Hello everyone. Today I will present our work about
linking audience physiology to choreography.
We will present our interactive performance in collaboration
with professional dance sitter
to break the invisible hello everyone.
Today I will present our work about
linking audience physiology to choreography.
We will present our interactive
performance in collaboration with professional doctor session house
to break the invisible wall between audience and performer.
From research perspective,
we adopted physiological sensing as a way to explore the relationship between mind
and body, invisible, interstates and external visible cues.
Sensing technologies have been applied in real performances and live events
to create a connection between performers and audience and stage design.
Physiological sensing technology can also
help researchers to understand lab audience experience.
Performance events could be reflected as
physiological changes and psychophysiological engagement.
In our work we collected audience
electrodermal activity and heart rate activity during the dance performance
and be used to trigger changes in staging elements such as projections and lighting.
We also recorded audience data
and analyzed afterwards to understand lab audience experience.
Now let's see a short video to know about our work directly.
Due to the time limit,
I will just apply for 1 minute and you can find the full video from the YouTube.
To better understand our work,
we will illustrate from these three aspects interdisciplinary collaboration,
interactive codesign, process and our multimodal sensing data set.
This is a graphical overview of our interdisciplinary collaboration process.
The design process was centered around the five main components shown in this graph.
Concept development, initial test performance, sensing,
feedback, design, the main performances and research insights.
The right circles highlight the common
goals between the right circles highlights the common
goals between two sides which are performances.
To fulfill our goals, we held regular meetings, discussions,
workshops and test performances over the course of three years.
During the iteration cycle, we have hold
two formal during the iteration cycle,
we have hold two formal performances where real audience attended.
As an initial use case for the work,
we created a trial of 15 minutes dance performance.
During this performance,
we tracked the audience heart rate, eye link and people dilation and tested
the sensing technologies by creating projections on the stage.
Building on this, we developed
building on this, we developed a full 17 minutes dance performance.
In our final performance,
there are six choreographic sections in the performance.
We may notice in the first half of the performance, the choreography,
dancers, movement and stage designs are more interactive with strong reasons.
Then in the Romeo scene,
one audience member was invited to the stage to dance together.
Well after that, the girls section became
more quiet and meditative trying to invoke audience reflection.
The choreographic characteristics also reflect in the physiological data set we
collected from the audience, which I will going to talk about.
Now let's see the data set I mentioned.
Especially we recorded elect.
Now let's see the data set I mentioned.
Specifically, we recorded electrodomal
activity, blood volume, pots risk acceleration and angular velocity.
The analysis was conducting mainly on the EDA and BvP data.
This is an example of my audience members EDA data.
We mainly focus on the drastic changes
in EDA data and relocating the changes as EDA tremor.
Here is an example of the one audience members BvP data.
We extracted four Hrv features from BvP
data and finally narrowed down to PN 50 and rfhf ratio.
We calculated average values and variance
among the audience members and mapped the trends to the choreography.
Xaxis is the timeline of the dance performance.
Y axis are the values for all four
features PM 50, rfhf ratio, EDA difference and EDA extreme account.
Now let's focus on the bar chart and the blue line chart.
The bar chart is number of audience who experience drastic EDA changes.
This aggregated value could be interpreted as how many audience have similar arouse,
excited or anxious feelings within 1 minute.
We can see when the music of Bolivo reached the peaks with strong rhythms.
Similarly, when dancers invited
an audience member as Romeo to the stage, all the audience suddenly became anxious,
wondering whether they would be invited or not.
Then let's also see the blue line chart.
Blue line chart shows the average and the variance of PM 50.
PM 50 is a heart rate of a variability
feature that is closely related to rest and digest.
Higher PN 50 value could be interpreted as more relaxation.
As I also mentioned when describing the choreography,
after the Romeo back to the seat, the choreography changed from tension
to relaxation, aiming to involve the audience to reflect meditate.
Internally, we could see the blue line
chart which is the PM 50 rest up at this time.
Actually, there were three performances following the same choreography.
Well, different audience members attended and we found this kind of link between
audience physiology and choreography exists over all three performances.
Besides analysis and findings from the collective data set,
we also collected subjective feedback from the audience and five dancers.
Audience subjective feedback was collected
from open ended questions in online questionnaires and they mentioned they
view participation in the performance with this kind of feedback loop and they
sense a kind of unity within this performance.
We also organized the semi structured
focus groups for five dancers to learn more about their experience.
During the feedback loop dancers mentioned
while dancing, they focus mostly on the dance moment itself.
While waiting for the turns,
they had more time to enjoy the visuals and found very exciting.
Sometimes they could be aware of the color
changing and adjusted their dance movement accordingly.
As the implications for researchers
and practitioners in HCI field, we explore the potential of physiological
sensing in live and in the world scenario collaborations.
In the springdesigned, live events may contribute a relatively
controlled condition and the mapping between the audience physiological trends
and choreography could provide a holistic view.
To understand live audience for performance artists,
we extracted hre and EDA features that might be used in the future.
For example, PM 50 in particular is a robust measure for visualizing
a sustained change in engagement or a shift from tension to relaxation.
EDA features are more sensitive to short, and sudden changes in arousal lack shock
effects, as suggested by the choreographer and director.
Future focal points could be trying out
different forms of aesthetic interactions and creating an environment that could
help enhance the audience comfort inside the thank you for listening.
That's all for our presentation.