How Commercial Food Videos Affect Female Customers
Measuring Female Bio-response Towards Commercial Food Videos
Xintong Zhu, Yong Wang, ZhenZheng Wang, Xiguang Wang and Chen Wang
Future Media Convengence Institute, Xinhua Net, Jinyu Building, Xicheng District, Peking, China
Keywords: Physiological Computing, GSR Sensors, Audience Experiences, Advertising Videos.
Abstract: The concept design of a food television commercial (TVC) could affect the effectiveness of immersive media
and user experience. Traditional methods (e.g., surveys or eye tracking) for evaluating the targeted consumer’s
responses are serious limited in several aspects. In this paper, through closely working with the TVC
designers, we used the data gathered from physiological sensor to measure viewers’ watching experiences.
We thereby conclude how we used our own Galvanic Skin Response (GSR) sensors to measure audience
responses to the three clips of food TVCs. The results demonstrate that GSR sensors can provide fine-grained
information for the advertisement community. Compared to subjective evaluation methods, the continuous
user experience can be vividly visualized, and this enables designers to efficiently evaluate the impact of a
TVC.
1 INTRODUCTION
The advertisement industry has been fully aware that
a content design for a commercial campaign plays a
vital role in spreading the name and the impact of
products. Different food promotion strategies shall be
specified with different concept designs in a
campaign. In particular, to attract the targeted
customers, advertising companies always seek for the
optimal way to design a television commercial (TVC)
that could seize the attention and further bringing the
tangible revenues.
However, it is a great challenge to measure the
impact of a TVC. First, obtaining such information is
rather expensive and time consuming. For instance,
the commercial companies generally hire agencies to
run market researches that normally take several
months. One of the feasible approaches is to carry out
online questionnaires to collect the answers from the
target customers and analyse the data. Second,
traditional methods (e.g., questionnaires) are not the
most appropriate one to measure the impact of a TVC,
since questionnaire data is discrete, which means the
data is hard to be connected to the whole duration of
a TVC. In addition, subjective reports can easily
collect information from the questions that users are
aware of, but it is unlikely to ask them to answer some
questions related to attention or engagement (Latulipe
et al., 2011). Last, recently, eye movements
Figure 1: The experimental session.
(Krugman et al., 1994)and facial expressions (Joho et
al., 2009)have risen as popular methods to measure
the impact of a TVC. We doubt the credibility of such
methods as a watcher might be completely absent-
minded when they are watching a video. If so, eye
tracking and facial expression data seem to be
particularly meaningless for evaluating the influence
of a TVC, since the relationship between user inner
attention and visual attention still remains unclear.
Physiological measurements have many
advantages as an alternative tool to monitor, in real-
time, audience responses in a video consumption. The
physiological data are continuous, providing time
series that can be mapped to events (e.g., the moment
that a brand logo appears) of a TVC. Second, the non-
intrusive nature of the physiological measurements
guarantees a seamless experience of the audience. In
Zhu, X., Wang, Y., Wang, Z., Wang, X. and Wang, C.
How Commercial Food Videos Affect Female Customers - Measuring Female Bio-response Towards Commercial Food Videos.
DOI: 10.5220/0006394300350044
In Proceedings of the 4th International Conference on Physiological Computing Systems (PhyCS 2017), pages 35-44
ISBN: 978-989-758-268-4
Copyright © 2017 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
35
addition, physiological sensors have been applied in
audience research, e.g., user emotion (Oliveira et al.,
2011)and user engagement(Picard and Daily, 2005).
Although some studies use them to assess the impact
of a video (Yoon et al., 1998), the audio effect
(Kenning and Plassmann, 2008), or use question-
naires to investigate audience’s purchase behaviour.
Surprisingly, no studies have ever used physiological
sensors to measure women’s experience when
watching a commercial. None of those studies has
paid particular attention to female customers, who are
most of the time the actual decision maker in a
household’s shopping journey.
