My name is Clara Maj and on behalf of
my coauthors Professor Youngjun Cho and Katie
Wang at University College London,
I will be presenting our paper shared
user interfaces with physiological data,
a systematic review of social
biofeedback systems and contexts in HCI.
Here, let me first talk about the
basic pipeline of physiological
computing which has been explored in
human computer interaction contexts.
Physiological computing as an innovative
approach to HCI always starts with
sensing human physiological signals
like heart and breathing rate.
This data is then processed often
with machine learning to analyze and
infer what psycho physiological States
and emotions a user is experiencing.
This physiological information is
then used to generate an appropriate
system response.
This system adaptation works in
a bio cybernetic loop.
They captured physiological information,
can also be sent back to the user
for more direct interventions.
This process is called biofeedback,
where continuous physiological data
is returned to a user in real time
and in an easily understandable form.
Biofeedback has long been explored
to train individuals to control their
Physiology as a way of enhancing
self awareness and regulation.
From here our question arose provided
the biofeedback can help users
understand there in psychophysiology,
can sharing biofeedback interpersonally.
To help promote and understanding of
others and if So what socio emotional
and well being benefits can this bring.
Although human Physiology is inherently
reactive to our social environment,
for the most part,
physiological signals do not constitute
an explicit channel of information
that humans use to interpret
other people's emotional states.
But with biofeedback,
we're now at a stage where it's
possible to externalize such data
and see explicitly how others around
us are feeling recent research
into this rapidly emerging topic.
We call social.
Biofeedback has suggested that
as physiological information
carries socio emotional meaning,
sharing biofeedback in itself.
Constitutes a novel communication medium.
Researchers have found this can
be leveraged for collaboration,
educational outcomes, and social play,
and even to enhance feelings of
social closeness and intimacy.
Creating positive user experiences to
further explore the current landscape
of social biofeedback technologies,
we wanted to understand what
types of interactions occur in
physiological social space,
and if biofeedback sharing
is a communication medium,
in what context does this
communication currently take place?
And with whom?
Secondly,
beyond transitory user experiences.
What kind of socio emotional competencies
can be practiced and or developed by
sharing displays of physiological data?
To answer our research questions,
we conducted a systematic
review and meta analysis.
We identified and screened research
articles following the prisoner guidelines.
We were interested in research
papers which included at least
one defined indirect physiological
measure involved human participants,
and described an experiment,
prototype or system involving a
minimum of two interactional partners.
After the application of further
eligibility criteria,
we assessed all papers for quality
prior to data extraction.
The number of studies on social biofeedback
sharing has gradually increased,
particularly since 2013.
Our Nalysis revealed a range
of applications
for social by feedback,
the most common being social
play from virtual reality to
tabletop and outdoor gameplay,
as well as Tele social tools from
nonverbal communication to instant
messaging and video chatting.
Most social biofeedback systems
use cardiovascular metrics.
There was a significant range in how
physiological data is fed back to
users by feedback was mostly presented.
Visually, but increasingly in
more implicit ways, for example,
through avatars changing their
characteristics as a function
of physiological activity.
Here we highlight some of our key findings,
particularly from our analysis based
on the timespace taxonomy of groupware,
which categorizes interaction
types into 4 dimensions,
along with spatiality and temporality.
Interestingly,
our analysis shows the majority of
current social biofeedback systems are
designed for use in distributed contexts.
Examples include Tele social
tools like in Nemo,
which allow users to send mood
representations derived from heart
rate data via a smart watch or the
dynacom virtual environment for dyadic
meditation based on respiration and EG.
In distributed contacts,
social biofeedback is used for
interpersonal connection and to
enhance feelings of social presence.
Most social biofeedback systems are also
designed for use in synchronous time,
which is particularly important for
overcoming physical distance during
social activities from games to
meditation and distributed exercise.
In synchronous interactions that are
either Co located and distributed,
we found social by feedback has
more of an informational role.
It is used to inform Co users
of each other's real time states
in the context of a social task.
Building.
On these findings,
we began articulating the
physiological social interaction space.
We identified 5 modes of interaction which
can be categorized according to symmetry.
Here,
with symmetry we refer to interactions
where in physiological data is
obtained from more than one user.
Asymmetrical interactions essentially
follow a participant observer model.
Here we distinguish between interactions
where end users physiological data
is only accessed by secondary user
and those in which both primary
and secondary users have access.
In contrast,
symmetrical interactions are those
in which Co users all have access
to biofeedback.
The differentiation here lies in the
content of the biofeedback received,
specifically,
whether Co users received
biofeedback from only their Co user.
Both their Co user and themselves.
Or whether they receive an aggregated
measure of their combined Physiology.
This is all combined in the
social biofeedback interactions
framework that we propose.
This framework shows the basic
physiological data flows in multi user
interactions around individual and
collective physiological display interfaces.
It also considers behavioral
forms of feedback between users.
Here we discuss six key socio emotional
benefits from social biofeedback
interaction systems which we have
identified through thematic analysis.
The first two are intrapersonal
socio emotional competencies
which are aligned with the main
intended outcomes of biofeedback.
Most importantly,
social biofeedback extends these
benefits to the point where users can
practice socioemotional skills oriented
towards others and towards tasks.
Empathy arose from shared positive
emotions and synchronization
of physiological activity.
Users were also able to accurately
infer and try to
understand causes for others internal states.
Users also acted compassionately,
explicitly validating the thoughts and
feelings of others and tailoring behavior
to make another more comfortable.
Thirdly, interacting with social
biofeedback sparked new connections
with strangers and encouraged emotional
disclosure in close relations.
Finally, when social biofeedback is
used for information in task scenarios,
this increases engagement,
group effort and performance.
Ethical considerations and mitigations
should guide future research in this area.
The intimate nature of sharing buy
signals can create privacy concerns,
especially when users don't feel they
have control over their own data.
We suggest aggregating or abstracting data
and affording user control where possible.
We also found receiving continuous
feedback can encourage too much
checking up on one another.
We need to consider the potential
for attachment issues to develop.
Finally,
we need to examine how to measure changes
in socio emotional skills and test overtime.
Whether practice can lead to long
term skills development and well
being improvement.
This is the end of our presentation.
Please check out the paper for more details.
Thank you for listening.