MetaHug: Audio-Visual Stimuli Change Stress Buffer Effect of Hug*
Masahiro Shiomi, Member, IEEE and Norihiro Hagita, Member, IEEE

Abstract— In human science literature, a hug interaction
from a close person buffers the stress of the person being hugged.
In this study, we investigated whether audio-visual stimuli
during a hug with a robot change such stress buffer effects. For
this purpose, we developed the MetaHug system, which consistsof a huggable robot and a head-mounted-display that enables avirtual agent to reciprocate hugs. In our experiment,
participants did stressful serial subtraction tasks after theyexperienced hug interactions with either two kinds of virtual
agents: female or male appearances. Experiment results with 18
participants showed that participants showed significantly low
stress when they did a hug with opposite-gender appearance
agent compared to same-gender appearance agent.

visual stimuli [13, 15, 16]. Thus, we chose a similar approachin this study; we developed a hug display system called
MetaHug that consists of a huggable robot which canreciprocate a hug and a virtual reality application to control
visual-audio stimuli and enables a virtual agent to physically
hug a user (Fig. 1). Through an experiment with MetaHug, we
addressed the following question:- Does perceived gender of a virtual agent, which iscontrolled by audio-visual stimuli, change the stress buffer
effects of hugs from the agent?

I. INTRODUCTION
Every day, we face many kinds of stressors like
communication failure with others, bad UIs, virus-infested
computer programs, PC crashes, losing critical data, and so on.
Against such stressors, social support (e.g., caring expressions
and verbal and nonverbal behaviors [1, 2]) from close people
such as friends, family members and lovers softens suchnegative effects. Several studies reported that haptic
interaction like a hug from a member of a support group more
effectively mitigates the negative effects of stressors andprovides stress-buffering relief [3-6]. A past study reported
that an imagined haptic interaction from close people also
buffers stress and pain [7].
However, these past studies mainly evaluated the effects ofsuch haptic interaction from close people only. Therefore,knowledge about the positive effects of haptic interactions
remains limited for people who have close relationships withothers. Instead of only dealing with haptic interaction effects
with close people, in this study we investigated such stressbuffer effects through a haptic interaction with an agent for
two reasons: 1) since past studies reported that a reciprocatedhug from a physical agent (i.e., a robot) provided such positiveeffects as encouraging prosocial behaviors and self-disclosures[8, 9], we thought a hug from an agent would have positiveeffects for buffering stress too, and 2) using an agent systemenables more people to experience hug interactions, notlimited to people who have close relationships with others.
To investigate the effects of hug interactions from an agent,
one concern is the perceived gender of the interaction partner,
because haptic interaction effects are strongly influenced by a
combination of genders between interaction partners [10-14].
To solve this problem, we focused on a phonomenon thatcombines both pseudo and actual stimuli that could change
people’s perceptions, including perceived gender by audio* This research work was supported by JSPS KAKENHI Grant Number
JP15H05322 and JP16K12505, and Tateisi Science and Technology
Foundation.
M. Shiomi, A and N. Hagita are with ATR, Kyoto, 6190288, Japan. (email: m-shiomi@atr.jp)

