS FILES, VOL. XX, NO. X, AUGUST 2022

10

Yuki Ban received his MS and PhD degrees in information science and technology from the Universityof Tokyo, Japan, in 2013 and 2016, respectively. He
was a researcher at Xcoo Inc. research from 2016
to 2017. He is currently a project lecturer at the
Department of Frontier Sciences at the University ofTokyo. His current research interests include crossmodal interfaces and biological measurement. He isa member of the IEEE.

Takumi Yokosaka received a PhD degree in information processing from the Tokyo Institute of Technology, in 2018. He is employed at NTT Communication Science Laboratories, Japan. His researchinterests include perception and cognition shaped bybody movement.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/