How Are We Connected?

Measuring Audience Galvanic Skin Response of Connected Performances

Chen Wang

, Xintong Zhu

, Erik Geelhoed

, Ian Biscoe

, Thomas Röggla

and Pablo Cesar

1,3

Centrum Wiskunde & Informatica, Science Park 123, Amsterdam, Netherlands

Falmouth University, Treliever Road, Penryn, U.K.

Delft University of Technology, Mekelweg 2, Delft, Netherlands

Keywords: Performing Arts, Audience Engagement, Galvanic Skin Response.

Abstract: Accurately measuring the audience response during a performance is a difficult task. This is particularly the

case for connected performances. In this paper, we staged a connected performance in which a remote

audience enjoyed the performance in real-time. Both objective (galvanic skin response and behaviours) and

subjective (interviews) responses from the live and remote audience members were recorded. To capture

galvanic skin response, a group of self-built sensors was used to record the electrical conductance of the skin.

The results of the measurements showed that both the live and the remote audience members had a similar

response to the connected performance even though more vivid artistic artefacts had a stronger effect on the

live audience. Some technical issues also influenced the experience of the remote audience. In conclusion we

found that the remoteness had little influence on the connected performance.

1 INTRODUCTION

One-Way delivery of live theatre performances to

cinemas or other theatres is a relatively recent

phenomenon, as well as still relatively small-scale.

However, it has already been a commercial success

for well-funded companies using expensive and not

readily available infrastructure (e.g. satellite

communication). For example, the National Theatre

in UK often applied NT Live technology to broadcast

live performances to digital cinemas (Bakhshi et al.,

2010). The long-term vision is that over the next few

years, smaller companies will follow suit to reach

wider audiences beyond their local community. In

addition, we foresee the development of the

technology to enable remote audiences to play a much

bigger role during live performances. Remote

audiences may interact with performers across space

and provide feedback, promoting a sense of audience

community on a larger scale.

A number of previous studies focused on how to

enable connected performances, and how to better

engage the audience (Sawchuk et al., 2003; Sheppard

et al., 2008; Yang et al., 2006). However, audience

response to connected performances has only been

investigated in a few papers. Our study was

conducted to better understand the effect of

remoteness on audience experience during a

connected theatre performance. This is a first step in

evaluating audience response to connected

performances. This paper aims to address the

following research question:

How does remote real-time watching compare to

being at a performance in person?

To answer this question, an experiment in highly

realistic conditions was conducted. Together with a

small theatre company, exploratory work was done

on synchronous watching (live streaming) of one

theatre play, which was called “Styx Boat on the

River”. It was staged at the University of Falmouth in

Falmouth, United Kingdom. The performance was

live streamed to another studio located at the same

building, which meant that the audiences at the two

locations watched the same performance at the same

time (Figure 1 and Figure 2). The experience of the

audience was captured by galvanic skin response

(GSR) sensors, video recordings, and interviews.

The remainder of this paper is structured as

follows: The next section is a review of recent

relevant research work, highlighting the novelty of

our contribution. Then, the methodology employed

during the experiments is described and the results are

analysed. A discussion concludes the paper.

Wang, C., Zhu, X., Geelhoed, E., Biscoe, I., RoÌ

Lggla, T. and Cesar, P.

How Are We Connected? - Measuring Audience Galvanic Skin Response of Connected Performances.

DOI: 10.5220/0005939100330042

In Proceedings of the 3rd International Conference on Physiological Computing Systems (PhyCS 2016), pages 33-42

ISBN: 978-989-758-197-7

2 RELATED WORK

Having an exhaustive overview of the previous

studies regarding audience experience of theatre

performances is beyond the scope of this paper; the

interested reader can consult the following surveys

(Bennett, 2013; Reason, 2010). A review of the most

relevant works is conducted in the following areas:

audience response in performing arts and

measurement of audience response.

2.1 Audience Response in Performing

Arts

In the broadest sense, audience response can be

considered as feedback to a stimulus coming from

several users, participants, or players (Mandryk, 2004;

Chanel et al., 2008; Lunn and Harper, 2010).

Different applications define audience response

depending on the requirements of the application.

Using an online environment as an example, O’Brian

and MacLean (2009) regarded audience response as

the perceived usability, aesthetics, focused attention

and involvement felt.

In other specific application areas like video

watching or theatre performances, audience affective

states or emotions were also used to define audience

response (Ruan et al., 2009; Sauro and Lewis, 2012).

