Heart Rate Monitoring as an Easy Way to Increase Engagement in
Human-Agent Interaction
Jérémy Frey
Univ. Bordeaux, LaBRI, UMR 5800, F-33400 Talence, France
CNRS, LaBRI, UMR 5800, F-33400 Talence, France
INRIA, F-33400 Talence, France
Heart Rate, Human-Agent Interaction, Similarity-attraction, Engagement, Social Presence.
Physiological sensors are gaining the attention of manufacturers and users. As denoted by devices such as
smartwatches or the newly released Kinect 2 which can covertly measure heartbeats or by the popularity
of smartphone apps that track heart rate during fitness activities. Soon, physiological monitoring could become
widely accessible and transparent to users. We demonstrate how one could take advantage of this situation
to increase users’ engagement and enhance user experience in human-agent interaction. We created an ex-
perimental protocol involving embodied agents “virtual avatars”. Those agents were displayed alongside a
beating heart. We compared a condition in which this feedback was simply duplicating the heart rates of users
to another condition in which it was set to an average heart rate. Results suggest a superior social presence of
agents when they display feedback similar to users’ internal state. This physiological “similarity-attraction”
effect may lead, with little effort, to a better acceptance of agents and robots by the general public.
Covert sensing of users’ physiological state is likely
to open new communication channels between human
and computers. When anthropomorphic characteris-
tics are involved – as with embodied agents – mirror-
ing such physiological cues could guide users’ prefer-
ences in a cheap yet effective manner.
One aspect of human-computer interaction (HCI),
albeit difficult to account for, lies in users’ engage-
ment. Engagement may be seen as a way to increase
performance, as in the definition given by (Matthews
et al., 2002) for task engagement: an “effortful striv-
ing towards task goals”. In a broader acceptation, the
notion of engagement is also related to fun and ac-
counts for the overall user experience (Mandryk et al.,
2006). Several HCI components can be tuned to im-
prove engagement. For example, content and chal-
lenge need to be adapted and renewed to avoid bore-
dom and maintain users in a state of flow (Berta et al.,
2013). It is also possible to study interfaces: (Kar-
lesky and Isbister, 2014) use tangible interactions in
surrounding space to spur engagement and creativity.
When the interaction encompasses embodied agents
either physically (i.e., robots) or not (on-screen
avatars) then anthropomorphic characteristics can
be involved to seek better human-agent connections.
Following the affective computing outbreak (Pi-
card, 1995), studies using agents that possess human
features in order to respond to users with the ap-
propriate emotions and behaviors began to emerge.
(Prendinger et al., 2004) created an “empathic” agent
that serves as a companion during a job interview.
While playing on empathy to engage users more
deeply into the simulation was conclusive, the dif-
ficulty lies in the accurate recognition of emotions.
Even using physiological sensors, as did the authors
with galvanic skin response and electromyography,
no signal processing could yet reach an accuracy of
100%, even on a reduced set of emotions see (Lisetti
and Nasoz, 2004) for a review.
Humans are difficult to comprehend for computers
and, still, humans are more attracted to others hu-
man or machine that match their personalities (Lee
and Nass, 2003). This finding is called “similarity-
attraction” in (Lee and Nass, 2003) and was tested by
the authors by matching the parameters of a synthe-
sized speech (e.g., paralinguistic cues) to users, when-
ever they were introverted or extroverted. An analo-
gous effect on social presence and engagement in HCI
has been described as well in (Reidsma et al., 2010),
this time under the name of “synchrony” and focusing
Frey J..
Heart Rate Monitoring as an Easy Way to Increase Engagement in Human-Agent Interaction.
DOI: 10.5220/0005226101290136
In Proceedings of the 2nd International Conference on Physiological Computing Systems (PhyCS-2015), pages 129-136
ISBN: 978-989-758-085-7
2015 SCITEPRESS (Science and Technology Publications, Lda.)
on nonverbal cues (e.g., gestures, choice of vocab-
ulary, timing, . . . ). Unfortunately, being somewhat
linked to a theory of mind, such improvements lean
against tedious measures, for instance psychological
tests or recordings of users’ behaviors. What if the
similarity-attraction could be effective with cues that
are much simpler and easier to set up?