In this paper, requested by the producers who
have rich experiences in designing commercial
videos, we dive into female customers’ experience in
commercial video consumption. Specifically, we first
interviewed the producers and collected their research
interests. Then, we designed the experiment and
chose the appropriate methods to answer the research
questions. In our case, we chose GSR sensors to
measure user experience, as they are highly accurate
for indicating the user arousal and the emotional
intensities compared to other bio sensors. Taking
advantage of the sensor data, we could map their
response to specific events (e.g., the moment that the
food brand appears).
Our contribution has twofold. On the one hand,
we focus our research interests on female customers,
since their watching experience has never been
investigated in the previous studies. On the other
hand, we use GSR sensors, instead of questionnaires,
to continuously record user responses in a food TVC,
a research area that has not been explored before.
1.1 Research Questions
The research questions were generalized from the
interviews with the producers. They are both very
experienced in commercial video production. They
believe that there are many ways to produce a good
TVC, but how to demonstrate that one concept design
is superior than the other one is a challenge. In
particular, the producers consider that how female
customers are influenced by these TVCs is extremely
important, because they are major buyers in
households. In other words, if a TVC hits their heart,
the chance is high that they will buy the product in the
future. The producers proposed three TVCs to run the
experiments, in which the different types of food are
involved. They had particular interests in female
reactions, i.e., how female would react when the food
close-up shot first appears, how do they response
differently when watching the three videos, and how
their bio-responses are related to their subjective
reports.
The interview was semi-conducted and coded
based on the notes. Four researchers participated and
concluded three research questions:
R1: What are the differences in female consumers’
responses when watching the three videos?
R2: How do the female consumers react to the close-
up of food first appearing in the videos?
R3: How is the female bio-response correlated to the
subjective reports?
The remainder of this paper is organized as
follows. Related work will be reported first and the
design alternatives for the hardware and the software
are introduced next. Then the results are exhibited.
followed with the discussion and the conclusion at
last.
2 RELATED WORK
There are various definitions for audience response.
For instance, some studies use different synonyms,
such as audience engagement, audience feedback,
audience interests, audience interaction and audience
(or user) experience. Christopher Peters et al. (Peters
et al., 2009) described audience response as a
combination of focus, interest, perception, cognition,
experience and action. On the other hand, Heather L.
O’Brien et al. (O'Brien and Mclean, 2009) pointed out
that audience engagement (response) can facilitate
users with more enriching interactions in computer
applications: engaged users tend to recommend the
products (or service) to others. In addition, in
affective computing, users’ emotional response
(Wang, Prendinger, and Igarashi, 2004) is obtained as
an evaluation tool to define user engagement. In game
application studies (Fischer and Benford, 2009),
audience engagement (response) refers to players’
state awareness and synchronization. Audience
biofeedback, e.g., arousal, is also used as an indicator
of the levels of players’ engagement(Chanel et al.,
2008).
Audience response can be measured in two ways:
explicitly and implicitly. Explicit methods normally
require users’ intentional inputs, like surveys or
ratings, whereas implicit measurements generally
capture audience feedback through physiological
sensors, such as GSR sensors. During the process of
data collection, sensor readings are collected in real
time without interrupting audience’s watching
journey.
PhyCS 2017 - 4th International Conference on Physiological Computing Systems
36
Visual analysis methods, e.g., eye movements, are
also classified into the scope of explicit methods of
measuring audience response. In such studies, eye-
gaze, eye movements and head movement trackers
are installed to define users’ interested spots during a
video consumption or other applications like game
studies. Some other methods, e.g., physiological
sensors, are also combined with visual analysis
methods to capture audience response, in which
audience inner state are characterized, e.g., boredom
or fatigue. However, these studies could not avoid
intentional requiring audience annotations constantly,
by which the labels of the attributes of the data are
generated(Kapoor et al., 2007).
Audience response has a significant impact on
many applications. There are plenty of quantitative
studies executed in game researches based on the
reliability and suitability of physiological sensors.