Figure 1. Participant experiencing MetaHug

II. RELATED WORKS
Already many research works exist which investigate
positive effects of haptic interaction between people. For
instance, Grewen et al. investigated that hug interaction during
stressor events, and reported that hug interaction with close
people reduce blood pressure and heart rates [3]. Cohen et al.
also reported that hug interactions provide stress-buffering,and protection against the infectious virus that causes thecommon cold [4]. Jakubiak et al. reported that an imagined
touch from close people buffers stress and pain [7]. From
another perspective, researchers investigated the gendereffects in haptic interaction and reported that being touched by
the opposite gender may be advantageous in several situations[10] [11] [12].
In human-robot interaction research fields, several
research works have started to investigate haptic interaction
effects [17-26]. For instance, touching a seal robot (Paro)provided mental health benefits for elderly people [17]. Being
touched by a robot increased people’s motivation during
monotonous tasks and improved their impressions of it [18],and prosocial behavior [19]. Moreover, a few study
investigated the effects of hug interaction with robots; e.g.,conversations through huggable devices decreased stresslevels more than with a smartphone in an investigation ofcortisol levels [22]. Other research reported that a reciprocal
hug behavior from a robot encouraged prosocial behavior of
people and their self-disclosure, as well as the willingness tointeract with it [23] [24]. From another perspective, a study
reported that perceived gender of a robot change hug
impressions by using VR system [27].
These research works investigated the positive effects andperceived gender effects for hug interaction with a robot, but
the relationships between stress buffering effects andperceived gender of a robot is still unknown. If perceived
gender of a robot have influences to stress buffering effects,
unveiling it would provide useful knowledge for huginteraction design with a robot. Thus, we investigated theeffects of perceived gender of a robot to stress buffering effects.
III. SYSTEM CONFIGURATION
Figure 2 shows the MetaHug system, which consists of ahead-mounted display (HMD), a head position tracker, virtual
agents, a robot, a touch sensor, and a motion controller. The
details are described below.
A. HMD and head position tracker
We used Oculus Rift as a HMD to show virtual agents tothe participants. Oculus Rift provides high-resolution (1080 x
1200 pixels per eye) stereoscopic images with a 110 field ofview with 90-HZ refresh rates. To only investigate the effectsof the audio-visual stimuli from the virtual agents, we removed
all of the background images from the virtual reality
application.
Oculus Rift can also be used in a head position tracker with
two Oculus sensors, which increased the head-tracking
accuracy by combining three different types of sensors: gyro,
acceleration, and geomagnetic.

during which the robot periodically patted the person on theback.
To ensure safety during hugs, we covered its frame with
polypropylene cotton and used weak motors that can be easily
resisted if needed. Moreover, we installed a touch sensor(ShokacCube by Touchence) in its left arm that makes the firstcontact with the participants during the hug. This sensor,
which can measure the height change on the top surface of asoft material with 16 measurement points, is 36 x 20 x 30 mm
and sends pressure information to the motion controller
function with a maximum of 100 Hz. During hug interactions,
if the pressure values exceed a certain threshold, the motion
controller interrupts the robot’s hug and opens its arms forsafety.
D. Motion controller
We implemented a motion controller to autonomously
manage the motions of both the virtual agents and the robot
using position and pressure information from the sensors. Thisfunction controls the start/end timings of the hug animations
and the behaviors of both the virtual agent and the robot.

PositontrackerTouchsensorHead position
HMD

Virtualagent

Motioncontroller
Pressure

Robot

Hug

Figure 2. System overview

B. Virtual agents
We prepared two virtual agents: a female and a male (Fig.
3, left and middle). For the female agent’s appearance andvoice, we combined a 3D model from the Futaba Honoka
Character Pack by the Game Asset Studio and speechsynthesis software from VOICEROID+ Kyomachi Seika EX
by AHS. For the male agent’s appearance and voice, we
combined a 3D model from the Taichi Character Pack by theGame Asset Studio and speech synthesis software from
VOICEROID+ Minase Kou EX by AHS.
The heights of both agents were identical. Based on the
robot position, we also adjusted their positions in the virtual
reality applications to maintain the same distance relationshipbetween the application and the real world. We implemented
eye-contact and lip-sync behaviors for both virtual agentsduring idling states or speaking as well as hug animation forthem. The hug animation lasted one minute.
C. Robot and touch sensor
We used a robot, Moffuly, that resembles a large teddy
bear (Fig. 3, right) as a huggable robot platform. It is 200-cm
tall with two elbows (1*2 DOF) and a speaker. Its 80-cm-longarms are adequate for reciprocating hugs. We controlled it witha Raspberry Pi 2 Model B. Its hug behavior lasted one minute,

Figure 3. Virtual agents and Moffuly

IV. EXPERIMENT
We conducted a laboratory stress study to investigate thestress buffer effect of physical hug interaction with a virtual
agent. For this purpose, we employed a serial subtraction task
[7] that reliably produces stress responses, which is commonlyused in cognitive science experiments.