According to most psychological models, affective

state or emotion includes two dimensions: valence

and arousal (Russell, 1980; Bradley et al., 1992;

Posner et al., 2005). Arousal has been commonly

used to represent audience experience during theatre

performance (Dmochowski et al., 2014; Latulipe et

al., 2011; Wang et al., 2014). For example, Latulipe

and her colleagues (2011) measured both the self-

reported arousal and the physiological arousal of an

audience member during a recorded performance.

They found that the audience self-reports were

positively correlated with the audience physiological

arousal.

However, previous studies about audience

response in the domain of performing arts have been

conducted at only one location. For example,

Radbourne et al., (2009) conducted a study to

investigate the differences in audience response

between a live music event and a theatre performance.

This study did not find any significant difference.

For connected performances, as mentioned above,

researchers focused on technical issues, like how to

support a connected performance (Yang et al., 2006;

Sheppard et al., 2008)) and performance issues, like

how to design a high quality connected performance

(Gonzalez et al., 2012). For example, Sheppard et al.,

(2008) and Yang et al., (2006) explored connected

dance using a 3D virtual room, where dancers could

interact with each other. Furthermore, in these

studies, even though audience response during the

performance was recorded, it was only a tool for

evaluating the quality of their technologies. So they

Figure 1: The performance: the two photos on the left show the remote location (the performance and the live audience were

both displayed on the big screens (top) and the remote audience actively interacted with the actor (bottom)); two photos on

the right were taken at the live location (the artist with special effect smoke (top) and the audience watching the play (bottom)).

PhyCS 2016 - 3rd International Conference on Physiological Computing Systems

Figure 2: Conceptual sketch of the experimental facilities at each location. Left: at the remote location, there was one screen

showing the performance from the live location in front of the audience members. Another two screens, which showed the

live audience members, were both on their left and right. One camera at the right of the audience was used to record them.

Right: at the live location, the actor was performing in front of the live audience. There was a camera in the back, which

recorded the performance. The projection of the remote audience was placed in a screen on the left of the live audience. The

camera recording the live audience was on the left side. This set up allowed that audience at both locations felt as if they were

in the same space.

focused on supporting the performers but not on

better understanding the audience response. The

current study instead intends to quantify the audience

response during the connected performance.

2.2 Measurement of Audience

Response

In the past, different mechanisms for quantifying

audience response have been employed (e.g., surveys,

real-time scaling system, and physiological

measurement).

Surveys are the most common method. For

example, Gonzalez et al., (2012) used surveys to

evaluate how audience responded to different

technology-oriented performances. However, this

method has some limitations. For instance, surveys

are subjective and the result of them can be easily

influenced by many other factors, like social pressure

and the bandwagon effect.

Besides surveys, Stevens et al., (2009) used a real-

time scaling system called “the portable Audience

Response Facility” (pARF) to measure audience

experience during a performance. There are three

drawbacks to this method: First, as with surveys, it is

a self-report, which is subjective. Furthermore, before

the real experiment starts, the participants have to be

trained to use the system to ensure that they can

respond using the least cognitive effort. The training

procedure is time consuming and inconvenient for

both participants and experimenters. Lastly, even

though the audience members are trained to use the

system, the real-time scaling system still interrupts

the audience during the performance.

In addition to these subjective tools, objective

methods, normally in the form of physiological

sensors, have also been used to measure the audience

response during a performance. For example, GSR

sensors, which measure the users’ electrical

conductance of the skin, have been proven to be a

valid approach for measuring audience engagement

(Picard, 1995). In 2014, Wang and her colleagues

conducted experiments in a real theatre studio using

GSR sensors. Clustering analysis showed that the

audience could be grouped into different engagement

levels. They validated that GSR is a valid proxy for

quantifying user experience.

Considering the advantages of GSR sensors, such

as being an objective and nonintrusive mechanism, in

the current study, GSR sensors were used to measure

the audience response. Additionally, interviews and

behavioural observations were also used for

analysing the data.

How Are We Connected? - Measuring Audience Galvanic Skin Response of Connected Performances

3 METHODOLOGY

3.1 Participants

All the participants were recruited at the university,

and they all were university staff without any visual

or acoustic problems. There were 12 audience

members in each location (24 participants in total).

3.2 Stimuli and Apparatus

3.2.1 Distributed Performance

The performance for this experiment was carried out

by a single actor. The play, called “Styx Boat on the

River”, was interactive including a number of pieces

like singing, effects using theatrical smoke and a

vacuum cleaner sound effect. The whole performance

lasted 25 minutes.

