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#rC3 - Boiling Mind

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    rC3 postroll music
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    Herald: Now, imagine a stage with an
    artist performing in front of a crowd.
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    Is there a way to measure and even quantify
    the shows impact on the spectators?
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    Kai Kunze is going to address this
    question in his talk Boiling Minds now.
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    Kai, up to you.
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    Kai: Thanks a lot for the introduction,
    but we have a short video. I hope
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    that can be played right now.
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    intense electronic staccato music
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    music shifts to include softer piano tones
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    music shifts again to include harp-like tones
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    music keeps gently shifting
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    longer drawn out, slowly decreasing pitch
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    shift towards slow, guitar-like sounds
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    with light crackling noises
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    music getting quieter, softer
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    and fades away
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    inaudible talking
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    Kai: So thanks a lot for the intro and
    this is the Boiling Mind talks or linking
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    physiology and choreography. I just started
    off with this short video, that could
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    give you an overview over the experience
    of this dance performance that we
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    staged in Tokyo beginning of the year,
    just before the lockdown, actually.
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    And the idea behind this was: we wanted to
    put the audience on a stage. So breaking the
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    fourth wall. Trying to use physiological
    sensing in the audience. And that change
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    then is reflected on stage over the
    projection, sound and also audio to
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    influence the dancers and performers and
    then, of course, feed them back again to
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    the audience. So creating an augmented
    feedback loop. In his talk today, I just
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    want to give you a small overview, a
    little bit about the motivation, why I
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    thought it's a nice topic for the remote
    experience from the Chaos Computer Club
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    and also a little bit more about the
    concept, the set up and the design
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    iterations, as well as the lessons
    learned. So for me to give this talk,
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    I thought it's a good way to exchange
    expertise and get a couple of people that
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    might be interested for the next
    iterations, because I think we are still
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    not done with this work. So it's still
    kind of work in progress. And also a way
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    to share data. So to do some explorative
    data analysis on the recorded performances
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    that we have. And then most important: I
    wanted to create a more creative way to
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    use physiological data and explore it,
    because also for me as a researcher
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    working on variable computing or activity
    recognition, often we just look into
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    recognizing or predicting certain motions
    or certain mental states.
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    And that kind of, at least for simple things,
    feeds back into this very - I think -
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    idiotic or stupid ideas of surveillance and
    applications cases and that.
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    So can we create more intuitive ways
    to use physiological data?
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    So from a concept perspective, I think the
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    video gave a good overview of what we
    tried to create. However,
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    what we did in 3 performances was: We used
    physiological sensors on all audience
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    members. So for us, it was important that
    we are not singling out individual people
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    to just get feedback from them, but have
    the whole response, the whole physiological
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    state of the audience as an input to the
    performance. In that case, we actually
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    used heart rate variability and also
    galvanic skin response as inputs.
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    And these inputs then changed the projection
    that you could see. The lights, especially
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    the intensity of the lights and also
    the sound. And that, again, then led to
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    changes in the dancing behavior of the
    performers.
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    For the sensing, we went with a variable
    set up,
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    so in this case a fully wireless wristband,
    because we wanted to do something that is
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    easy to wear and easy to put on and to put
    off. We had a couple of iterations on that
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    and we decided then for electrodermal
    activity and also heart activity
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    to sense, because there's some
    related work that link these sensors to
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    engagement stress and also excitement
    measures. And the question then was also
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    where to sense it first. We went with a
    couple of wrist bands and also kind of
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    commercial approaches or half-commercial
    approaches. However, the sensing quality
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    was just not good enough, especially from
    the wrist. You cannot really get a good
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    electrodermal activity, so galvanic skin
    response. It's more or less a sweat
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    sensor. So that means that you can detect
    if somebody is sweating and some of the
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    sweat is actually then related to a stress
    response. And in that case, there are a
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    couple of ways to measure that. So it
    could be on the lower part of your hand or
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    also on the fingers. These are usually the
    best positions. So we used the fingers.