A. Hypotheses and prediction
Hugs from close people buffer the stress of those being
hugged [3] [4], even if touch interaction is imagined [7]. Past
studies revealed that pseudo audio-visual information
increased the feelings of the reality of physical stimuli andchanged their perceptions [15] [16]. Therefore, we believe thatphysical hug stimuli with audio-visual stimuli will also buffer
the stress of people.
However, the effects of tactile interaction (including hugs)
are changed by the perceived gender of the interaction partner.
Past studies reported that opposite-gender touches are more
often positively evaluated than same-gender touches, and its
tendency strongly occurred in males [10]. Another study
reported that same-gender touches by males created complex
and contradictory situations of acceptance, rejection, andsuspicion in a nursing situation [12]. Therefore, if we control
the perceived gender of the virtual agents using different
appearances and voices, the stress buffering effect will change
due to the combinations of genders between people and agents.
Based on these considerations, we made the following
hypotheses:
Prediction: Hug interaction with a virtual agent will buffer
their stress more when the perceived gender of the virtual
agent is opposite to a participant.
B. Participants
Eighteen Japanese people (9 women and 9 men, whose
average ages were 37.94, age range was 21 to 56, and S.D was
9.65) were paid for their participation. They had neverinteracted with our robot or used an HMD.
C. Environment
We conducted an experiment in a 5 x 2 m room in alaboratory. We attached the robot to a wall and installed two
cameras, microphones, and Oculus sensors near it.
D. Conditions
The study had a within-participant design with thefollowing two conditions whose order was counterbalanced:
Same-gender condition: participants interacted with therobot using the HMD, which showed an agent with a samegender appearance and voice.
Opposite-gender condition: participants interacted withthe robot using the HMD, which showed an agent with an
opposite-gender appearance and voice.
E. Procedure
Before the first session, the participants were given a brief
description of our experiment’s purpose and procedure. This
research was approved by our institution’s ethics committee
for studies involving human participants. Written, informed
consent was obtained from all of them.
We showed our robot, explained their interaction with it,
and literally demonstrated how to hug it. We added that the
robot’s face part with which their faces make contact duringhugging was replaceable to alleviate any sanitation concerns.
We also explained how to use the HMD and asked them tomaintain the hug interaction until the virtual agents stopped
talking.

In both conditions, after the participants put on the HMD
and approached the robot, it automatically started the hug
behavior and asked them to maintain the hug for about one
minute. After the hug ended, the participants were instructed
to do a subtraction task, which is a part of the Trier Social
Stress Test and reliably produce stress response [28, 29], fromamong these three patterns (the order was counterbalanced):
2091 to zero in 17-step sequences, 2337 to zero in 19-step
sequences, or 3567 to zero in 29-step sequences. All threetasks require 123 subtractions to become zero.
At the beginning of the subtraction task, the experimenter
instructed the participants to calculate as quickly and ascorrectly as possible. When they made mistakes, theexperimenter played synthetic sounds that informed of their
mistake and asked them to begin again from the first value.
During the subtraction task and after it ended, the participants
rated their stress and completed questionnaires (details aredescribed in the next subsection)
F. Measurements
In this study, we measured the perceived stress during thetask by a questionnaire, because this approach can accurately
measure the perceived stress compared to before or after
measurement [30]. Moreover, this past study suggested thatthe self-reported stress showed a temporal correlation between
the physiological and the psychological stress response [30].
Other past study which investigate the stress buffering effects
about imagined touch from close people employed thisapproach [7], therefore this approach is applicable toinvestigate the perceived stress during the task. Based on these
considerations, we also measure the perceived stress during
the task by the questionnaire. Therefore, participants rated
their tress during the subtraction task on a 0 to 10 numericalstress rating scale, which ranged from no stress to great stress.
They made stress ratings at 30 seconds intervals (prompted bya tone) for a total of ten stress ratings (α = .97).
For a manipulation check about the perceived gender, weinvestigated whether the appearance and the voice of thevirtual agents changed their perceived gender by aquestionnaire on a 1-to-7 point scale, where 7 is the mostsame-gender and 1 is the most opposite-gender. This
questionnaire is conducted after each subtraction task.
As an additional evaluation, we asked the participants tocomplete a short questionnaire about their perceptions of thesubtraction task to assess their enthusiasm for the task,following past similar study [7]. Participants completed 7point bipolar scales to describe their feelings about the task
(i.e., not fun-fun, not enjoyable-enjoyable, boring-interesting,
not frustrating-frustrating), and the item assessing frustration
was reverse scored (α = .73).
Moreover, we measured the performances of thesubtraction tasks and errors. For example, a performance scoreof 15 indicates that the participant correctly completed 15
serial subtractions during the 5-minute task.