Pejam et al.(Mirza-Babaei et al., 2013) have explored
the possibilities of improving games design by
providing user biofeedback, and the results showed
that a combination of user GSR feedback would help
designers choose proper design strategy for higher
game play quality. In the game industries, Sony, for
example, recently has decided to add GSR sensor into
their new game controller, DualShock 4 (DualShock,
2017), where users’ GSR-response is detected as
interest level towards the game. Another example
(Sakurazawa et al., 2003) is that users’ GSR-response
is used as the users’ agitation in a game, and the more
often a user feels agitated, the more enemies will
appear in a game.
Previous studies have also shown that GSR sensor
is one of the indicators for users’ cognitive and
emotional status. Lin et al (Lin et al., 2008)
successfully investigated how audience GSR-
response differs in the different movie sections. In
particular, the fluctuations of GSR data were linked
to events during a 3D movie experience.
Matthew et al. (Pan et al., 2011)demonstrated a
novel interaction by using GSR sensors in an audio
stream bookmarking, where users’ GSR-response
was monitored as a response to the external
interruptions. GSR sensors were also applied in a
wearable system in order to help users select the high
arousal photos, which were the most relevant to the
users’ ordinary daily life(Sas et al., 2013).
Web applications are also benefited from
audience response research. In the paper(Lunn et al.,
2010), users’ biofeedback was used to distinguish
audience response within the different age scopes, in
terms of a web 2.0 application. Audience affective
states were also employed to investigate what kind of
interaction technique on the web has a significant
Figure 2: The GSR sensors and the monitoring system.
impact on users(Hart et al., 2012). Transforming
audience physiological signals (audience response)
into a smile icon was implemented in an online
chat(Wang et al., 2004). Users’ biofeedback on
preference, e.g., like or dislike, was used as the input
data in order to improve the accuracy of online
recommendation system(Madan et al., 2004).
Audience response also plays an important role in
some other applications. For instance in Olympic
Games, audience clapping frequency was visualized
on the display screen to encourage athletes’
performance(Barkhuus, 2008); audience ‘cheering
meter” was measured to aid voting at rap
competitions(Aigner et al., 2004).
GSR, is also known as galvanic skin response,
electro dermal response (EDR), psych galvanic reflex
(PGR), skin conductance response (SCR), or skin
conductance level (SCL). GSR sensors measure the
users’ electrical conductance of the skin, where users’
sweat glands are varied and controlled by the
sympathetic nervous system. Therefore, GSR sensors
are normally considered as an indicator of
psychological or physiological arousal or stress.
When users are highly aroused, users’ skin
conductance increases in turn. GSR sensors can be
either purchased from commercial companies or self-
developed. Commercial companies, such as BioPac,
Thought technology and Q sensors, offer this type of
GSR sensor with a high price. Although such
commercial sensors allow researchers to start
experimenting immediately, they do not provide
functions to measure groups of users simultaneously.
The reason is that the communication protocol
normally is Bluetooth, which has limitation to
connect cell nodes in wireless network, e.g., a master
and up to 7 slave piconet networks.
3 METHODOLOGY
3.1 Participants
All the participants were recruited from our institute
per the requirements (from 25 to 34) of the producers,
How Commercial Food Videos Affect Female Customers - Measuring Female Bio-response Towards Commercial Food Videos
37
and they had not any visual or acoustic problems
(Figure 1). All participants were divided into 4 teams
with 4 persons in each.
3.2 Stimuli and Apparatus
The stimuli consisted of 3 different short videos
(video A, B, and C). There was a short pause left
between video clips (32 seconds) with grey screen to
let users jump out of the previous watching
experience. The video A is intended to prompt the
organic blueberry, the video B advertises the ham
product, and the video C addresses on the beer
chicken. The three videos were played in different
sequence for each team in order to minimize the
influence of order. The duration of the experiments
was 12 minutes 34 seconds in total.
We built the GSR sensors using a Jeenode board
with a RF12 wireless module, a low pass filter and
some accessories (e.g., electrodes) (Figure 2). The
wireless function of the RF12 module makes it
possible for simultaneously measuring audience at a
large scale.