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    Over the fingers we can also get heartrate
    activity. And in addition to that, there's
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    also a small motion sensor, so a gyro and an
    accelerometer in the wristband. We haven't
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    used that for the performance right now, but
    we still have the recordings also from the
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    audience for that. When I say we, I mean
    George especially and also Dingding,
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    2 researchers that work with me, did
    actually took care of the designs.
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    So then the question was also how to
    map it to the environment or the staging.
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    In this case, actually, this was done
    by a different team,
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    this was done by the embodied media team
    also at KMD.
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    So I know a little bit about it,
    but I'm definitely not an expert.
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    And for the initial design we
    thought we use the EDA for the movement
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    speed of the projection. So the EDA rate
    of change is matched to movement of these
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    blobs that you could see or also the meshs
    that you can see and the color represents
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    the heart rate. We went for the LFHF
    feature that's low frequency, high
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    frequency ratio and should give you,
    according to related work, some indication
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    about excitement. For the lights: the
    lights were also bound to the heart rate,
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    in this case, the beats per minute, and
    they were matched to intensity. So if the
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    beats per minute of the audience go
    collectively up, the light gets brighter,
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    otherwise, it's dimmer. For the audio: we
    had an audio designer that cared about
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    sounds and faded in and faded out specific
    sounds also related to the EDA to the
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    relative rate of change of the electro-
    dermal activity. All this happened while
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    the sensors were connected over sensing
    server in QT to touch designer software
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    that generated this type of projections.
    And also the music got fed into and that
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    was then controlling the feedback
    to the dancers. If you want to
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    have a bit more of detail, I uploaded the
    work in progress preprint paper, a draft
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    of an accepted TI paper. So in case you are
    interested in the mappings and the design
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    decisions for the projections, there is
    a little bit more information there.
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    I'm also happy later on to answer those
    questions. However, I will probably just
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    forward them to the designers, that worked
    on them. And then, for the overall
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    performance, what happened was, we started
    out with an explanation of the experience.
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    So it was already advertised as a performance
    that would take in electrodermal
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    activity and heartbeat activity.
    So, people that bought tickets or came to
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    the event already had a little bit of
    background information. We, of course,
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    made also sure that we explained at the
    beginning what type of sensing we will be
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    using. Also what the risks and problems
    with these type of sensors and data
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    collection is and then audience could decide,
    with informed consent, if they just want to
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    stream the data, don't want to do
    anything, or they want to stream and also
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    contribute the data anonymously to our
    research. And then when the performance
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    started, we had a couple of pieces and
    parts, that is something that you can see in
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    B, where we showed the live data feed from
    all of the audiences in individual tiles. We
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    had that in before just for debugging, but
    actually the audience liked that. And so
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    we made it a part of the performance, also
    deciding with the choreographers to
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    include that. And then for the rest, as
    you see in C, we have the individual
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    objects, these blob objects that move
    according to the EDA data and change colour
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    based on the heart rate information. So
    the low to high frequency. In B, you see
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    also these clouds. And yet similarly, the
    size is related to the heart rate data.
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    And the movement again is EDA. And there's
    also one scene in E where the dancers pick
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    one person in the audience and ask them to
    come on stage. And then we will display
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    that audience members data at large in the
    back of the projection. And for the rest,
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    again, we're using this excitement data
    from the heart rate and from the
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    electrodermal activity to change sizes and
    colours. So, to come up with this design, we
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    went the co-design route, discussing with
    the researchers, dancers, visual
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    designers, audio designers a couple of
    times. And actually that's also how I got
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    involved first, because the initial idea is
    also from Moe, the primary designer of this
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    piece, were to combine somehow perception
    and motion. And I worked a bit in research
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    with the eye tracking. So you see on the
    screen the pupil website eye tracker it is
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    and open source eye tracking solution and
    also EOG electro-oculography glasses, that
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    use the capacitance of your eyeballs to
    detect something. Rough about eye emotion.