V. RESULTS
A. Manipulation check
Figure 4 shows the questionnaire results about perceived
gender. We conducted a two-factor mixed ANOVA for thegender and agent factors, and the results showed significant
differences in the agent factor (F(1, 16)=66.038, p<.001, η2=.805), but we found no significant differences in the genderfactor (F(1, 16)=0.615, p=.444, η2 = .037) or the interaction
effect (F(1, 16)=0.051, p=.824, η2 = .003). The perceived
gender was significantly different due to the changes of theappearances and the voices with our system.
B. Verification of prediction about perceived stress
Figure 5 shows the perceived stress of the participants. We
conducted a two-factor mixed ANOVA for the gender andcondition factors, and the results showed significant
differences in the agent factor (F(1, 16)=4.768, p=.044, η2= .230), but we found no significant differences in the genderfactor (F(1, 16)=0.459, p=.508, η2 = .028) or the interaction
effect (F(1, 16)=2.700, p=.120, η2 = .120). These results
indicated that a hug interaction with an opposite-gender agent
buffers the stress of both female and male participants during
the tasks. Thus, prediction was supported.
C. Additional analysis: enthusiasm and task performance
Figure 6 shows the enthusiasm in all the conditions. We
conducted a two-factor mixed ANOVA for the gender andcondition factors, and the results showed no significant
differences in the agent factor (F(1, 16)=0.04, p=.951, η2= .001), in the gender factor (F(1, 16)=0.123, p=.730, η2= .008) or the interaction effect (F(1, 16)=0.004, p=.951, η2= .021). Thus, hug interactions under the opposite-gender
agent condition buffers stress toward participants, but ourstudy found no clear significant differences in the enthusiasm.
Figure 7 shows the task performances in all the conditions.
We conducted a two-factor mixed ANOVA for the gender andcondition factors, and the results showed no significant
differences in the agent factor (F(1, 16)=0.01, p=.980, η2= .001), in the gender factor (F(1, 16)=1.304, p=.270, η2= .075) or the interaction effect (F(1, 16)=0.337, p=.569, η2= .021). Thus, our study found no clear significant differencesin the performances of the subtraction tasks.

Same-gender
Male

Opposite-gender
FemaleFigure 4. Perceived gender (7 is most same-gender feeling)* : p <.05.

10

*

8
6
4

2
0
Same-gender
Male

Opposite-gender
FemaleFigure 5. Perceived stress during taks

7
6
5
4
3
2
1
Same-gender

Male

Opposite-gender

FemaleFigure 6. Enthusiasm of tasks

VI. DISCUSSION
A. Implications
Our experimental results showed that the audio-visual
stimuli changed the stress-buffering effects of hug
interactions with virtual agents, and hug interaction with
opposite-gender decreased their perceived stress. In humanscience literatures reported similar trends of same-gender andopposite-gender effects in haptic interaction, e.g., a touchfrom opposite-gender is more often positively evaluated thana touch from same-gender [10], and for males they reported
negative attitudes to being touching by the same-gender [12].
These results suggest design policy ramifications forapplications that will be used in mental therapy or counseling
contexts by interacting with virtual/physical agents [31-35].

* : p <.05.

