This article has been accepted for publication in IEEE Transactions on Haptics. This is the author's version which has not been fully edited andcontent may change prior to final publication. Citation information: DOI 10.1109/TOH.2022.3213313

JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015

1

Vibrotactile Spatiotemporal Pattern Recognition in
Two-dimensional Space Around Hand
Yusuke Ujitoko, Ryunosuke Tokuhisa, and Koichi Hirota

Abstract—This study investigated vibrotactile spatiotemporal
pattern recognition in the two-dimensional space around ahand. The participants placed their hands on the medium,and identiﬁed the recognized pattern presented in the medium.
There were 64 rotational patterns, presented with sequential
impulse vibrations. We investigated how well humans recognized
the patterns presented around their hand, and identiﬁed thepattern factors (e.g., rotational direction) that affected recognition
accuracy. The probability of obtaining correct answers was
48.9 %. It was observed that the start and end points, rotational
direction, and the number of vibrations affected recognition
accuracy. It was also found that patterns starting or ending on
the ulnar side (0◦ ) of the hand were difﬁcult to recognize, whereas
those starting or ending on the distal (90◦ ) or proximal side (270◦ )of the hand were easily recognizable. Furthermore, we found a
type of the oblique effect. Patterns starting or ending in theoblique direction were more difﬁcult to recognize than those in
the cardinal direction. We also found that the clockwise rotational
pattern was slightly easier to recognize than the counterclockwise
rotational pattern. Finally, the underestimation of the judgmentof tactile numerosity explains how the number of vibrations in
the patterns affected the recognition accuracy. This result canbe used as a baseline when the recognition of spatiotemporal
patterns outside the body under other conditions is examined infuture studies.
Index Terms—Haptics, Vibrotactile, Spatiotemporal pattern.

I. I NTRODUCTION
HERE are cases where presenting information via tactile
sense is helpful. One of the assumed cases is a casewhere visual and audio senses are unavailable. For example,
informing people in a car of the direction and distance ofobstacles around the car with tactile senses would be desirable.
This is because the tactile sense is often more available thanthe visual and auditory senses while driving [1]. This method’s
applicability is not limited to cars but would also be effective
in cases where other audio-visual channels are overloaded andcases involving people with audio-visual disabilities. Anotherassumed case would be one where it is beneﬁcial to combine
tactile sense with audio or visual senses. For example, tactile
senses are important to enhance realism in entertainment
applications. Let us assume that an AR (augmented reality)
character, visualized by stereoscopic technology, is walking
around the user. By feeling the vibration while the characteris walking, realism would be enhanced. In this study, we focused on vibrotactile stimulation, which can be inexpensively
implemented among various types of tactile stimuli [2].
Users can recognize spatiotemporal patterns using vibrotactile stimuli. It is known that a sequence of vibrations at

T

Manuscript received April XX, 20XX; revised August XX, 20XX.

vibration
sourcespatiotemporal
pattern
two-dimensional
space

Fig. 1. Vibrotactile spatiotemporal pattern recognition.

different locations at appropriate intervals can induce vibrotactile sensations that continuously move [3], [4]. The moving
sensation using a 2D vibrotactile actuator array on the skincan present the shape of spatiotemporal patterns [5]. Suchspatiotemporal patterns can provide users with symbolic information by allowing them to discriminate between patterns. In
addition, spatiotemporal patterns can provide spatiotemporal
information (e.g., the moving direction [6]).
To present vibrotactile spatiotemporal patterns, previous
studies laid out an array of vibrotactile actuators on the bodysurface and caused vibrations sequentially. They have investigated how humans discriminate patterns presented at various
locations in the body [7], [8], [5]. Although this approachis straightforward, some concerns exist due to the contact
between the vibrotactile array and the body surface. The ﬁrst
concern is about the method of laying out the vibrotactile arrayin a limited space. If we want to present complex patterns,
we need to arrange the vibrotactile array in considerablyhigh density, but it is expensive to miniaturize and integrate
the array in limited spaces. Another concern is about heatgeneration. The heat generated by the presentation system,
including the vibrotactile array, can make it uncomfortable touse and may disturb the tactile sensation. These concerns areunavoidable when a vibrotactile array is in contact with thebody.
In our previous study, we addressed these concerns byinvestigating how well humans localized a remote vibration
source in two-dimensional space via propagated vibration
cues coming through a medium [9], [10]. Speciﬁcally, theparticipants placed their hands on a silicone rubber sheet andattempted to localize the vibration source around their hands.
The localization accuracy was better with impact vibrations
than sinusoidal vibrations. Although the direction of the vi-

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

This article has been accepted for publication in IEEE Transactions on Haptics. This is the author's version which has not been fully edited andcontent may change prior to final publication. Citation information: DOI 10.1109/TOH.2022.3213313

JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015

bration source could be considerably recognized, the distancecould not be accurately recognized regardless of the vibration
waveform. We note that this method alleviates the concerns
about laying out the vibrotactile actuators in a limited space
and the concerns about heat generation since it does not require
the vibrotactile array to contact the body.
Using the same experimental system used in our previous
study [9], the present study investigated the human recognition
ability toward vibrotactile spatiotemporal patterns in the twodimensional space around the hand (see Fig. 1). Speciﬁcally,
we used eight vibrotactile actuators around the hand and hadparticipants recognize 64 patterns (see Figs. 2(a) and (b)).
Because we previously found that humans can recognize thedirection of the remote vibration source [9], we deﬁned thepatterns to investigate as rotational ones around the hand.
Such patterns can be recognized with directional recognitionof the vibration source. As a result of the experiment, we could
characterize a human’s ability to recognize rotational patterns
in terms of various factors of patterns comprehensively (e.g.,
the rotational direction or starting point of patterns). The
results of the present study provide basic information on thehuman ability to recognize spatiotemporal patterns aroundthe hand and insights into the development of spatiotemporal
pattern presentation displays.

2

distal side(a)
500 mm135° 90°

45°
0°

180°

radialside

130 mm
ulnar
side

315°

225°

270°
contactor

silicone rubber sheet

proximal side

(b)

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

II. R ELATED WORKAs related work, we introduce previous studies that used
remotely distributed vibration sources from the human bodyto investigate the human localization ability. Subsequently, we
introduce studies on the recognition of vibrotactile spatiotemporal patterns presented on the surface of the body.
A. Vibrotactile Localization outside Body Surface
Some studies have investigated localization ability whenusers are holding the edge of a one-dimensional medium (e.g.,a stick), which is stimulated at a certain distance from the point
it is held. Miller et al. had participants hold a one-dimensional
medium (such as a stick) in their hand and actively shake it
to determine whether they could estimate the point of contact
when striking an object [11], [12]. Sreng et al. had participants
hold one end of a stick in each hand and use several vibration
patterns to estimate the location of the vibration source [13].
In contrast to these studies, which used a one-dimensional
medium, we used a two-dimensional medium. This is because
more patterns can be represented in a two-dimensional spacethan in a one-dimensional space.
The study by [14] proposed a system to present users witha vibration source in a two-dimensional space, but it did
not clarify how well users could localize the source because
they did not conduct an experiment. In similar settings, ourprevious study examined the localization ability of a handplaced on a two-dimensional medium when an impact wasmade somewhere on the medium around the hand [9]. We
found that the participants could not recognize the distancebut recognized direction to some extent. We found variability
in the absolute error of direction recognition depending on thestimulus direction. We also found a directional bias, which