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    And we thought at the beginning, we want
    to combine this, a person seeing the play
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    with the motions of the dancers and
    understand that better. So that's kind of
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    how it started. The second inspiration for
    this idea in the theatre came from a
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    visiting scholar, Jamie. Jamie Ward came
    over and his work with the flood theater
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    in London. That's an inclusive theatre
    that also does workshops or Shakespeare
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    workshops. And he did some sensing just
    with the accelerometers and gyroscopes or
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    inertial motion wristbands to detect
    interpersonal synchrony between
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    participants in these workshops. And then
    we thought, when he came over, we had a
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    small piece where we looked into this
    interpersonal synchrony again in face to
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    face communications. I mean, now we are
    remote and I'm just talking into a camera
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    and I cannot see anybody. But usually, if
    you would have a face to face conversation,
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    doesn't happen too often anymore,
    unfortunately. We would show some type of
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    synchronies or, you know, kind of eyeblink,
    head nod and so on would synchronize with
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    the other person, if you're talking to
    them. And we also showed, that in small
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    recordings also we showed that we
    can recognize this in a variable sensing
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    setup. So again, using some glasses and we
    thought, why don't we try to scale that
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    up? Why don't we try and see what happens
    in a theatre performance or in another
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    dance performance and see if we can
    recognize also some type of synchrony. And
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    with a couple of ideation sessions, a
    couple of also test performances, also
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    including dancers trying out glasses,
    trying out other headwear. And that was
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    not really possible to use for the dancers
    during the performance. We came up with an
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    initial prototype and that we tried out,
    so in, I think November 2018 or so, where
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    we used a couple of pupil-labs and also
    pupil-invisible. These are nicer eye tracking
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    glasses, they are optical eye tracking
    glasses, so they have small cameras in
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    them, distributed in the audience. A couple
    of those Yoji glasses, they have also
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    initial motion sensors in them. So
    accelerometer and gyroscope. And we had at
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    the time heart rate sensors. However, they
    were fixed and wired to the system. And
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    also the dancers wore some wristbands
    where we could record the motion data. And
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    then what we did in these cases, then we
    had projections on three frames on top
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    of the dancers. One was showing the blink
    and the headnod synchronization of the
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    audience. The other one showed heart rate
    and variability. And the third one just
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    showed raw feed from one of the eye
    trackers. And it looked more or less like
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    this. And from a technical perspective, we
    were surprised because it actually worked.
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    So we could stream around 10 glasses,
    three eye trackers and four, five, I think
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    heart rate sensors at the same time and the surfer
    worked. However, from an audience
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    perspective, a lot of the feedback was the
    audience didn't like that just some people
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    got singled out and got the device by
    themselves and others could not really
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    contribute and could not also see the
    data. And then also from a performance
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    perspective, the dancers didn't really
    like that they couldn't interact with the
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    data. The dance piece also in this case
    was pre-choreographed. So there was no
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    possibility for the dancers to really
    interact with the data. And then also,
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    again, from an esthetic perspective, we
    really didn't like that the screens were
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    on top because either you would
    concentrate on the screens or you would
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    concentrate on the dance performance. And
    you had to kind of make a decision also on
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    what type of visualization you would focus
    on. So overall, you know, kind of partly
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    okay, but still there were some troubles.