Indeed, at a lower level of information, (Slovák
et al., 2012) studied how the display of heart rate (HR)
could impact social presence during human-human
interaction. They showed that, without any further
processing than the computation of an average heart-
beat, users did report in various contexts being closer
or more connected to the person with whom they
shared their HR. We wondered if a similar effect could
be obtained between a human and a machine. More-
over, we anticipated the rise of devices that could
covertly measure physiological signals, such as the
Kinect 2, which can use its cameras (color and in-
frared) to compute users’ HRs the use of video
feeds to perform volumetric measurements of organs
is dubbed as “photoplethysmography” (Kranjec et al.,
Consequently, we extended on the theory and we
hypothesized that users would feel more connected
toward an embodied agent if it displays a heart
rate similar to theirs, even if users do not realize
that their own heart rates are being monitored.
By relying on a simple mirroring of users’ phys-
iology, we elude the need to test users’ personality
(Lee and Nass, 2003) or to process and eventu-
ally fail to recognize – their internal state (Prendinger
et al., 2004). Creating agents too much alike humans
may provoke rejection and deter engagement due to
the uncanny valley effect (MacDorman, 2005). Since
we do not emphasize the link between users’ physi-
ological cues and the feedback given by agents, we
hope to prevent such negative effect. The similary-
attraction applied to physiological data should work at
an almost subconscious level. Furthermore, implicit
feedback makes it easier to improve an existing HCI.
As a matter of fact, only the feedback associated with
the agent has to be added to the application; feedback
that can then take a less anthropocentric form e.g.,
see (Harrison et al., 2012) for the multiple meanings a
blinking light can convey and (Huppi et al., 2003) for
a use case with breathing-like features. Ultimately,
our hypothesis proved robust, it could benefit to virtu-
ally any human-agent interaction, augmenting agent’s
social presence, engaging users.
The following sections describe an experimen-
tal setup involving embodied agents that compares
two within-subject conditions: one condition during
which agents display heartbeats replicating the HR of
the users, and a second condition during which the
displayed heartbeats are not linked to users. Our main
contribution is to show first evidence that displaying
identical heart rates makes users more engaged to-
ward agents.
The main task of our HCI consisted in listening to
embodied agents while they were speaking aloud sen-
tences extracted from a text corpus, as inspired by
(Lee and Nass, 2003). When an agent was on-screen,
a beating heart was displayed below it and an au-
dio recording of a heart pulse was played along each
(fake) beat. This feedback constituted our first within-
subject factor: either the displayed HR was identical
to the one of the subject (“human” condition), either
it was set at an average HR (“medium” condition).
The HR in the “medium” condition was ranging from
66 to 74 BPM (beats per minute), which is the grand
average for our studied population (Agelink et al.,
Agents possessed some random parameters: their
gender (male or female), their appearance (6 faces of
different ethnic groups for each gender), their voice
(2 voices for each gender) and the voice pitch. Those
various parameters aimed at concealing the true inde-
pendent variable. Had we chosen a unique appearance
for all the agents, subjects could have sought what
was differentiating them. By individualizing agents
we prevented subjects to discover that ultimately we
manipulated the HR feedback. To make agents look
more alive, their eyes were sporadically blinking and
their mouths were animated while the text-to-speech
system was playing.
In order to elicit bodily reactions, we chose sen-
tences for which a particular valence has been as-
sociated with, and, as such, that could span a wide
range of emotions. Valence relates to the hedonic tone
and varies from negative (e.g., sad) to positive (e.g.,
happy) emotions (Picard, 1995). HR has a tendency
to increase when one is experiencing extreme pleas-
antness, and to decrease when experiencing unpleas-
antness (Winton et al., 1984).
Our experiment was split in two parts (second
within-subject factor). During the first session, called
“disruptive” session (see Figure 1), subjects had to
rate each sentence they heard on a 7-point Likert scale
according to valence they perceived (very unpleasant
to very pleasant). Sentences came from newspapers.
A valence (negative, neutral or positive) was ran-
domly chosen every 2 sentences. Every 4 sentences,
subjects had to rate the social presence of the agent.
Figure 1: Procedure during the “disruptive” session: sub-
jects rate the valence of each one of the sentences spoken
by an agent. After 4 sentences, they rate agent’s social pres-
ence (3 items). Then a new agent appears. 20 agents, aver-
age time per agent 62.2s.