Fig. 2. (a) Vibration contactor layout. The orange points indicate the positionsof the vibration contactor. The distance of the vibration contactor from thecenter was 130 mm. (b) In this study, 64 spatiotemporal patterns were used.
Patterns were deﬁned by a combination of rotational direction, starting point,
and end point.

indicated a signiﬁcant difference in perceived direction andstimulus direction depending on the stimulus direction. Since
recognizing direction is possible to some extent, the present
study used eight different vibrotactile stimuli directions thatwere used in our previous study [9] to investigate vibrotactile
spatiotemporal pattern recognition.
B. Recognition of Vibrotactile Spatiotemporal Pattern
Two-dimensional tactile arrays on body surfaces have beenused to provide users with motion or directional informationby presenting spatiotemporal patterns. An intuitive way topresent spatiotemporal patterns is to use dense tactile vibrotactile actuators [15] and activate the actuators arounda target trajectory sequentially. However, this approach iscostly. Relatively sparse arrays of vibrotactile actuators wereeffective in providing spatiotemporal patterns [3], [4]. Thusfar, studies have investigated the recognition of vibrotactile

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

This article has been accepted for publication in IEEE Transactions on Haptics. This is the author's version which has not been fully edited andcontent may change prior to final publication. Citation information: DOI 10.1109/TOH.2022.3213313

JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015

spatiotemporal patterns on various body sites, including thepalm [16], [17], [18], [19], [20], [21], [22], [23], wrist [24],[25], [7], [26], [27], arm [28], [29], back [30], [31], [5], [32],[33], nail [8], head [34], forearm [35], [31], and waist [36],[37], [38]. Among these body sites, we chose the hand topresent spatiotemporal patterns because the human hand hasa relatively high sensitivity to vibration among all body sites.
Another reason is that the hand has been the most popularpresentation area in ordinary tactile devices.
Previous studies have reported that speciﬁc factors of rotational patterns affect the accuracy of pattern recognition.
Regarding the rotational directions (clockwise (CW) or counterclockwise (CCW) directions) of stimuli, some studies reported a signiﬁcant effect of the rotational directions of stimuli
on recognition accuracy, while others did not. Some also
reported that the accuracy was better in the case of CW thanin the case of CCW patterns [17]. In their experiment, eightout of 10 participants were right-handed, and it was unclear
whether the interaction effect between the dominant hand androtational direction was signiﬁcant. Conversely, other studies
reported that rotational directions did not affect the accuracy ofrecognition [23], [16], [25], [39]. It should be noted that while
there was no signiﬁcant difference in accuracy depending on
the stimuli’s rotational direction in the experiment [25], someparticipants reported a difference in constructing mental models between CW and CCW patterns. We investigated the effectof rotational direction on the accuracy of our experimental
setup.
Regarding the starting point of rotational patterns, according
to the results of Seo et al. [23]’s experiment, it is known thatthe starting point signiﬁcantly affects recognition accuracy. In
their experiment, the starting point on the ulnar side of thehand was signiﬁcantly better than that on the distal, proximal,
and radial sides. In contrast to the study by Seo et al. [23],in our previous study [9], the absolute directional error ofvibrotactile localization was worse on the ulnar side andbetter on the distal one. The characteristics of the absolute
directional error of vibrotactile localization would be related
to spatiotemporal pattern recognition in terms of the starting
point of rotational patterns in the present study. It would beinteresting to see how these contrasting results in previous
studies would relate to the present study’s results. In addition,
we assumed that the end point would affect the accuracy,similar to the starting point.
Furthermore, we considered that the effect of the start andend points can be discussed within the context of the “obliqueeffect.” Although this term has been used in many different
experimental settings, the basic idea is that the performancewith oblique stimuli is worse than that with stimuli oriented in
the cardinal directions (i.e., horizontal and vertical). For example, Appelle and Gravetter [40] conﬁrmed the oblique effect,in which participants were less accurate when discriminatinglines oriented obliquely than those oriented either vertically or
horizontally. Recently, Kappers et al. [41] observed an obliqueeffect in vibratory direction recognition on the back. Theirresults showed that both the bias and variance of direction
recognition were worse in the oblique direction than in the
cardinal direction. It was hypothesized that the recognition of3

spatiotemporal patterns characterized by the cardinal direction(starting and ending in the cardinal direction) would be moreaccurate than those characterized by the oblique direction.
In addition, the number of vibrations in the stimuli also
affects the accuracy in recognizing spatiotemporal patterns.
Humans often underestimate the number of vibrations inspatiotemporal vibrational patterns [42]. If humans recognize
such patterns using the numerosity cue, the recognition accuracy would be worse when the number of vibrations inthe stimuli is large. Indeed, it has been reported that the
number of vibrations directly affects the recognition accuracyof spatiotemporal patterns [43], although studies provided
vibrotactile rotational patterns on the hand directly.
All previous studies have investigated the recognition of
spatiotemporal patterns that directly present vibration on bodysurfaces. Yet, no study has explicitly investigated spatiotemporal pattern recognition stimulated by vibrotactile actuators
outside the body, which the present study investigates. We
were interested in how factors such as the starting point, end
point, rotational direction, and number of vibrations would
affect the recognition accuracy of spatiotemporal patterns in a
two-dimensional space around the hand.
III. M ETHOD
A. Participants
Fourteen participants (13 males and one female, all righthanded, with a mean age of 22.4 (SD: 1.1) years) took part in
the study. The average height and width of the participants’hands were 18.14 (SD: 0.55) cm and 17.26 (SD: 1.12) cm,
respectively. All participants were naive to the purpose of thestudy. Ethical approval for this study was obtained from the
ethics committee of the University of Electro-communications(approval number: 21052).
B. Apparatus
The apparatus used in this experiment was the same as thatused in our previous study [9]. Please refer that study fordetails. Fig. 3 shows the appearance of this apparatus.
Participants were seated comfortably on a chair and instructed to close their eyes during the task. They wore noisecanceling headphones that played white noise to mufﬂe theexternal sounds. The right arm was placed on the armrestand their hands were set in the designated area on a silicone
rubber sheet (see the designated hand position in Supplementary Figure 1). The size of the silicone rubber sheet was
500 mm×500 mm, and its thickness was 10 mm. The hardnessof the silicone rubber sheet was 30 (tested using a Shore
A durometer). This sheet was placed on soft urethane foam
(INOAC CORP, ECZ, 25 % ILD was 80) to diminish theinﬂuence of vibration propagation from the ground. The sizeof the soft urethane foam was 500 mm×500 mm, and its
thickness was 100 mm. Solenoids (CBS10290100, TAKAHAKIKOU Co.) were placed inside the urethane foam. The
diameter of the contactor at the top of the solenoid was 3
mm. The solenoids were actuated using a driver circuit (SB6565-01, TAKAHA KIKOU Co.).