*
7
6
5
4
3
2
1

18

12
6
0

Same-gender
Male

Opposite-gender
FemaleFigure 7. Performance of tasks

B. Future work
The experimental results revealed that perceived genderchanged stress buffering effects of hug interaction with arobot, but still it is unknown why such effects occurred. We
need to investigate further relationships via othermeasurements to discuss the effects of hug interaction with
virtual agents. One possible approach is to investigate
participants’ impression more deeply, actually a past study
reported that perceived gender of virtual agents changed hug
impression more positively, such difference might be one
reason to decrease their perceived stress. From anotherperspective, investigating physiological measurements suchas cortisol, which indicates the stress levels of people, would
be effective to investigate the hug interaction effects. Other
physiological measures, such as brainwaves, might also help
understand the effects of hug interaction with virtual agents[19].
One technical future work is to provide richer hug stimuli.
In this study we used a touch sensor to detect human presence
and to stop the robot’s hug behavior for safety, but it also canbe used to change hug stimuli for both stronger/softer. For thispurpose, may be additional touch sensors to the robot’s bodyand/or face parts would be useful to detect hug situation
deeply. Moreover, adding more DOFs to the robot’s arms
would enable different kinds of hug interactions, such as
patting a head or a stroking a back of an interacting person.
Another possible future work is to compare different
setting such as non-hug condition or without HMD. For
instance, past study which investigated touch effects employednon-touch conditions [18, 36]. Comparing non-touchconditions might provide richer knowledge about huginteraction with a robot. Moreover, a direct hug interaction
with the robot (i.e., without HMD) might have stress buffering
effect too, because past studies showed the positive effects of
hug interaction with it [24]; but the main aim of this paper was
to investigate the relationship between perceived gender andstress buffering effects of hug interaction. Therefore,
Comparison of the level of stress buffering effect of hug
interaction is out of scope in this study, but it would be one
interesting future work, too.
C Limitations
On the other hand, we need to carefully contemplate theexperiment results of this study. Since we only used a specificrobot and virtual agents, we must test different types of robots
and virtual agents to generalize our experimental results.
Moreover, in this study we did not confirm the sexual fluidityof the participants, which might influence the haptic
interaction effects. Even if such limitations exist, we still
believe that our setting offers essential knowledge forresearchers who are interested in hug interactions with virtual
agents and/or robots.
VII. CONCLUSION
We developed a MetaHug system that consists of ahuggable robot and a virtual reality application, andexperimentally investigated how physical hug interactions