Then a new randomly generated agent appeared, for
a total of 20 agents, 10 for each “human”/“medium”
As opposed to the first part, during the second part
of the experiment, called “involving” session, sen-
tences order was sequential (see Figure 2). Agents
were in turns narrating a fairy tale. Subjects did not
have to rate each sentence’s valence, instead they only
rated the social presence of the agents. To match the
length of the story, agents were shuffled every 6 sen-
tences and there were 23 agents in total, 12 for the
“human” condition, 11 for the “medium” condition.
Because of its distracting task and the nature of
its sentences, the first part was more likely to dis-
rupt human-agent connection; while the second part
was more likely to involve subjects. This let us test
the influence of the relation between users and agents
on the perception of HR feedback. We chose not to
randomize sessions order because we estimated that
putting the “disruptive” session last would have made
the overall experiment too fatiguing for subjects. A
higher level of vigilance was necessary to sustain
its distracting task and series of unrelated sentences.
Subjects’ cognitive resources were probably higher at
the beginning of the experiment.
We created a 2 (HR feedback: “human” vs
“medium” condition) x 2 (nature of the task: “dis-
ruptive” vs “involving” session) within-subject ex-
perimental plan. Hence our two hypothesis. H1:
Hear rate feedback replicating users’ physiology in-
creases the social presence of agents. H2: This effect
is more pronounced during an interaction involving
more deeply agents.
Figure 2: Procedure during the “involving” session: sub-
jects rate agent’s social presence after it recited all its sen-
tences. Then a new agent appears, continuing the tale. 23
agents, average time per agent 46.6s.
2.1 Technical Description
Most of the elements we describe in this section,
hardware or software, come from open source move-
ments, for which we are grateful. Authors would also
like to thank the artist who made freely available the
graphics on which agents are based
. All code and
materials related to the study are freely available at
2.1.1 Hardware
We chose to use a BVP (blood volume pulse) sen-
sor to measure HR, employing the open hardware
Pulse Sensor
(see Figure 3 for a closeup). It as-
sesses blood flow variations by emitting a light onto
the skin and measuring back how fluctuates the inten-
sity of the reflected light thanks to an ambient light
photo sensor. Each heartbeat produces a characteris-
tic signal. This technology is cheap and easy to im-
plement. While it is less accurate than electrocardio-
graphy (ECG) recordings, we found the HR measures
to be reliable enough for our purpose. Compared to
ECG, BVP sensors are less intrusive and quicker to
install – i,e,. one sensor around a finger or on an ear-
lobe instead of 2 or 3 electrodes on the chest. In addi-
tion, as far as general knowledge is concerned, BVP
sensors are less likely to point out the exact nature of
their measures. This “fuzziness” is important for our
experimental protocol, as we want to be as close as
possible to the real-life scenarios we foresee with de-
vices such as the Kinect 2, where HR recordings will
be transparent to users.
The BVP sensor was connected to an Arduino
(see Figure 3). Arduino boards have become
a well-established platform for electrical engineering.
The Due model comes forward due to its 12 bits res-
olution for operating analog sensors. The program
uploaded into the Arduino Due was feeding the se-
rial port with BVP values every 2ms, thus achieving a
500Hz sampling rate.
Figure 3: BVP (blood volume pulse) sensor measuring
heartbeats, connected to an Arduino Due.
Two computers were used. One, a 14 inches
screen laptop, was dedicated to the subject and ran
the human-agent interaction. This computer was also
plugged to the Arduino board to accommodate sen-
sor’s cable length. A second laptop was used by the
experimenter to monitor the experiment and to detect
heartbeats. Computers were connected through an
ethernet cable (network latency was inferior to 1ms).
2.1.2 Software and Signal Processing
Computers were running Kubuntu 13.10 operating
system. The software on the client side was pro-
grammed with Processing framework
, version 2.2.1.
Data acquired from the BVP sensor was streamed to
the local network with ser2sock
. This serial port-to-
TCP bridge software allowed us to reliably process
and record data on our second computer. OpenViBE
(Renard et al., 2010) version 0.18 was running on the
experimenter’s computer to process BVP.