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

This article has been accepted for publication in IEEE Transactions on Haptics. This is the author's version which has not been fully edited andcontent may change prior to final publication. Citation information: DOI 10.1109/TOH.2022.3213313

JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015

To characterize the vibration, we measured the displacementof the silicone rubber sheet indented by the solenoid contactor
by using a laser displacement sensor (ZX2-LD50, OMRONCo.). The maximum displacement was approximately 1 mm
and the duration was approximately 0.2 s at the point ofcontact with the solenoid contactor. See Fig. 7 in our previous
paper [9] for acceleration at 0 mm and 130 mm from thevibration source to determine the damping when the vibration
propagates. Additionally, see Fig. 8 in our previous paper [9]for the power spectral density of acceleration.

…

siliconerubbersheet
urethanefoamsolenoidbase

impulse generating device
arm rest
Fig. 3. Apparatus.

C. Stimuli
The key design factors for spatiotemporal patterns were
the shape of the patterns and the stimulus onset asynchrony(SOA).
1) Pattern Shape: As described in the Introduction, weused rotational patterns. Though the linear patterns such as
moving away from or approaching a hand were other candidate
patterns, humans cannot recognize the distance accurately [9]and thus, it would be difﬁcult for participants to recognize
such linear patterns. In contrast, it was expected that humans
can recognize the rotational patterns with the human ability torecognize direction better. Thus, we decided to use rotational
patterns in this experiment.
To clearly characterize the human recognition ability forspeciﬁc spatiotemporal patterns around the hand, we wanted
to increase the difﬁculty of this task. If the tasks were
too easy, it would be difﬁcult to observe the difference inrecognition accuracy due to the stimulus factor. We assumed
that the difﬁculty of the task depended on the number ofspatiotemporal patterns presented to the participants.
To deﬁne the appropriate set of spatiotemporal patterns,
we set up a preliminary experiment with 32 spatiotemporal
patterns made up of only four points (0◦ , 90◦ , 180◦ , and 270◦ )and 11 participants (see Supplementary Figure 2 for the patternshapes). The distance between the points from the center of the
sheet was 130 mm. In the preliminary experiment, participants
were presented with patterns and they identiﬁed 32 candidates.
Consequently, the recognition accuracy was found to be 84.1
% and the maximum information transfer (IT) was 4.24. Refer
to Supplementary Note 1 for details on the task and results. We
found that there was a signiﬁcant main effect of the starting4

point but not for other factors such as rotational direction or
end point of patterns.
In the main experiment, we presented participants with agreater number of patterns. Eight points were indented by thesolenoid’s contactors in eight directions (see orange points in
Fig. 2(a)). Fig. 2(b) shows the 64 spatiotemporal patterns thatwere used. The patterns were deﬁned by a combination ofrotational direction, starting point, and end point. In addition tothese three factors, the number of vibrations also characterized
the patterns. The rotational direction levels were CW and
CCW. The levels of starting and end points were 0◦ , 45◦ , 90◦ ,135◦ , 180◦ , 225◦ , 270◦ , and 315◦ . The numbers of vibrations
were three, ﬁve, seven, and nine.
2) SOA Optimization: The current experiment elucidates
the effect of spatial factors of patterns (e.g., starting point ofpatterns). As another factor, it should be noted that stimulus
onset asynchrony (SOA), or interval time between the onsets
of consecutive stimuli, might affect the recognition of patterns.
To clearly focus on the spatial factors, we optimized the SOA,
which is a temporal factor, prior to the main experiment. We
conducted another preliminary experiment to deﬁne the SOA
to improve the recognition of the vibrotactile patterns. In thispreliminary experiment, 10 participants adjusted the SOA foreach of 64 patterns. We asked participants to determine the
SOA, where the vibrotactile pattern was clearly recognizable.
The same SOA was assigned to all the intersolenoids in one
pattern. In one trial, the participants received the vibration on
their right hand and could change the SOA value by pressing
the keyboard with their left hand’s ﬁnger. Once the SOA value
was changed, corresponding vibrations would be presented.
The change of SOA and the presentation of vibration during
one trial could be repeated without limit. In total, there were64 trials for each participant.
We hypothesized that the optimized SOA depends on four
factors (rotational direction, starting point, end point, andnumber of vibrations). The mean values of optimized SOA
by each participant are shown in Supplementary Table 2. To
verify the hypothesis, we performed a generalized linear model(GLM) analysis. The GLM was ﬁtted to regress the optimized
SOA values using four factors. Since the optimized SOAs werepositive continuous values that were not expected to have anormal distribution, the GLM employed a logarithmic linkfunction with a gamma distribution. Based on the likelihood
ratio test (Type II test), a statistically signiﬁcant main effectof the number of vibrations was found [df = 3, χ2 = 10.787,
and p = 0.012] (see Fig. 4. The other three factors had no statistically signiﬁcant main effects (p > 0.05). For post-hoc testsof the signiﬁcant main effect of the number of vibrations, we
conducted multiple comparisons. The signiﬁcance level was
set at 0.05 with Bonferroni correction. There were statisticallysigniﬁcant differences between patterns 3 and 9 only in termsof the number of vibrations in the patterns. Based on the resultsof the preliminary experiment, we conﬁgured the SOA to eachspatiotemporal pattern according to the number of vibrations in
the pattern. In other words, the SOA was 125.4, 116.1, 109.2,
and 102.4 ms for the patterns with 3, 5, 7, and 9 vibrations,
respectively. Note that the SOA for each pattern was the samefor all participants.

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

This article has been accepted for publication in IEEE Transactions on Haptics. This is the author's version which has not been fully edited andcontent may change prior to final publication. Citation information: DOI 10.1109/TOH.2022.3213313

JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 20155*

* :p<0.05
Fig. 4. Optimized SOA with the number of vibrations in preliminary
experiments. Error bars denote 95 % conﬁdence interval (CI).