with virtual agents buffer the stress of people using a serial
subtraction task as a stressor stimuli. Our experimental results
showed that the perceived gender of the virtual agents andtheir hug interactions significantly influences stress buffers,
and hug interaction with the opposite-gender agent decreases
perceived stress compared to the same-gender agent.
REFERENCES[1] S. Cohen, “Social relationships and health,” American Psychologist, vol.
59, no. 8, pp. 676, 2004.
[2] B. C. Feeney, “A secure base: Responsive support of goal strivings andexploration in adult intimate relationships,” Journal of personality andsocial psychology, vol. 87, pp. 631-648, 2004.
[3] K. M. Grewen, B. J. Anderson, S. S. Girdler, and K. C. Light, “Warmpartner contact is related to lower cardiovascular reactivity,” Behavioral
medicine, vol. 29, no. 3, pp. 123-130, 2003.
[4] S. Cohen, D. Janicki-Deverts, R. B. Turner, and W. J. Doyle, “Doeshugging provide stress-buffering social support? A study ofsusceptibility to upper respiratory infection and illness,” Psychologicalscience, vol. 26, no. 2, pp. 135-147, 2015.
[5] J. A. Bartz, J. Zaki, N. Bolger, and K. N. Ochsner, “Social effects ofoxytocin in humans: context and person matter,” Trends in cognitive
sciences, vol. 15, no. 7, pp. 301-309, 2011.
[6] J. K. Burgoon, D. B. Buller, J. L. Hale, and M. A. Turck, “Relational
messages associated with nonverbal behaviors,” Human
Communication Research, vol. 10, no. 3, pp. 351-378, 1984.
[7] B. K. Jakubiak, and B. C. Feeney, “Keep in touch: The effects ofimagined touch support on stress and exploration,” Journal of
Experimental Social Psychology, vol. 65, pp. 59-67, 2016.
[8] A. Nakata, M. Shiomi, M. Kanbara, and N. Hagita, “Does Being Hugged
by a Robot Encourage Prosocial Behavior?,” in Proceedings of theCompanion of the 2017 ACM/IEEE International Conference on
Human-Robot Interaction, pp. 221-222, 2017.
[9] A. Nakata, M. Shiomi, M. Kanbara, and N. Hagita, “Does a Reciprocated
Hug from a Robot Encourage Self-Disclosure?,” in Proceedings of theCompanion of the 2017 ACM/IEEE International Conference on
Human-Robot Interaction, pp. 223-224, 2017.
[10] D. S. Stier, and J. A. Hall, “Gender differences in touch: An empiricaland theoretical review,” Journal of personality and social psychology,
vol. 47, no. 2, pp. 440, 1984.
[11] A. S. Ebesu Hubbard, A. A. Tsuji, C. Williams, and V. Seatriz,“ Effects of Touch on Gratuities Received in Same ‐ Gender andCross‐Gender Dyads,” Journal of Applied Social Psychology, vol.
33, no. 11, pp. 2427-2438, 2003.
[12] J. A. Evans, “Cautious caregivers: gender stereotypes and thesexualization of men nurses' touch,” Journal of advanced nursing, vol.
40, no. 4, pp. 441-448, 2002.
[13] K. Suzuki, M. Yokoyama, Y. Kionshita, T. Mochizuki, T. Yamada, S.
Sakurai, T. Narumi, T. Tanikawa, and M. Hirose, “Gender-Impression
Modification Enhances the Effect of Mediated Social Touch Between
Persons of the Same Gender,” Augmented Human Research, vol. 1, no.
1, pp. 1-11, 2016.
[14] J. B. F. van Erp, and A. Toet, “Social Touch in Human–Computer
Interaction,” Frontiers in Digital Humanities, vol. 2, no. 2, 2015.
[15] T. Narumi, S. Nishizaka, T. Kajinami, T. Tanikawa, and M. Hirose,“Augmented reality flavors: gustatory display based on edible marker
and cross-modal interaction,” in Proceedings of the SIGCHI conferenceon human factors in computing systems, pp. 93-102, 2011.
[16] K. Matsumoto, Y. Ban, T. Narumi, Y. Yanase, T. Tanikawa, and M.
Hirose, “Unlimited corridor: redirected walking techniques using visuo
haptic interaction,” in ACM SIGGRAPH 2016 Emerging Technologies,
Anaheim, California, pp. 1-2, 2016.
[17] R. Yu, E. Hui, J. Lee, D. Poon, A. Ng, K. Sit, K. Ip, F. Yeung, M. Wong,and T. Shibata, “Use of a Therapeutic, Socially Assistive Pet Robot(PARO) in Improving Mood and Stimulating Social Interaction andCommunication for People With Dementia: Study Protocol for a
Randomized Controlled Trial,” JMIR research protocols, vol. 4, no. 2,
2015.
[18] M. Shiomi, K. Nakagawa, K. Shinozawa, R. Matsumura, H. Ishiguro,
and N. Hagita, “Does A Robot’s Touch Encourage Human Effort?,”International Journal of Social Robotics, vol. 9, pp. 5-15, 2016.