Within OpenViBE the BVP values were interpo-
lated from 500 to 512Hz to ease computations. The
script which received values from TCP was downsam-
pling or oversampling packets’ content to ensure syn-
chronization and decrease the risk of distorted signals
due to network or computing latency. A 3Hz low-
pass filter was applied to the acquired data in order
to eliminate artifacts. Then a derivative was com-
puted. Since a heartbeat provokes a sudden variation
of blood flow, a pulsation was detected when the sig-
nal exceeded a certain threshold. This threshold was
set during installation: values too low could produce
false positives due to remaining noise, and values too
high could skip heartbeats. Eventually a message was
sent. See figure 4 for an overview of the signal pro-
Figure 4: Signal processing of the BVP sensor with Open-
ViBE. A low-pass filtered and a first-derivative are used to
detect heartbeats.
Once the main program received a pulse message,
it computed the HR from the delay between two beats.
This value was passed over the engine handling the
HR feedback during the “human” condition. We pur-
posely created an indirection here using BPM values
in separate handlers instead of triggering a feedback
pulse as soon as a heartbeat was detected in order
to suit our experimental protocol to devices that could
only average HR over a longer time window (e.g., fit-
ness HR monitor belts). It should be easier to replicate
our results without the need to synchronize precisely
feedback pulses with actual heartbeats.
The TTS (text-to-speech) system comprised two
applications. eSpeak
was used to transform textual
sentences into phonemes and MBROLA
to synthe-
size phonemes and produce an actual voice. The TTS
speed was controlled by eSpeak (120 word per min-
utes), as well as the pitch (between 65 and 85, values
higher than the baseline of 50 to match the teenage
appearance of the agents). The four voices (2 male
and 2 female, “fr1” to “fr4”) were provided by the
MBROLA project. Sentences’ valence did not influ-
ence speech synthesis.
2.2 Text Corpuses
During the first part of the experiment (i.e., the
“disruptive” session) sentences were gathered from
archives of a french-speaking newspaper. These data
were collated by (Bestgen et al., 2004). Sentences
were anonymized, e.g., names of personalities were
replaced by generic first names. A panel of 10 judges
evaluated their emotional valence on a 7-point Lik-
ert scale. The final scores were produced by averag-
ing those 10 ratings. We split the sentences in three
categories: unpleasant (scores between [3; 1[, e.g.,
a suspect was arrested for murder), neutral (between
[1;1]) and pleasant (between ]1; 3], e.g., the national
sport team won a match) – see section 2.
The sentences of the second part (i.e., the “in-
volving” session) come from the TestAccord Emotion
database (Le Tallec et al., 2011). This database origi-
nates from a fairy tale for children see (Wright and
McCarthy, 2008) for an example of storytelling as an
incentive for empathy. We did not utilize per se the
associated valences (average of a 5-point Likert scale
across 27 judges for each sentence), but as an indi-
cator it did help us to ensure the wide variety of the
carried emotions. For instance, deaths or bonding mo-
ments are described during the course of the tale.
It is worth noting that when the valence of these
corpuses has been established, sentences were pre-
sented in their textual form, not through a TTS sys-
2.3 Procedure
The overall experiment took approximately 50 min-
utes per subject. 10 French speaking subjects partic-
ipated in the experiment; 5 males, 5 females, mean
age 30.3 (SD=8.2). The whole procedure comprised
the following steps:
1. Subjects were given an informed consent and a
demographic questionnaire. While they filled the
forms, the equipment was set up. Then we ex-
plained to them the procedure of the experiment.
We emphasized the importance of the distrac-
tion task (i.e., to rate sentences’ valence) and ex-
plained to the subjects that we were monitoring
their physiological state, without further detail
about the exact measures. 5 min.
2. The BVP sensor was placed on the earlobe op-
posite to the dominant hand, so as not to impede
mouse movements. Right after, the headset was
positioned. We ensured that subjects felt com-
fortable, in particular we checked that the headset
wasn’t putting pressure on the sensor. We started
to acquire BVP data and adjusted the heartbeat de-
tection. 2 min.