D. TaskAt the beginning of the main experiment, the participants
were presented with written instructions that described thesituation and tasks of the experiment. After reading it, theparticipants moved on to the experiment.
In one trial, one of the 64 patterns was presented toparticipants. The participants answered questions regarding
the starting point, end point, and rotational direction. Thus,
they could answer one of 128 parameter combinations in
total (= 8 starting points × 8 end points × 2 rotational
directions). Because we wanted to investigate the absolute
recognition accuracy of each spatiotemporal pattern, we did
not make participants select the patterns among the 64 pattern
candidates. If we made participants discriminate the patterns,
the result of discrimination would be inﬂuenced by the set ofpatterns, which should be avoided.
Each block consisted of 64 trials. The order of presentationof the patterns in each block was randomly assigned. There
were four blocks per participant, resulting in 256 trials conducted per participant. To reduce their workload, the blocks
were assigned to two consecutive days per participant. Twoblocks were assigned to the ﬁrst day and the other two blocks
were assigned to the second day. There was a 10-min break
between blocks in a day.
IV. R ESULTS AND D ISCUSSIONS
Fig. 5 shows the recognition accuracy for each pattern. The
average accuracy was 48.9 %. In our experiment, we let theparticipants choose the parameters (starting points, end points,
and rotational directions) as answers. The number of combined
parameters was 128. Among these combinations, there was
one correct answer that was the same as the pattern ID that
had been presented to the participant tactilely. The probability
that the correct answer will be chosen by chance is 1/128= 0.78 %. As compared to this value, the average accuracy
obtained in our study was substantially higher.
First, we averaged the recognition accuracy per participant
and checked whether this accuracy correlated with the participant’s hand size. Refer to Supplementary Figure 2 forthe relationship between them. We calculated Spearman’srank correlation coefﬁcient between accuracy and hand width,height, or size. The hand size is the average of the hand widthand height. The correlation coefﬁcient between recognition
accuracy and hand width was -0.01 (p = 0.97), that between
recognition accuracy and hand height was -0.21 (p = 0.46),
and that between recognition accuracy and hand size was-0.13 (p = 0.64). These results suggest that there is no
signiﬁcant correlation between recognition accuracy and handsize. Thus, we performed the analysis without considering theparticipants’ hand sizes.
We performed a GLM analysis to investigate how the four
factors of stimuli (rotational direction, starting point, end point,and number of vibrations) affected recognition accuracy. We
ﬁtted the GLM to regress the number of correct answers
using these four factors. The GLM employs a logarithmic
link function with a Poisson distribution. As a result of thelikelihood ratio test (Type II test), there were signiﬁcant main
effects on the starting point [df = 7, χ2 = 25.32, p < 0.001],
the end point [df = 6, χ2 = 37.13, p < 0.001], rotational
direction [df = 1, χ2 = 5.16, p = 0.023], and the numberof vibrations [df = 3, χ2 = 66.69, p < 0.001]. There wasalso signiﬁcant interaction effect between the starting and end
points (df = 16, χ2 = 30.18, p = 0.017). The other interaction
effects were not signiﬁcant (p > 0.05).
Next, we describe the results of the post hoc tests on thesesigniﬁcant main effects and interactions.
A. Effect of Starting Point
Fig. 6(a) shows the recognition accuracy due to the starting
point of stimuli. As post-hoc tests of the signiﬁcant main
effect of the starting point, we conducted multiple comparisons
between starting points. The signiﬁcance level was set at 0.05
with Bonferroni correction. There were signiﬁcant differences
between pairs of 0◦ -90◦ , 0◦ -270◦ , 45◦ -90◦ , 90◦ -135◦ , 90◦ 225◦ , and 90◦ -315◦ . This shows that the accuracy was better
when the starting points were 90◦ and 270◦ and the accuracy
was worse when the starting points were 0◦ , 45◦ , 135◦ , 225◦ ,and 315◦ .
We consider that variation of accuracy due to the starting
points would be caused by the error in the direction recognitionof the vibration source at the starting points. In our previous
study [9], we obtained the absolute errors of the direction
recognition. The absolute error was the largest (26.6◦ ) when
the stimulus direction was 0◦ , and smallest (6.7◦ ) when thestimulus direction was 90◦ . We used these values to calculate the correlation between the recognition accuracy of thespatiotemporal patterns and the absolute error of the direction
recognition. The Spearman’s rank correlation coefﬁcient was-0.86 (p = 0.007), which showed a statistically signiﬁcantcorrelation between them (see the relationship between themin Fig. 6(b)). This indicates that the variation in the recognition
accuracy of spatiotemporal patterns due to starting points canbe explained by the ability of humans to recognize directionof the vibration source at the starting points. Thus, we expectthat if we can improve the human direction recognition ability
by some methods (e.g., training), the recognition accuracy of
spatiotemporal patterns will also be improved successfully.

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

This article has been accepted for publication in IEEE Transactions on Haptics. This is the author's version which has not been fully edited andcontent may change prior to final publication. Citation information: DOI 10.1109/TOH.2022.3213313

JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015

6

Fig. 5. Recognition accuracy for each pattern.

(a)
***
***

*

*

***

***

* :p<0.05** :p<0.01***:p<0.001(b)correlation coefficient ρ: -0.86 (p=0.007)

side was short and the edge of the distal and proximal side
was long. Since the vibration sources were spatially dense on
the short side, participants in Seo et al.’s work might be able
to clearly recognize the vibrotactile ﬂow on the short side.
It can explain the better accuracy for patterns starting at theulnar side than that starting at the distal and proximal side. The
reason why the ulnar side was better than the radial side in
Seo et al.’s work might be attributed to the adhesion between
skin and device. While the participant’s hand naturally adhere
to the silicone rubber sheet in our experiment, Seo et al.’s
participant’s hand did not do so because they gripped thedevice in hand. It seems that the ulnar was in close contact
with the device, and the radial side had a part where theobject ﬂoated from the skin. Therefore, a small area of theskin received the propagated vibration on the radial side, andthis may lead to low recognition accuracy for the patterns
starting at the radial side.
B. Effect of End Point

Direction ofstart point of stimuli

Fig. 6. (a) Recognition accuracy due to starting point of stimuli. The error
bars denote the 95 % CI. (b) Relationship between absolute directional errorof vibration localization and spatiotemporal pattern recognition accuracy. The
data for the absolute directional error of vibration localization are quoted fromour previous study [9].

Our results regarding the starting points are not consistent
with Seo et al.’s experiment [23], in which the recognition
performance was better when stimuli started at the ulnar sidethan when stimuli started at the distal, proximal, and radialsides. In contrast, the recognition performance when stimuli
started at the ulnar side was difﬁcult in our experiments. We
can attribute the inconsistency of the recognition accuracy
to the device shape and degree of adhesion between skinand device. While our device is vertically and horizontallysymmetric, Seo et al.’s device was asymmetric (6 cm × 11 cm
× 1 cm). In Seo et al.’s device, the edge of the ulnar and radial

Fig. 7(a) shows the recognition accuracy due to the end
point of stimuli. As post-hoc tests of the signiﬁcant main effectof the end point, we conducted multiple comparisons between
end points. There were signiﬁcant differences between pairs of0◦ -90◦ , 0◦ -270◦ , 90◦ -225◦ , 90◦ -315◦ , 225◦ -270◦ , and 270◦ 315◦ . This shows that the accuracy was better when the end
points were 90◦ and 270◦ , and the accuracy was worse whenthe end points were 0◦ , 225◦ , and 315◦ .
We considered that the variation in recognition accuracy
due to the end point was caused by the error in direction
recognition of the vibration source at the end points. We
calculated the correlation between the recognition accuracyof the spatiotemporal patterns and the absolute error of
direction recognition which was obtained in our previous
study [9]. The Spearman’s rank correlation coefﬁcient was−0.81 (p = 0.015), which shows a signiﬁcant correlation
between them (see the relationship between them in Fig. 7(b)).
This indicates that the variation in recognition accuracy of
spatiotemporal patterns due to the end points can be explainedby the human direction recognition ability of the vibration
source at these points. We expect that if we can improve
human direction recognition ability by some methods (e.g.,training), the recognition accuracy of spatiotemporal patterns
will also be improved.

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

This article has been accepted for publication in IEEE Transactions on Haptics. This is the author's version which has not been fully edited andcontent may change prior to final publication. Citation information: DOI 10.1109/TOH.2022.3213313

JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015

7

(a)

(a)
***
***

***

**

***

**

**
**

**
*

**
*

**
*

Direction ofend point of stimuli* :p<0.05** :p<0.01:p<0.001
***

(b)

(b)

correlation coefficient ρ: -0.81 (p=0.015)
****

* **

*

*** **

Direction ofstart point of stimuli

Direction ofend point of stimuli* :p<0.05** :p<0.01***:p<0.001

Fig. 7. (a), Recognition accuracy due to end point of stimuli. The error bars
denote the 95 % CI. (b) Relationship between absolute directional error invibration localization and the recognition accuracy of spatiotemporal patterns.
The data for the absolute directional error of vibration localization are quoted
from our previous study [9].