    So one was definitely we wanted to include
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    all of the audience. Meaning we wanted to
    have everybody participate. Then the
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    problem with that part was then also
    having enough eye trackers or having
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    enough head worn devices was an issue. In
    addition to that, you know, kind of, if
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    it's head worn some people might not like
    it. The pandemic hadn't started yet. When
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    we did the recordings, however, there was
    already the information, some information
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    about the virus going around. So we didn't
    really want as, putting everybody,
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    giving everybody some eyeglasses. So then
    we moved to the heart rate and, galvanic
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    skin response solution and the set up
    where the projection is now part of the
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    stage. So we used the two walls, but we
    also used, it's a little bit hard to see
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    in the images, but we also used the floor
    as another projection surface for the
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    dancers to interact with and the main
    interaction, actually came then over the
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    sound. So then moving over to the lessons
    learned. So what did we take away from
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    from that experience? And the first part
    was talking with the dancers and talking
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    with the audience often, if you saw,
    especially the more intricate, the more
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    abstract visualizations, it was sometimes
    hard to interpret also how their own data
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    would feed into that visualization. So,
    you know, kind of some audience members
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    mentioned to some point in time they were
    not sure if they're influencing anything
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    or if it had an effect on other parts,
    especially if they saw the live data. It
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    was kind of obvious. But for future work,
    we really want to play more with the
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    agency and also perceived agency of the
    audiences and the performers. And we also
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    really wonder how can we measure this type
    of feedback loops? Because now we have
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    these recordings. We looked also a little
    bit more into the data, but it's hard to
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    understand. Were we successful? I think in
    some extent maybe yes, because the
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    experience was fun. It was enjoyable. But
    on this level of, did we really create
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    feedback loops and how do you evaluate
    feedback loops, that's something that we
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    want to address in future work. On the
    other hand, what was surprising I
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    mentioned before the raw data was
    something that the dancers as well as the
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    audience really liked. And that was
    surprising for me because I thought we had
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    to hide that more or less. But we had it
    on. As I said, there's kind of a debug at
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    the beginning of some test screenings and
    audience members were interested in it and
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    could see and were talking about: "Oh, see
    your heart rate is going up or your EDA is
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    going up." And the dancers also like that.
    And we used that then in the performance
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    in the three performances that we then
    successfully made for especially scenes
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    where the dancers would interact directly
    with parts of the audience. At the
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    beginning of the play is a scene where the
    dancers give out business cards to some
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    audience members. And it was fun to see
    that some audience members could identify
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    themselves, other audience members would
    identify somebody else that was sitting
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    next to them. And then this member had a
    spike in EDA because of the surprise. So
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    there was really, you know, kind of some
    interaction going on. So maybe staying if
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    you're planning to do a similar event,
    staying close to the raw data and also low
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    latency. So I think it's quite important
    for some types of these interactions. From
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    the dancers there was a big interest, on
    the one side, they wanted to use the data
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    for reflection. So they really liked that
    they had the printouts of the effects of
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    the audience later on. However, they also
    wanted to dance more with biometric data
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    and also use that for their rehearsals
    more. So, of course, you know, we had to
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    co-design, so we worked directly. We
    showed the dancers the sensors and the
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    possibilities and then worked with them to
    figure out what can work and what cannot
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    work and what might have an effect, what
    might not have an effect. And then we did
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    some, as you saw, also some prototype
    screenings and also some internal
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    rehearsals where we used some recorded
    data. We used some, a couple of people of
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    us were sitting in the audience. We got a
    couple of other researchers and also
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    students involved to sit in the audience
    to stream data. And we also worked a
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    little bit with prerecorded experiences
    and also synthetic experiences, how we
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    envisioned that the data would move. But
    still, it was not enough in terms of
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    providing an intuitive way to understand
    what is going on, especially also for the
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    visualizations and the projections. They
    were harder to interpret than the sound in
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    the sound sphere. So and then the next and
    the biggest point, maybe as well is, the
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    sensors and the feature best practices. So
    we're still wondering, you know, what to
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    use. We're still searching. What kind of,
    sensing equipment can we use to relay
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    this, in this invisible link between
    audience and performers? How can we
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    augment that? We started out with the
    perception and eye tracking part, we then
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    went to wrist one device because it's
    easier to maintain and it's also wireless.
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    And it worked quite well to stream 50 to
    60 audience members for one of those
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    events to a wireless router and do the
    recording, as well as the life
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    visualization with it. However, the
    features might have not been.