3.2.2 GSR Sensors

There are several commercial GSR sensors, e.g.,

BioNomadix Wireless Wearable Physiology from

BioPac Systems Inc., GSR 2™ from Thought

Technology Ltd., and Q sensors from Affectiva Inc.

However, these sensors use Bluetooth as

communication protocol, which makes them not

suitable for group experiments, where simultaneous

readings are needed. We thus decided to build our own

GSR sensor using a Jeenode board with a RF12

wireless module, a low pass filter, and several

accessories (Figure 3), such as a band to be worn on the

user’s palm, holding the electrodes. The wireless

function of the RF12 module makes it possible to run

user studies with a group of users at the same time,

which can be carried out during theatre performances.

The sensors have been validated through a number of

experiments (Wang et al., 2016). All the sensor slaves

simultaneously send packets back to the master sink

node, which is connected to a laptop. The master node

communicates with all the slave nodes by using a

polling mechanism. In a lab testing environment, each

slave sensor node generated 7 or 8 samples per second

(7 Hz or 8 Hz), but in reality the sample rate was

reduced to 4 Hz due to in-air collisions. Before we

used the sensors in the experiment, the effects of

noise in all the sensors were tested, and were

validated in different scenarios (i.e., video watching

or video game playing). In our case, our sensors are

resilient against noise because of the filter. In addition

to that, the sensor data distribution was also proved to

be in accordance with the typical characteristics of

GSR sensor data.

3.2.3 Interviews

Both, actors and audience members were interviewed

after the performance. The interview of audience

members mainly focused on three parts: the overall

evaluation of the performance and the reasons behind

their opinions, the closeness they felt to the actors,

and the closeness they felt to the audience at the other

location. The interview with the actors discussed the

overall evaluation of the performance and the reasons

behind their opinions, and how they felt with respect

to the audience.

3.2.4 Other Apparatus and Software

The performance was live streamed to another

performance studio located in the same building, which

meant that the audiences at the two locations watched

the same performance at the same time. The technical

research team developed the live streaming system.

There were three cameras deployed in total, so that the

remote audience could see the actor and the live

audience through three projector screens. At the live

venue, there were only two projector screens installed,

so that the actors could see the reaction of the remote

audience during the performance (Figure 2).

During the rehearsal, the latency of the live streaming

system was measured, to be around 150 milliseconds,

so that the audiences at the two locations could hardly

feel the influence of delay.

Figure 3: The GSR measuring system: (left) the front side of the sensor board; (middle) the sink node connected with a laptop;

(right) the complete sensor sets.

PhyCS 2016 - 3rd International Conference on Physiological Computing Systems

The software for controlling the cameras,

recording the data and networking was written in C

and Python. All the data analysis was done using

SPSS and Python.

3.3 Experimental Procedures

Before the experiment started, the participants filled

an informed consent form. Then oral instructions

were provided. After that, the audience members

from both locations attached the sensors to their non-

dominant palm. At the end of the play, there was a

small group interview at each location. Both the

audience behavioural response during the whole

performance and the performance were video

recorded in order to better recall the experiments

when analysing the sensor readings.

3.4 Data Analysis

To understand the audience members’ GSR response,

both the event-related skin conductance (SCR) and

the skin conductance level (SCL) were analysed.

Before that, the raw GSR data was processed by

averaging the results every second.

3.4.1 Data Analysis of Event-related SCR

There are several steps to analyse event-related SCR

data (Figure 4) based on Fleureau and his colleagues'

(2013) work.

Normally, when humans receive an engaging

stimulus, their GSR value will increase fast with a

latency of 1-3 seconds, and after reaching a maximum

value, it will recover to a value around the baseline.

In the algorithm we used, first, a 2Hz (

(t)) low-pass

filter was applied to remove noise, such as other

physiological signals and electrical noise. Then a

derivation (from

(t) to 



(t)) was applied to

calculate the rate of change of the GSR data. This way

we know if the GSR value is ascending (positive

values) or descending (negative values). After that,

only the positive values were kept, while the negative

ones were ignored (from 



(t) to 





(t)), which means

that we only focused on the increasing phases of the

GSR signal, because the negative phases only reflect

the recovery of the signal to the baseline. The steps

above helped us to extract the SCR data.