Fig. 8. (a), Recognition accuracy due to end point of stimuli at each starting
point of stimuli. (b) Recognition accuracy due to end point of stimuli at eachstarting point of the stimuli. Error bars denote 95 % CI.

C. Effect of Interaction between Starting and End Points
As post-hoc tests of the signiﬁcant interaction effect between the starting and end points, we conducted multiple
comparisons between the end points at each starting point. The
results are presented in Fig. 8(a). We also conducted multiple
comparisons between the starting points at each end point. The
results are presented in Fig. 8(b).
We found that whether the starting or end point of thestimuli was 0◦ affected the recognition accuracy. For instance,
among patterns whose end points were at 180◦ , there weresigniﬁcant differences between stimuli starting at 0◦ andstimuli starting in the other three directions (90◦ , 180◦ , and270◦ ). This suggests that stimuli starting or ending at 0◦ were
difﬁcult to recognize.
We also found that whether the starting or end point of
the stimuli was 90◦ affected the recognition accuracy. Even
when the stimuli started or ended at 90◦ , it was not difﬁcult torecognize the stimuli that started or ended at 0◦ . This suggeststhat the stimuli that started or ended at 90◦ were easy torecognize.
D. Effect of Rotational Direction
Fig. 9 shows the recognition accuracy due to rotational
direction of stimuli. The main effect of rotational direction was
found to be signiﬁcant, which suggests that the accuracy wasiFig. 9. Recognition accuracy due to rotational direction of stimuli. Error bars
denote 95 % CI.

signiﬁcantly higher in the case of CW than in the case of CCW.
This is consistent with a previous study [17] that indicatedthe recognition of CW stimuli was signiﬁcantly better than
the recognition of CCW stimuli. In contrast, our result is
not consistent with previous studies reporting that rotational
directions did not affect the accuracy of recognition [23], [16],[25], [39].
Factors causing rotational direction, which could be both
physical and perceptual, are yet to be identiﬁed. To discussthese factors, let us consider the pattern ID 1 and 9 shownin Fig. 2. As physical factors, in the case of both of thesepatterns, the same positions 315◦ , 0◦ , 45◦ vibrated, and theThis work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/

This article has been accepted for publication in IEEE Transactions on Haptics. This is the author's version which has not been fully edited andcontent may change prior to final publication. Citation information: DOI 10.1109/TOH.2022.3213313

JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015

SOAs between them are the same, but the direction of rotation
is different. The direction of rotation might have a signiﬁcanteffect on how the vibration propagates over the skin in space
and time. As a perceptual factor, there is a possibility that
it is easier for humans to perceive CW and CCW rotation.
Our results could be related to some participants’ reports inthe previous work by [25] that there was a difference in constructing mental models between CW and CCW stimuli. Still,
it is unclear from our experimental results which perceptual
factors made a difference.
In our experiment, all participants were right-handed. It
is also unclear whether the interaction effect between thedominant hand and rotational direction was signiﬁcant. Investigating the interaction effect is a topic for future research.

85-7. This analysis conﬁrmed that the difference in recognition
accuracy between the three vibrations and the other numberof vibrations was partly due to the underestimation of thenumerosity of vibrations.
(a)

(b)

***

***

***

**

**
:p<0.05* :p<0.01*****:p<0.001

E. Effect of Number of Vibrations

***

***

Fig. 11. (a), Relationship between the number of vibrations of stimuli andthat of answer. (b) Number of vibrations of stimuli minus that of the answer.
Error bars denote 95 % CI.

***

***

***
* :p<0.05** :p<0.01***:p<0.001

Fig. 10. Recognition accuracy due to number of vibrations of stimuli. Error
bars denote 95 % CI.

Fig. 10 shows the recognition accuracy due to the numberof vibrations of stimuli. We conducted multiple comparisonsas post-hoc tests of the signiﬁcant main effect of the numberof vibrations. The results are shown in Fig. 10. There weresigniﬁcant differences in all pairs (p < 0.05) except the pairof 5-7 (p > 0.05).
The reason for the high accuracy when there were threevibrations could be related to the characteristics of the tactile
numerosity judgment. Humans often underestimate the number of vibrations in spatiotemporal vibrational patterns [42].
Fig. 11(a) shows the relationship between the number ofvibrations of the stimuli and the answer. Fig. 11(b) shows
the difference between them, that is, the number of vibrationsof the stimuli minus that of the answer. In the following
analysis, we conﬁrmed that the differences between them weredependent on the number of vibrations in the patterns. Since
we could not identify the distribution suitable for both positive
and negative count data for the GLM, we transformed the
distribution using an aligned rank transform (ART) [44] andapplied ANOVA. As a result of the ANOVA, there was asigniﬁcant main effect of the number of vibrations in thestimuli [df = 3, F = 103, p < 0.001]. Multiple comparisonswith Bonferroni corrections were performed as a post-hoc
test. Fig. 11(b) presents the comparison results. It shows asigniﬁcant difference between all pairs, except for the pair of

It should be noted that the underestimation was not strong
when the number of vibrations was nine, rather than ﬁve orseven. We consider that when the participants assumed that the
number of vibrations was larger than nine, they provided theanswer as nine. This is because the participants only answered
about the starting point, end point, and rotational direction, andit was assumed that the maximum number of vibrations wasnine. Another possibility is that when the number of vibrations
is nine, the start and end points are the same. If the participant
noticed this similarity, it would be easy to identify the points
because the stimuli were presented twice.
Since the SOA was conﬁgured differently for each numberof vibrations as shown in Fig. 4, there is a possibility that participants judged the number of vibrations by the difference in
the SOA. If this is correct, time and number interference [45]might have affected the accuracy. However, it is not clear
from the results of this study whether or how the interference
affected the result.
F. Oblique Effectexamples of cardinal condition

2

34

examples of oblique condition

1

33

Fig. 12. Examples of cardinal and oblique conditions.