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    Audio Failure
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    Okay. Sorry for the short part where it was
    offline. So, we were talking about a sense
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    of features and best practices. So in this
    case, we are still searching for the right
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    type of sensors and features to use for
    this type of audience, performer
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    interaction. And we were using, the, yeah,
    the low frequency, high frequency ratio of
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    the heart rate values and also the
    relative changes of the EDA. And that was
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    working, I would say not that well,
    compared to other features that we now
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    found while looking into the performances
    and the recorded data of the around, 98
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    participants that agreed to share the data
    with us, for these performances. And from
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    the preliminary analysis that Karen Han,
    one of our researchers working on and
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    looking into what type of features are
    indicative of changes in the performance.
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    It seems that a feature called PNN that's
    related to heart rate variability to the
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    R-R intervals is, seems to be quite good. And
    also the peak detection per minute using
  • 32:19 - 32:25
    the EDA data. So we're just counting the
    relative changes, the relative up and
  • 32:25 - 32:32
    down, for the EDA. If you're interested
    I'm happy to share the data with you. So
  • 32:32 - 32:38
    we have three performances each
    around an hour and 98 participants in
  • 32:38 - 32:46
    total. And we have the heart rate data,
    the EDA data, from the two fingers as well
  • 32:46 - 32:54
    as, the motion data as well. We haven't
    used the motion data at all except for
  • 32:54 - 33:00
    filtering out a little bit the EDA and
    heart rate data because if you're moving a
  • 33:00 - 33:07
    lot, you will have some errors and some
    problems, some motion artifacts in it. But
  • 33:07 - 33:15
    what do I mean with why is the PNN or why
    is the EDA peak detection so nice? Let's
  • 33:15 - 33:21
    look a little bit closer into the data.
    And here you see I just highlighted
  • 33:21 - 33:31
    performance three from the previous plots.
    You see PNN50 on the left side is the scale, the
  • 33:31 - 33:40
    blue line gives you the average of the
    PNN50 value. So this is the R-R interval
  • 33:40 - 33:48
    related heart rate variability feature and
    that feature is especially related to
  • 33:48 - 33:55
    relaxation and also to stress. So usually
    a lower PNN50 value means you're more
  • 33:55 - 34:01
    relaxed and a higher value means that
    you're. No, higher value means that you're
  • 34:01 - 34:08
    more relaxed, sorry. Lower value means
    that you are more stressed out. So what happens
  • 34:08 - 34:13
    now in the performance is something that
    fits very, very well and correlates with
  • 34:13 - 34:19
    the intention of the choreographer.
    Because the first half of the performance,
  • 34:19 - 34:27
    you see section one, two, three, four,
    five and six on the bottom. The first half
  • 34:27 - 34:32
    of the performance is to create a conflict
    in the audience and to stir them up a
  • 34:32 - 34:40
    little. So, for example, also the business
    card scene is part of that part, or also
  • 34:40 - 34:48
    the scene where somebody gets brought from
    the audience to the stage and joins the
  • 34:48 - 34:54
    performance is also part of that versus
    the latter part is more about reflection
  • 34:54 - 34:59
    and also relaxation. So taking in what you
    experienced at the first part, and that's
  • 34:59 - 35:04
    something that you see actually quite nice
    in the PNN50. So at the beginning it's
  • 35:04 - 35:10
    rather low. That means the audience is
    slightly tense versus in the latter part
  • 35:10 - 35:18
    they more relaxed. Similarly, the EDA in
    the bottom as a bar chart gives you an
  • 35:18 - 35:24
    indication of a lot of peaks happening at
    specific points. And these points
  • 35:24 - 35:31
    correlate very well to memorable scenes in
    the performance. So seeing the one scene,
  • 35:31 - 35:36
    where, actually section four, the red one,
    is the one where somebody from the
  • 35:36 - 35:42
    audience gets brought onto the stage.