3. A training session took place. We started our pro-
gram with an alternate scenario, adjusting the au-
dio volume to subjects’ taste. Both parts of the
experiment occurred, but with only two agents
and with a dedicated set of sentences. This way
subjects were familiarized with the task and with
the agents i.e., with their general appearance
and with the TTS system. During this overview,
so as not to bias the experiment, “human” and
“medium” conditions were replaced with a “slow”
HR feedback (30 BPM) and a “fast” HR feedback
(120 BPM). Once subjects reported that they un-
derstood the procedure and were ready, we pro-
ceeded to the experiment. 5 min.
Figure 5: Our experimental setup. A BVP sensor connects
subject’s earlobe to the first laptop, where the human-agent
interaction takes place. Subject is wearing a headset to lis-
ten to the speech synthesis. A second laptop is used by the
experimenter to monitor heartbeats detection.
4. We ran the experiment, as previously described.
First the “disruptive” session (80 sentences, 20
agents, 22 min), then the “involving” session
(138 sentences, 23 agents, 17 min). We were
monitoring the data acquired from the BPV sen-
sor and silently adjusted the hearbeat detection
through OpenViBE if needed rarely, a big head
movement could slightly move the sensor and
modify signal amplitude. Figure 5 illustrates our
setup. 40 min.
The newspapers sentences being longer than the
ones forming the fairy tale, agents on-screen time var-
ied between both parts. Agents mean display time
during the first part was 62.2s, during the second part
it was 46.6s.
2.4 Measures
We computed a score of social presence for each
agent, averaged from the 7-point Likert scales ques-
tionnaires presented to the subjects before a new agent
were generated. This methodology was validated with
spoken dialogue systems by (Möller et al., 2007).
This score was composed of 3 items, consistent with
ITU guidelines (ITU, 2003). Translated to English,
the items were: “Do you consider that the agent is
pleasant?” (“very unpleasant” to “very pleasant”);
“Do you think it is friendly?” (“not at all” to “very
friendly”); “Did it seem ‘alive’?” (“not at all” to
“much alive”).
2.5 Results
We compared agents’ social presence scores between
the “human” and the “medium” conditions for each
part. Statistical analyses were performed with R
3.0.1. The different scores were comprised between
0 (negative) and 6 (positive), 3 corresponding to neu-
A Wilcoxon Signed-rank test showed a significant
difference (p < 0.05) during the “disruptive” session
(means 3.29 vs 2.91) but no significant difference (p =
0.77) during the “involving” session (means: 3.30 vs
3.34). H1 is verified while H2 cannot be verified. Be-
sides, when we analyzed further the data, we found no
significant effect (p = 0.27) of the “human”/“medium”
factor on the valence scores attributed to the sentences
during the “disruptive” session (means: 3.06 vs 2.91).
Subjects’ HRs were a little higher than expected
during the experiment: mean 74.73 BPM (SD =
5.59); to be compared with the average 70 BPM set in
the “medium” condition. We used Spearman’s rank
correlation test to check whenever this factor could
have influenced the results obtained in the “disrup-
tive” session. To do so, we compared subjects’ aver-
age HRs with the differences in social presence scores
between “human” and “medium” conditions. There
was not significant correlation (p = 0.25).
In the course of the “disruptive” session our main hy-
pothesis has been confirmed: users’ engagement to-
ward our HCI increased when agents provided feed-
back mirroring their physiological state. This result
could not be explained by a preference for a certain
pace of the HR feedback. For instance, even though
their HRs were higher than average, subjects did not
prefer agents of the “human” condition because of
faster heartbeats. Some of them did possess HRs
lower than 70 BPM. The only other explanation lies
in the difference of HR synchronization between “hu-
man” and “medium” conditions.
Beside agents’ social presence, similarity-
attraction effect may influence the general mood of
subjects, as they had a slight tendency to overrate
sentences valence during “human” condition. It is
interesting to note that while the increase in social
presence scores is not huge (+13%), it shifts the items
from slightly unpleasant to slightly pleasant.
Maybe the effect would have been greater in a less
artificial situation. Indeed, despite our experimental
protocol, subjects reported afterwards that the TTS
system was sometimes hard to comprehend, which
bothered them on some occasions. It may have re-
sulted in a task not involving enough for the subjects
to really “feel” the emotions carried by the sentences.