We investigated whether the oblique effect was signiﬁcant.
Cardinal directions are 0◦ , 90◦ , 180◦ , and 270◦ and oblique
directions are 45◦ , 135◦ , 225◦ , and 315◦ . Since the number ofvibrations was only odd in our patterns, when the start point
was cardinal/oblique, the end point was also cardinal/oblique.
Hereafter, we refer to the condition where the start or end
points are cardinal (0◦ , 90◦ , 180◦ , and 270◦ ) as the cardinal
condition. We refer to the condition where the start or end
points are oblique (45◦ , 135◦ , 225◦ , and 315◦ ) as the obliqueThis work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/

This article has been accepted for publication in IEEE Transactions on Haptics. This is the author's version which has not been fully edited andcontent may change prior to final publication. Citation information: DOI 10.1109/TOH.2022.3213313

JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015

9cardinal (oblique) directions at both the start and end points
because the number of vibrations was conﬁgured to be three,ﬁve, seven, or nine. This double-direction recognition might
have enhanced the oblique effect, while there might be no
signiﬁcant oblique effect for single-direction recognition.
V. G ENERAL D ISCUSSION

Fig. 13. Recognition accuracy due to cardinal and oblique direction. Error
bars denote 95 % CI.

condition. Fig. 12 shows the examples of the cardinal andoblique conditions.
Fig. 13 shows the recognition accuracy for the two conditions. We investigated whether the recognition accuracy
differed between the cardinal and oblique conditions. Here,
we used non-parametric bootstrap because it does not require a
speciﬁc distribution (e.g., normal distribution). The procedureof non-parametric bootstrap is to resample from the original
dataset and calculate the statistical value of interest. Specifically, we calculated 10,000 bootstrap samples [46] of thedifferences in accuracy between the cardinal and oblique conditions. We sampled with replacement from all participants’
answers and calculated the difference in recognition accuracy
between the cardinal and oblique conditions. This calculation
was made 10,000 times to obtain 10,000 differences. If the
Bonferroni-corrected 95 % CI of the difference did not overlap
with zero, we could conclude that the difference was statistically signiﬁcant. The calculated 95 % CI was 0.029-0.073 andthe difference was shown to be signiﬁcant. Based on this, wefound an oblique effect in spatiotemporal pattern recognition
in a two-dimensional space around the hand.
We found that spatiotemporal patterns starting and ending in
the oblique direction are more difﬁcult than those starting andending in the cardinal direction. This result is consistent withprevious studies indicating that the performance of localization
is better in cardinal conditions than in oblique conditions [41].
According to our results shown in Sections IV-A and IV-B,
in our experimental settings, the variations in the recognition
performance of the spatiotemporal pattern due to the startingor end point were signiﬁcantly correlated with the absolute
directional error of vibration at the start or end point. If
there was an oblique effect in this absolute directional error,
then the presence of oblique effect in the recognition of thespatiotemporal pattern can be explained. However, in our previous study, we did not ﬁnd the oblique effect in the absolute
directional error of vibration [9]. One possible cause of thedifference is the difference in the number of times direction
was recognized for the cardinal or oblique directions during
the task. In a previous study, the participants only recognized
the direction once to localize the vibration source. In contrast,in the current experiment, the participants were presented with

One of the purposes of this study was to investigate how
well humans can recognize spatiotemporal patterns in a twodimensional space around the hand. The probability of being
correct (all the start points, end points, and rotational directions
were correct) was 48.9 %. This result can be used as a baseline
when examining recognition outside the body in the future,since no studies have investigated this angle.
Another purpose of the present study was to identify which
factors of spatiotemporal patterns affect the recognition accuracy. We found that the direction of the start or end points
affected the recognition accuracy, which could be explainedby the direction recognition error of the vibration source atthese points. We also found that the rotational direction of thepatterns affected the recognition accuracy. The accuracy wasslightly but signiﬁcantly higher in the case of CW rotation thanin the case of CCW rotation. The reason for this effect of therotational direction of the patterns remains unclear, but theresults are consistent with those of previous studies [17]. The
recognition accuracy was higher when the number of vibrations in the pattern was small, which can be explained by theunderestimation of tactile numerosity judgment. Furthermore,
we observed an oblique effect. The patterns starting and endingin an oblique direction were more difﬁcult to recognize thanthose starting and ending in the cardinal directions.
Our results indicate that the variation in recognition accuracy of spatiotemporal patterns can be partly explained by thehuman direction recognition ability of the vibration source atthe start or end points. This suggests that improvement of thedirection recognition of the vibration source by some methods (e.g., training) also improves the spatiotemporal patternrecognition. Conversely, there is a possibility that the direction
recognition accuracy of vibrations within the pattern would
be better than the direction recognition accuracy of a single
vibration source. For example, when pattern ID: 1 in Fig. 2
is presented, the vibration sources are presented in the orderof 315◦ , 0◦ , and 45◦ . In that case, the direction recognition
accuracy for vibration from 0◦ might be better than single
vibration from 0◦ that is present. It is because the humans
can also use the cue of the vibrations from 45◦ and 315◦ andestimate values in the middle to recognize the direction of 0◦ .
This method might improve accuracy but does not necessarily
improve time efﬁciency because the presentation duration islonger when presenting three vibrations than when presenting
a single vibration.
Based on these ﬁndings, we identiﬁed speciﬁc patterns thatwere easy and difﬁcult to recognize. The patterns that wereeasy to recognize were characterized by a small number ofvibrations, starting and ending in the cardinal direction but not
at 0◦ , and in a clockwise rotation. Performing a recognition
task using such patterns is expected to increase recognition

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

This article has been accepted for publication in IEEE Transactions on Haptics. This is the author's version which has not been fully edited andcontent may change prior to final publication. Citation information: DOI 10.1109/TOH.2022.3213313

JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015

accuracy. When conveying symbolic information by making
users discriminate between patterns, it is advisable to conﬁgure
the stimuli set with such patterns. In contrast, patterns thatwere difﬁcult to recognize were characterized by three or ﬁvevibrations, starting and ending in an oblique direction andcounterclockwise rotation. When we wish to convey spatial
information by making users recognize them, we cannot help
but present users with such patterns. In such cases, it may be
severe unless the low recognition rate is acceptable.
In the present study, we did not make participants select thepattern from candidate patterns. Instead, we made participants
answer the absolute pattern shape. This is because we wanted
to investigate the absolute recognition accuracy. If we madeparticipants discriminate against the patterns, the result of thediscrimination would be inﬂuenced by the set of patterns. By
making participants answer the absolute pattern shape, we
could clarify which factor of the patterns affects the recognition accuracy. However, from the viewpoint of symbolic information presentation, it is important to know how accurately
the pattern can be discriminated from among the provided
candidate patterns. In our ﬁrst preliminary experiment in which
all the patterns started and ended at cardinal directions (seeSupplementary Figure 2), the IT was found to be 4.24 (seeSupplementary Figure 3). We expect that the IT would change
if our 64 patterns were discriminated against because of theadded patterns starting and ending in the oblique direction.
Like these, we believe that the results of the present study
will be useful as basic knowledge for selecting a stimulus set
to be used in such discrimination tasks.
Considering the application in real-world scenarios, generalization of the result would be one of the future topics.
The results of the experiments might be affected by theexperimental design, human factors, and characteristics of themedium such as material or shapes. Regarding experimental
design, we conﬁgured the repetition times of presentation ofpatterns so that it would be similar to previous studies [17],[23] and we can fairly compare our results with them. Since
it takes more than two hours per participant in the current
experimental design, it is impractical to increase the repetition
times, but we recognize that the small repetition times are
one of the limitations of this study for generalization. There
are reports that the age [47] or the sex [48] affects thehuman tactile function. Also, there is a report that vibrotactile
localization changed with the material of the medium inthe case of a one-dimensional medium due to a change inpropagated vibration characteristics [49]. Thus, it is necessary
to conduct an experiment with other types of materials andwith people of various ages and sex as participants in futurestudies. In addition, in real-word scenarios, there could becases where there are distractors and users cannot focus on
the tactile senses. How the result would be changed when
there is a distraction is another important issue.
VI. C ONCLUSION
This study investigated vibrotactile spatiotemporal patternrecognition in a two-dimensional space around the hand.
Participants placed their hands on the medium and responded