To temporally analyse emotional flow, we applied

an overlapping time moving window with a window

size of 30 samples (30 seconds), and an overlap of 15

samples (15 seconds). This step helped us to smooth

the data and remove the users’ GSR latency. So the

mean values of 





(t) were converted into

Gi

(1 ≤ i

≤ k, k is the number of the moving windows).

Gi

the mean derivative value of one subsample in one

specific moving window.

Figure 4: The description of the different steps of the

algorithm on the processing the raw GSR signals.

Since each individual may have a different

amplitude for the derivative GSR signal when

exposed to the same stimulus,

Gi

was divided by the

sum of the subsampled skin response values (Formula

(1)), and the output was 









(1 ≤ n ≤ N, N is the

number of the sample; 1 ≤ i ≤ k, k is the number of the

moving windows).





















∑













(1)











is the individual value in a moving window,

which cannot represent the whole group’s response,

because there is some individual, different from

person to person, noise (e.g. body movements). To

define whether the group had a significant arousal or

not, a statistical test called the bilateral Mann-

Whitney-Wilcoxon (MWW) test was used. This test

detects whether there is a significant difference

between the audience arousal response (









) and

the background noise. We took the lowest 10% of the

values in 









as background noise (Fleureau et al.,

2013). 









of a single time sample was compared to

the background noise of each time sample, which

means that we used MWW test to compare k times

and obtain k p-values for each time sample. The final

p-value of each time sample is the averaged value of

those k p-values. For final

-values lower than 5%,

we considered the response during that time sample

How Are We Connected? - Measuring Audience Galvanic Skin Response of Connected Performances

to be significantly different from the background

noise.

3.4.2 Data Analysis of SCL

The first sensor readings of each participant were

used as the baseline, which was then subtracted from

the raw data, to remove individual differences. Then,

the Pearson product-moment correlation coefficient

was used to check whether there was a significant

correlation between the responses from the audiences

at different locations.

In addition, a t-test was used to compare the SCL

data of live audience members and remote audience

members.

3.4.3 Data Analysis of Video Recordings

Several parameters (e.g., eye contact between the

actor and the audience, interactions between the

actors and the audience, laughter, smile, and applause)

from the video recordings were calculated by

inspecting the recordings (Roto et al., 2009).

4 RESULTS

4.1 Event-related SCR

The event-related SCR results, extracted from the 24

participants (two groups: 12 live audience members

and 12 remote audience members) during the whole

performance, are shown in Figur and Figure 6. In the

top graph of each figure, the blue columns represent

the average value of 









at time (











). The red

bar means that the p value in this moment is less than

0.05, i.e. significantly different from background

noise. The concept is mirrored in the bottom graph

where the p-value (blue line) goes below the critical

value (red line).

The algorithm detected a number of moments

where the event-related SCR signals were

significantly different from the background noise,

which means that the audience members were more

engaged. For example, the significantly different

audience SCR response can be seen during the

theatrical smoke effect in the graph of the live

audience. During the smoke effect, also the remote

audience was significantly engaged. The remote

audience members were more absorbed when the

Figure 5: The extracted SCR signals of the live audience members during the performance, where points 1, 2, 3, and 4 are the

significantly different SCR responses identified by the algorithm. In the top graph, the y-axis is the mean derivative value. In

the bottom graph, the y-axis is the mean p value of the bilateral MWW test. The x-axis of both two graphs is the time in

seconds. 1, 2, 3, and 4 are events performed were the live audience SCR response is significantly different from the

background noise. 1: the smoke event; 2 and 3: the interaction between the actor and the audience; 4: the actor is sitting in

the audience and talking.

PhyCS 2016 - 3rd International Conference on Physiological Computing Systems

Figure 6: The extracted SCR signals of the remote audience members during the performance, where 1, 2, 3, 4, 5, 6, 7, and 8

are the significantly different SCR response defined by the algorithm. The meaning of x-axis and y-axis is same as Figure 4.

1, 2, 3, 4, 5, 6, 7, and 8 are the events performed while the remote audience SCR response is significantly different from the

background noise. 1: the actor is talking with his arms hurling; 2: the actor is talking to the remote audience; 3: the actor is

singing; 4: the actor is preparing the microphone holder for singing; 5: there is some problems of the projector and the audience

members raised their hand; 6 and 7: the actor was singing in the smog effect with a vacuum sound; 8: the actor was sitting on

the floor and being silent.

actor was singing, while the live audience members

were more engaged during the interaction event. In

addition to that, it is interesting to see that the number

of engaging moments of the remote audience

members is higher than for the live audience members.