Several reasons could explain why the effect ap-
peared only during our “disruptive” session. During
the first session agents were displayed on a longer du-
ration (+33%) because of the longer sentences used
in the newspapers. The attraction toward a mirrored
feedback could take time to occur. In addition, be-
cause the task was less disruptive in the second ses-
sion, subjects were more likely to focus their atten-
tion on the content (i.e., the narrative) instead of the
interface (i.e., the feedback). This could explain why
they were less sensible to ambient cues. Subject were
less solicited during the “involving” session; we ob-
served that between agents questionnaires they often
removed their hands from the mouse, leaning back on
the chair. Lastly, the “involving” session systemati-
cally occurred in second position. Maybe the occur-
rence of the similarity-attraction effect is correlated to
the degree of users’ vigilance.
As for subjects’ awareness of the real goal of
the study, during informal discussions after the ex-
periments, most of them confirmed that they had no
knowledge about the kind of physiological trait the
sensor was recording, and none of them realized that
at some point they were exposed to their own HR.
This increases the resemblance of our installation
with a setup where HR sensing occurs covertly.
We demonstrated how displaying physiological sig-
nals close to users could impact positively social pres-
ence of embodied agents. This approach of “ambient”
feedback is easier to set up and less prone to errors
than feedback as explicit as facial expressions. It does
not require prior knowledge about users nor complex
computations. For practical reasons we limited our
study to a virtual agent. We believe the similarity-
attraction effect could be even more dramatic with
physically embodied agents, namely robots. That
said, other piece of hardware or components of an
HCI could benefit from such approach. While its ap-
pearance is not anthropomorphic, the robotic lamp
presented by (Gerlinghaus et al., 2012) behaves like
a sentient being. Augmenting it with physiological
feedback, moreover when correlated to users, is likely
to increase its presence.
Further research is of course mandatory to con-
firm and analyze how the similarity-attraction applies
to human-agent interaction and to physiological com-
puting. The kind of feedback given to users need to
be studied. Are both audio and visual cues necessary?
Does the look of the measured physiological signal
need to be obvious or could a heart pulse take the form
of a blinking light? In human-human interaction such
questions are more and more debated (Slovák et al.,
2012);(Walmink et al., 2014). Obviously, one should
check that a physiological feedback does not diminish
user experience. (Lee et al., 2014) suggest it is not the
case, but the comparison should be made again with
human-agent interaction.
Various parameters in human-agent interaction
need to be examined to shape the limits of the
similarity-attraction effect: exposure time to agents,
nature of the task, involvement of users, and so on.
Especially, we suspect the relation between human
and agent to be an important factor. Gaming settings
are good opportunities to try collaboration or antag-
onism. Concerning users, some will perceive differ-
ently the physiological feedback. As a matter of fact,
interoception the awareness of internal body states
varies from person to person and affects how we
feel toward others (Fukushima et al., 2011). It will
be beneficial to record finely users reactions, maybe
by using the very same physiological sensors (Becker
and Prendinger, 2005).
Finally, our findings should be replicated with
other hardware. We used lightweight equipment to
monitor HR, yet devices such as the Kinect 2 if as
reliable as BVP or ECG sensors – will enable remote
sensing in the near future. But with the spread of de-
vices that sense users’ physiological states, it is essen-
tial not to forgo ethics.
Measuring physiological signals such as HR en-
ters the realm of privacy. Notably, physiological sen-
sors can make accessible to others data unknown to
self (Fairclough, 2014). Even though among a certain
population there is a trend toward the exposition of
private data, if no agreement is provided it is difficult
to avoid a violation of intimacy. Users may feel the
urge to publish online the performances associated to
their last run – including HR, as more and more prod-
ucts that monitor it for fitness’ sake are sold – but ex-
perimenters and developers have to remain cautious.
Physiological sensors are becoming cheaper and
smaller, and hardware manufacturers are increasingly
interested in embedding them in their products. With
sensors acceptance, smartwatches may tomorrow pro-
vide a wide range of continuous physiological data,
along with remote sensing through cameras. If users’
rights and privacy are protected, this could provide
a wide range of areas for investigating and putting
into practice the similarity-attraction effect. Heart
rate, galvanic skin response, breathing, eye blinks: we
“classify” events coming from the outside world and
it influences our physiology. An agent that seamlessly
reacts like us, based on the outputs we produce our-
selves, could drive users’ engagement.
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