[19] H. Fukuda, M. Shiomi, K. Nakagawa, and K. Ueda, “‘Midas touch’in
human-robot interaction: Evidence from event-related potentials during
the ultimatum game,” in Human-Robot Interaction (HRI), 2012 7th
ACM/IEEE International Conference on, pp. 131-132, 2012.
[20] K. Nakagawa, M. Shiomi, K. Shinozawa, R. Matsumura, H. Ishiguro,
and N. Hagita, “Effect of Robot’s Whispering Behavior on People’s
Motivation,” International Journal of Social Robotics, vol. 5, no. 1, pp.
5-16, 2012.
[21] T. Hirano, M. Shiomi, T. Iio, M. Kimoto, I. Tanev, K. Shimohara, and
N. Hagita, “How Do Communication Cues Change Impressions of
Human–Robot Touch Interaction?,” International Journal of Social
Robotics, 2017.
[22] H. Sumioka, A. Nakae, R. Kanai, and H. Ishiguro, “Huggablecommunication medium decreases cortisol levels,” Scientific Reports,
vol. 3, pp. 3034, 2013.
[23] M. Shiomi, A. Nakata, M. Kanbara, and N. Hagita, “A hug from a robot
encourages prosocial behavior,” in 2017 26th IEEE International
Symposium on Robot and Human Interactive Communication (ROMAN), pp. 418-423, 2017.
[24] M. Shiomi, A. Nakata, M. Kanbara, and N. Hagita, "A Robot that
Encourages Self-disclosure by Hug," Social Robotics: 9th International
Conference, ICSR 2017, Tsukuba, Japan, November 22-24, 2017,
Proceedings, A. Kheddar, E. Yoshida, S. S. Ge et al., eds., pp. 324-333,
Cham: Springer International Publishing, 2017.
[25] M. Shiomi, T. Minato, and H. Ishiguro, “Subtle Reaction and Response
Time Effects in Human-Robot Touch Interaction,” in International
Conference on Social Robotics, pp. 242-251, 2017.
[26] C. J. A. M. Willemse, A. Toet, and J. B. F. van Erp, “Affective andBehavioral Responses to Robot-Initiated Social Touch: Toward
Understanding the Opportunities and Limitations of Physical Contact in
Human–Robot Interaction,” Frontiers in ICT, vol. 4, no. 12, 2017.
[27] M. Shiomi, and N. Hagita, "Do Audio-Visual Stimuli Change Hug
Impressions?," Social Robotics: 9th International Conference, ICSR
2017, Tsukuba, Japan, November 22-24, 2017, Proceedings, A.
Kheddar, E. Yoshida, S. S. Ge et al., eds., pp. 345-354, Cham: Springer
International Publishing, 2017.
[28] M. A. Birkett, “The Trier Social Stress Test protocol for inducing
psychological stress,” Journal of visualized experiments: JoVE, no. 56,
2011.
[29] J. D. Creswell, W. T. Welch, S. E. Taylor, D. K. Sherman, T. L.
Gruenewald, and T. Mann, “Affirmation of personal values buffers
neuroendocrine and psychological stress responses,” Psychologicalscience, vol. 16, no. 11, pp. 846-851, 2005.
[30] J. Hellhammer, and M. Schubert, “The physiological response to Trier
Social Stress Test relates to subjective measures of stress during but not
before or after the test,” Psychoneuroendocrinology, vol. 37, no. 1, pp.
119-124, 2012.
[31] D. Hartanto, W.-P. Brinkman, I. L. Kampmann, N. Morina, P. G.
Emmelkamp, and M. A. Neerincx, “Home-Based Virtual Reality
Exposure Therapy with Virtual Health Agent Support,” in International
Symposium on Pervasive Computing Paradigms for Mental Health, pp.
85-98, 2015.
[32] G. Pioggia, M. Sica, M. Ferro, R. Igliozzi, F. Muratori, A. Ahluwalia,
and D. De Rossi, “Human-robot interaction in autism: FACE, anandroid-based social therapy,” in Robot and Human interactive
Communication, 2007. RO-MAN 2007. The 16th IEEE International
Symposium on, pp. 605-612, 2007.
[33] G. Pioggia, R. Igliozzi, M. L. Sica, M. Ferro, F. Muratori, A. Ahluwalia,
and D. De Rossi, “Exploring emotional and imitational android-based
interactions in autistic spectrum disorders,” Journal of CyberTherapy &Rehabilitation, vol. 1, no. 1, pp. 49-61, 2008.
[34] D. DeVault, R. Artstein, G. Benn, T. Dey, E. Fast, A. Gainer, K.
Georgila, J. Gratch, A. Hartholt, M. Lhommet, G. Lucas, S. Marsella, F.
Morbini, A. Nazarian, S. Scherer, G. Stratou, A. Suri, D. Traum, R.
Wood, Y. Xu, A. Rizzo, and L.-P. Morency, “SimSensei kiosk: a virtualhuman interviewer for healthcare decision support,” in Proceedings ofthe 2014 international conference on Autonomous agents and multiagent systems, Paris, France, pp. 1061-1068, 2014.
[35] M. L. Tielman, M. A. Neerincx, R. Bidarra, B. Kybartas, and W.-P.
Brinkman, “A Therapy System for Post-Traumatic Stress Disorder
Using a Virtual Agent and Virtual Storytelling to Reconstruct Traumatic
Memories,” Journal of Medical Systems, vol. 41, no. 8, pp. 125, 2017.
[36] K. Tai, X. Zheng, and J. Narayanan, “Touching a teddy bear mitigates
negative effects of social exclusion to increase prosocial behavior,”

Social Psychological and Personality Science, pp. 1948550611404707,
2011.