4.2 SCL

First, we compare the SCL data of the audience at

different locations during the whole performance.

There is a strong positive correlation between the data

from the live audience and the remote audience (r =

0.535, n = 12, p < 0.01), which indicates that the skin

conductance response pattern at both locations was

synchronised. Additionally, the result of the t-test

showed that there was no significant difference

between the response from the live audience and the

remote audience (t = 1.18, p > .05).

Although the SCL data at the two locations was

similar, we found that the two audiences responded

significantly different to different events. These

findings may help performers to better understand

what kind of effects could arouse a remote audience.

When the actor was singing, we found that the remote

audience was more absorbed (t = -4.04, p < 0.01)

(Figure 7). Additionally, both the theatrical smoke

and the interaction were more engaging for the live

audience (smoke effect: t = 3.35, p < 0.01; interaction:

t = 4.37, p < 0.01) (Figure 8 and Figure 9).

Figure 7: The SCL difference during the singing event.

How Are We Connected? - Measuring Audience Galvanic Skin Response of Connected Performances

Figure 8: The SCL difference during the theatrical smoke.

Figure 9: The SCL difference during the interaction.

4.3 Interview and Video Recordings

The data from interviews and video recordings is

summarised and presented in Table 1. In the video

recording, we found that eye contact between the

actor and the audience at both locations was constant

during the performance. Besides, most of the time, the

audience at both locations were smiling. According to

the results of the interview, all of the audience

members felt connected to both the actor and the

audience at the other location. Thus we can conclude

that both the live and the remote audiences were

similarly immersed during the performance.

5 DISCUSSION

In this paper, we reported about a study aimed at

investigating the effect that connected theatre plays

have on the experience of the audience. Both

objective (GSR sensor and video recording) and

subjective (interview) measurements were used in

this study. We found that compared to the live

audience, the remote audience reported a very similar

response to the whole performance, and had a similar

reaction to the event.

Generally, both the live and remote audience

members were engaged, and they had similar

response during the whole performance. This

suggests that connecting two spaces during a live

performance is feasible, and can enable a good

experience.

To be more specific, according to the SCL results,

the live audience was more engaged during the

interaction part and the part with theatrical smoke,

while the remote audience members were more

absorbed during the singing part, which is consistent

with the SCR results. This indicates that remoteness

Table 1: Summaries of interview and video recordings.

THE LIVE AUDIENCE THE REMOTE AUDIENCE

VIDEO

RECORDINGS

Eyes Contact

Constant eye contact Constant eye contact

Interactions

6 times 6 times

Laughter

2 times 3 times

Smile

Most of the time Most of the time

Applause

They applaud at the end of the play They applaud at the end of the play

INTERVIEWS

Closeness to

the actor

Being connected Being connected

Closeness to

another

location

audience

Being connected Being connected

Summarized

opinions

The play was interesting and entertaining, and we felt involved as part of the

play. We liked the play, because we could interact with the actor during his

performance, and it was also funny to see him singing a song with a vacuum

cleaner sound as background.

PhyCS 2016 - 3rd International Conference on Physiological Computing Systems

still has some effects on audience experience during

connected performances. The reason why the

theatrical smoke and interaction were more engaging

for the live audience members may be the physical

contact. Those two parts were more vivid, which

caused higher arousal of live audience members.

These results may also help producers to think about

how to design a connected performance, which better

takes into account both the live audience and the

remote audience.

Additionally, it is interesting to see that the remote

audience was engaged more often than the live

audience, based on the results of SCR data. To

explain this, technical issues should be considered.

According to the SCR results of the remote audience,

they were for example engaged when the projector

had problems. This means that when technical

problems occur, the remote audience members will

pay more attention and the GSR signals will increase.

It suggests as well that good control of the technical

aspects is crucial for connected performances.

There is a consistency of the GSR data (the SCR

and SCL results) and the other results. This

demonstrates that GSR is a reliable and valid

indicator of audience response.

6 CONCLUSIONS

This paper explores the effects of remoteness on

audiences attending theatre plays. Based on the

results of all measurements, we found that the remote

audience has a similar experience to the live audience,

which means that remoteness has little influence on a

connected performance. In addition, we can conclude

that audience experience of connected performances

is also influenced by the physical contact to the

audience.

During the experiment, the remote audience

experience was heavily influenced by technical

problems. So we conclude that adequate technical

support plays an important role in a successful

connected performance.

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PhyCS 2016 - 3rd International Conference on Physiological Computing Systems