    Where is this? I think around minute
  • 35:42 - 35:53
    twelve there is a scene where the dancers
    handout business cards. And that's
  • 35:53 - 35:56
    also something, I think. So it's
    promising, we're not there yet definitely
  • 35:56 - 36:02
    from the data analysis part, but there are
    some interesting things to see. And that
  • 36:02 - 36:11
    kind of brings me back to the starting
    point. So I think, it was an amazing
  • 36:11 - 36:16
    experience actually, working with a lot of
    talented people on that and the
  • 36:16 - 36:22
    performance was a lot of fun, but we are
    slowly moving towards putting the audience
  • 36:22 - 36:28
    on stage and trying to break the fourth
    wall, I think, with these type of setups.
  • 36:28 - 36:36
    And that leads me then also to the end of
    the talk where I just have to do a shout
  • 36:36 - 36:42
    out for the people who did the actual
    work. So all of the talented performers
  • 36:42 - 36:50
    and the project lead, especially Moe who
    organized and was also the link between
  • 36:50 - 36:56
    the artistic side and the dancers with
    Mademoiselle Cinema and us, as well as the
  • 36:56 - 37:05
    choreographer Ito-san. And yeah, I hope I
    didn't miss anybody. So that's it. So
  • 37:05 - 37:14
    thanks a lot for this opportunity to
    introduce this work to you. And now I'm
  • 37:14 - 37:21
    open for a couple of questions, remarks. I
    wanted to also host a self organized
  • 37:21 - 37:26
    session sometime. I haven't really gotten
    the link or anything, but I'll probably
  • 37:26 - 37:33
    just post something on Twitter or in one
    of the chats if you want to stay in
  • 37:33 - 37:39
    contact. I'll try to get two or three
    researchers also to join. I know George,
  • 37:39 - 37:44
    who was working on the hardware, and
    Karen, who worked on the visualizations,
  • 37:44 - 37:53
    the data analysis might be available. And
    if you interested in that, just send me an
  • 37:53 - 38:00
    email or check, maybe, I just also add it
    to the blog post or so if I get the link
  • 38:00 - 38:05
    later. So, yeah. Thanks a
    lot for the attention.
  • 38:09 - 38:17
    Herald: Thanks, Kai, for this nice talk.
    For the audience, please excuse us for the
  • 38:17 - 38:22
    small disruption of service we had here.
    We're a little bit late already, but I
  • 38:22 - 38:27
    think we still have time for a question or
    so. Unfortunately, I don't see anything
  • 38:27 - 38:32
    here online at the moment. So if
    somebody tried to pose a question and
  • 38:32 - 38:37
    there was also disruption of service, I
    apologize beforehand for that. On the
  • 38:37 - 38:43
    other hand now, Kai, you talked about data
    sharing. So how can the data be accessed?
  • 38:43 - 38:48
    Do people need to access you or drop to
    you a mail or personal message?
  • 38:48 - 38:54
    Kai: Yeah, we're on the,
    so right now, no publication is
  • 38:54 - 39:00
    still accepted and there's also some
    issues actually, a little bit of some
  • 39:00 - 39:03
    rights issues or so on. So the
    easiest part is just to send me a mail.
  • 39:03 - 39:14
    It will be posted sometime next year
    on a more public website. But the easiest
  • 39:14 - 39:20
    is just to post me a mail. There're already
    a couple of people working on it and we
  • 39:20 - 39:26
    have the rights to share it. It's just a little
    bit of a question of setting it up.
  • 39:26 - 39:32
    I wanted to have the website also online
    before the talk, but yeah, as with the
  • 39:32 - 39:35
    technical difficulties and so on, everything
    is a little bit harder this year.
  • 39:35 - 39:43
    Herald: Indeed. Indeed. Thanks,
    guys. Yes, I'd say that's it for this
  • 39:43 - 39:49
    session. Thank you very much again for
    your presentation. And I'll switch back to
  • 39:49 - 39:53
    the others.
  • 39:53 - 39:58
    postroll music
  • 39:58 - 40:33
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#rC3 - Boiling Mind
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