10

to the shape of the patterns. The probability of obtaining
correct answers was 48.9 %. This result can be used as abaseline for examining recognition outside the body in the
future. In addition, we found pattern factors (e.g., start andend points or rotational direction) that affect the recognition
accuracy. An oblique effect was also observed. The patterns
starting or ending in the oblique direction were more difﬁcult
to recognize than those in the cardinal direction.
From the viewpoint of symbolic information presentation,
it is important to know how accurately a pattern can bediscriminated from the candidate patterns provided. We believethat the results of the present study will be useful as basic
knowledge for selecting a stimulus set which can be used insuch discrimination tasks in future studies.
R EFERENCES[1] J. Scott and R. Gray, “A comparison of tactile, visual, and auditory
warnings for rear-end collision prevention in simulated driving,” Human
factors, vol. 50, no. 2, pp. 264–275, 2008.
[2] S. Choi and K. J. Kuchenbecker, “Vibrotactile display: Perception,technology, and applications,” In Proceedings of the IEEE, vol. 101,
no. 9, pp. 2093–2104, 2012.
[3] H. E. Burtt, “Tactual illusions of movement.” Journal of Experimental
Psychology, vol. 2, no. 5, p. 371, 1917.
[4] J. H. Kirman, “Tactile apparent movement: The effects of interstimulus
onset interval and stimulus duration,” Perception & Psychophysics,
vol. 15, no. 1, pp. 1–6, 1974.
[5] A. Israr and I. Poupyrev, “Tactile brush: drawing on skin with a tactile
grid display,” in Proceedings of the SIGCHI Conference on Human
Factors in Computing Systems, pp. 2019–2028, 2011.
[6] K. Yatani, N. Banovic, and K. Truong, “Spacesense: representing
geographical information to visually impaired people using spatial tactile
feedback,” in Proceedings of the SIGCHI Conference on Human Factors
in Computing Systems, pp. 415–424, 2012.
[7] J. Lee, J. Han, and G. Lee, “Investigating the information transfer
efﬁciency of a 3x3 watch-back tactile display,” in Proceedings of the
33rd Annual ACM Conference on Human Factors in Computing Systems,
pp. 1229–1232, 2015.
[8] M.-J. Hsieh, R.-H. Liang, and B.-Y. Chen, “Nailtactors: eyes-free spatial
output using a nail-mounted tactor array,” in Proceedings of the 18th
International Conference on Human-Computer Interaction with Mobile
Devices and Services, pp. 29–34, 2016.
[9] Y. Ujitoko, R. Tokuhisa, S. Sakurai, and K. Hirota, “Impact vibration
source localization in two-dimensional space around hand,” IEEE Transactions on Haptics, pp. 1–1, 2021.
[10] Y. Ujitoko and S. Kuroki, “Sinusoidal vibration source localization in
two-dimensional space around hand,” Frontiers in Psychology, p. 2834,
2022.
[11] L. E. Miller, L. Montroni, E. Koun, R. Salemme, V. Hayward, andA. Farnè, “Sensing with tools extends somatosensory processing beyond
the body,” Nature, vol. 561, no. 7722, pp. 239–242, 2018.
[12] L. E. Miller, C. Fabio, V. Ravenda, S. Bahmad, E. Koun, R. Salemme,
J. Luauté, N. Bolognini, V. Hayward, and A. Farnè, “Somatosensory
cortex efﬁciently processes touch located beyond the body,” Current
Biology, vol. 29, no. 24, pp. 4276–4283, 2019.
[13] J. Sreng, A. Lécuyer, and C. Andriot, “Using vibration patterns toprovide impact position information in haptic manipulation of virtual
objects,” In Proceedings of International Conference on Human Haptic
Sensing and Touch Enabled Computer Applications, pp. 589–598, 2008.
[14] R. Mehra, C. Hohnerlein, D. Perek, E. Gatti, R. DeSalvo, and S. Keller,“Hapticwave: directional surface vibrations using wave-ﬁeld synthesis,”
In Proceedings of ACM SIGGRAPH 2016 Emerging Technologies, pp.
1–2, 2016.
[15] C. W. Borst and C. D. Cavanaugh, “Touchpad-driven haptic communication using a palm-sized vibrotactile array with an open-hardware
controller design,” in Proceedings of EuroHaptics conference, pp. 344–
347, 2004.
[16] G.-H. Yang, D. Ryu, and S. Kang, “Vibrotactile display for handheld input device providing spatial and directional information,” inProceedings of World Haptics 2009-Third Joint EuroHaptics conference
and Symposium on Haptic Interfaces for Virtual Environment andTeleoperator Systems, pp. 79–84, 2009.

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

This article has been accepted for publication in IEEE Transactions on Haptics. This is the author's version which has not been fully edited andcontent may change prior to final publication. Citation information: DOI 10.1109/TOH.2022.3213313

JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015[17] K. Yatani and K. N. Truong, “Semfeel: a user interface with semantic
tactile feedback for mobile touch-screen devices,” in Proceedings ofthe 22nd annual ACM symposium on User interface software andtechnology, pp. 111–120, 2009.
[18] G.-H. Yang, M.-s. Jin, Y. Jin, and S. Kang, “T-mobile: Vibrotactile
display pad with spatial and directional information for hand-helddevice,” in Proceedings of 2010 IEEE/RSJ International Conference onIntelligent Robots and Systems, pp. 5245–5250, 2010.
[19] J. Rantala, K. Myllymaa, R. Raisamo, J. Lylykangas, V. Surakka,
P. Shull, and M. Cutkosky, “Presenting spatial tactile messages witha hand-held device,” in Proceedings of 2011 IEEE World Haptics
Conference, pp. 101–106, 2011.
[20] J. Park, J. Kim, Y. Oh, and H. Z. Tan, “Rendering moving tactile strokeon the palm using a sparse 2d array,” in Proceedings of International
Conference on Human Haptic Sensing and Touch Enabled Computer
Applications, pp. 47–56, 2016.
[21] Y. Gong, D. Wang, Q. Guo, H. Luo, Y. Zhang, and J. Xiao, “Identiﬁcation of vibrotactile ﬂow patterns on a handheld haptic device,”in Proceedings of International Conference on Virtual Reality and
Visualization, pp. 76–81, 2019.
[22] J. Seo and S. Choi, “Perceptual analysis of vibrotactile ﬂows on a mobile
device,” IEEE Transactions on Haptics, vol. 6, no. 4, pp. 522–527, 2013.
[23] ——, “Edge ﬂows: Improving information transmission in mobile devices using two-dimensional vibrotactile ﬂows,” in Proceedings of IEEE
World Haptics Conference, pp. 25–30, 2015.
[24] M. Matscheko, A. Ferscha, A. Riener, and M. Lehner, “Tactor placementin wrist worn wearables,” in Proceedings of International Symposium on
Wearable Computers (ISWC) 2010, pp. 1–8, 2010.
[25] S. C. Lee and T. Starner, “Buzzwear: alert perception in wearable tactile
displays on the wrist,” in Proceedings of the SIGCHI conference on
Human factors in computing systems, pp. 433–442, 2010.
[26] Y.-C. Liao, Y.-L. Chen, J.-Y. Lo, R.-H. Liang, L. Chan, and B.-Y. Chen,“Edgevib: Effective alphanumeric character output using a wrist-worntactile display,” in Proceedings of the 29th Annual Symposium on User
Interface Software and Technology, pp. 595–601, 2016.
[27] Q. Chen, S. T. Perrault, Q. Roy, and L. Wyse, “Effect of temporality,
physical activity and cognitive load on spatiotemporal vibrotactile pattern recognition,” in Proceedings of the 2018 International Conference
on Advanced Visual Interfaces, pp. 1–9, 2018.
[28] L. Kohli, M. Niwa, H. Noma, K. Susami, Y. Yanagida, R. W. Lindeman,
K. Hosaka, and Y. Kume, “Towards effective information display using
vibrotactile apparent motion,” in Proceedings of 14th Symposium on
Haptic Interfaces for Virtual Environment and Teleoperator Systems,
pp. 445–451, 2006.
[29] M. Niwa, R. W. Lindeman, Y. Itoh, and F. Kishino, “Determining
appropriate parameters to elicit linear and circular apparent motion using
vibrotactile cues,” in Proceedings of World Haptics 2009-Third Joint
EuroHaptics conference and Symposium on Haptic Interfaces for Virtual
Environment and Teleoperator Systems, pp. 75–78, 2009.
[30] H. Tan, R. Gray, J. J. Young, and R. Taylor, “A haptic back display forattentional and directional cueing,” 2003.
[31] L. A. Jones, J. Kunkel, and E. Piateski, “Vibrotactile pattern recognitionon the arm and back,” Perception, vol. 38, no. 1, pp. 52–68, 2009.
[32] A. Israr, Z. Schwemler, J. Mars, and B. Krainer, “Vr360hd: a vr360
player with enhanced haptic feedback,” in Proceedings of the 22nd ACM
Conference on Virtual Reality Software and Technology, pp. 183–186,
2016.
[33] M. Ahtamad, C. Spence, C. Ho, and R. Gray, “Warning drivers about
impending collisions using vibrotactile ﬂow,” IEEE transactions on
haptics, vol. 9, no. 1, pp. 134–141, 2015.
[34] M. Kim, A. Abdulali, and S. Jeon, “Rendering vibrotactile ﬂow onbackside of the head: Initial study,” in Proceedings of IEEE Games,Entertainment, Media Conference, pp. 1–250, 2018.
[35] E. Piateski and L. Jones, “Vibrotactile pattern recognition on the armand torso,” in Proceedings of First Joint Eurohaptics Conference andSymposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems. World Haptics Conference, pp. 90–95, 2005.
[36] L. A. Jones and K. Ray, “Localization and pattern recognition withtactile displays,” in Proceedings of Symposium on Haptic Interfaces for
Virtual Environment and Teleoperator Systems, pp. 33–39, 2008.
[37] J. Rosenthal, N. Edwards, D. Villanueva, S. Krishna, T. McDaniel,
and S. Panchanathan, “Design, implementation, and case study of apragmatic vibrotactile belt,” IEEE Transactions on Instrumentation andMeasurement, vol. 60, no. 1, pp. 114–125, 2010.
[38] T. L. White, “The perceived urgency of tactile patterns,” ARMYRESEARCH LAB ABERDEEN PROVING GROUND MD HUMANRESEARCH AND ENGINEERING …, Tech. Rep., 2011.

11[39] G.-H. Yang, W. Lee, and S. Kang, “Development of vibrotactile pedestal
with multiple actuators and application of haptic illusions for information delivery,” IEEE Transactions on Industrial Informatics, vol. 15,
no. 1, pp. 591–598, 2018.
[40] S. Appelle and F. Gravetter, “Effect of modality-speciﬁc experience on
visual and haptic judgment of orientation,” Perception, vol. 14, no. 6,
pp. 763–773, 1985.
[41] A. M. Kappers, J. Bay, and M. A. Plaisier, “Perception of vibratory
direction on the back,” in Proceedings of International Conference on
Human Haptic Sensing and Touch Enabled Computer Applications, pp.
113–121, 2020.
[42] A. Gallace, H. Z. Tan, and C. Spence, “Numerosity judgments for tactile
stimuli distributed over the body surface,” Perception, vol. 35, no. 2, pp.
247–266, 2006.
[43] G. Park, H. Cha, and S. Choi, “Haptic enchanters: Attachable and detachable vibrotactile modules and their advantages,” IEEE transactions
on haptics, vol. 12, no. 1, pp. 43–55, 2018.
[44] J. O. Wobbrock, L. Findlater, D. Gergle, and J. J. Higgins, “The aligned
rank transform for nonparametric factorial analyses using only anova
procedures,” in Proceedings of the SIGCHI conference on human factors
in computing systems, pp. 143–146, 2011.
[45] A. H. Javadi and C. Aichelburg, “When time and numerosity interfere:
the longer the more, and the more the longer,” PloS one, vol. 7, no. 7,
p. e41496, 2012.
[46] B. Efron and R. J. Tibshirani, An introduction to the bootstrap. CRC
press, 1994.
[47] S. McIntyre, S. S. Nagi, F. McGlone, and H. Olausson, “The effects
of ageing on tactile function in humans,” Neuroscience, vol. 464, pp.
53–58, 2021.
[48] A. M. Fernandes and P. B. Albuquerque, “Tactual perception: a review
of experimental variables and procedures,” Cognitive processing, vol. 13,
no. 4, pp. 285–301, 2012.
[49] D. Gongora, H. Nagano, Y. Suzuki, M. Konyo, and S. Tadokoro,“Collision representation using vibrotactile cues to bimanual impact
localization for mobile robot operations,” In Proceedings of 2017 IEEE
International Conference on Robotics and Automation, pp. 461–468,
2017.

Yusuke Ujitoko is a researcher of NTT Communication Science Laboratories since 2020. He used to bea member of R&D Group in Hitachi, Ltd from 2016
to 2020. He received a PhD. in engineering fromthe University of Electro-Communications, Japan
in 2020. He received a B.E. degree in mechanicalengineering and M.A.E. degree in inter-disciplinary
information studies from the University of Tokyo,
Japan in 2014 and 2016, respectively. His researchinterests include applied haptic perception and haptic
interfaces.

Ryunosuke Tokuhisa is a graduate student of the
University of Electro-Communications. He received
the Asc.B. Eng. degree in 2018 and the B.Eng.
degree in 2020 respectively from Electronic Control Department and Advanced Electronic Engineering Program at National Institute of Technology(KOSEN), Niihama College, Japan.

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

This article has been accepted for publication in IEEE Transactions on Haptics. This is the author's version which has not been fully edited andcontent may change prior to final publication. Citation information: DOI 10.1109/TOH.2022.3213313

JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015

Koichi Hirota received a BS degree from the
University of Tokyo, Japan, in 1988. He received
a PhD degree from the University of Tokyo, in
1994. He was then an assistant professor with the
Toyohashi University of Technology in 1995. In
2000, he was an associate professor with the University of Tokyo. Currently, he is a professor in
the Department of Human Media Systems, Graduate School of Information Systems, University ofElectro-Communications. His research interests include haptic rendering and human interfaces. He hasreceived several academic awards.

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

12