The sensors have been validated through a
number of experiments (anonymous). All the sensor
subordinates simultaneously send GSR data packets
back
to the master sink node, which is connected to a
laptop. The master node communicates with all the
subordinate nodes by using a polling mechanism. In
our case, we set up the sampling rate at 2Hz, which is
not optimal, but it is sufficient to recover the sensor
data. The sensors are robust against noise because of
the circuit design and the data smoothing procedure
in the software (Figure 3). In addition, all the sensor
data were synchronized with the time stamp of the
videos.
Figure 3: the diagram of the feature extraction of the GSR
data.
3.3 Interviews and Questionnaires
The purpose of the interviews and questionnaires is to
better interpret the sensor data. All participants were
interviewed before and after they watched the videos.
The interviews mainly focused on the following five
aspects: the title of their job, their favourite food, their
favourite videowhich specific scenes in the videos
that they were interested in, and reasons for likes and
dislikes.
The questionnaires were designed at seven scales.
The pre-questionnaires include the following
questions:
1. What is your job and responsibilities?
2. what kind of food do you like to eat?
3. what kind of food you do not like to eat?
4. what taste of food do you like?
5. what taste of food you do not like?
6. what kind of video do you like?
7. what kind of video you do not like?
8. Do you often watch video by smart phone?
9. Do you often buy food online?
The post-questionnaires have three questions:
1. What do you want to eat after watching?
2. Which video do you think the best?
3. Which style of the video do you like? And the
reason?
3.4 Software
All the videos were played on the four same-type
smart phones, and the participant hold the phone in
their most comfortable gesture. The whole
experiment was video-recorded by using the facilities
installed in our user lab (Figure 4). The recording
video streams were collected by the software written
in Python, which is installed in the observation room.
All the sensor data was analysed by SPSS and
MathWorks (Matlab).
3.5 Experimental Procedures
Before the experiment started, all the participants
were asked to fill an informed consent form and the
pre-questionnaires. Then the interview began and oral
instructions were provided. After that, the
experimenters helped the participants to wear the
sensors, and then they opened the video play
program, and clicked the “play” button. When the
experiment was finished, the interviews and the post-
questionnaires were conducted after the sensors were
taken off.
3.6 Data Analysis
To explore the participant’s GSR response, both the
event-related skin conductance response (SCR) and
PhyCS 2017 - 4th International Conference on Physiological Computing Systems
38
Figure 4: the user lab and the observation room.
the skin conductance level (SCL) were analysed and
normalized by Z-score. In addition to that, we also
used analysis of variance (ANOVA) and Least
Significant Difference (LSD) to test the significance
of physiological data in three videos.
There are several steps to analyse Electro Dermal
Activity (EDA) to extract the event-related SCR data
based on Fleureau (
Fleureau, Julien, Philippe Guillotel,
and Izabela Orlac. 2013
) and his colleagues’ work.
Normally, when participants receive an engaging
stimulus, their GSR value will increase quickly with
a latency of 1-3 seconds, and after reaching a
maximum value, it will recover to a value around the
baseline level. Firstly, a 2Hz (G(t)) low-pass filter
was applied to remove noise from the raw data, such
as other physiological signals and electrical noise.
Then a derivation (from G(t) to G’(t) ) was applied to
calculate the rate of change of the GSR data. In doing
so, we could know if the GSR value is ascending
(positive values) or descending (negative values).
After that all the negative values were discarded, with
only the positive ones being kept (from G’(t) to
G’
+
(t)), which means that we only focus on the
increasing phases of the GSR signal, because the
negative phases only reflect the recovery of the signal
to the baseline. These steps helped us to extract the
SCR data from the raw electrophysiological data.
(1)
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 G’
+
(t) were converted
into G(i) (1 i k, k is the number of the moving
windows). G(i) is the mean derivative value of one
subsample in one specific moving window.
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 G
n
(i) (1 n N, N is the
number of the sample; 1 i k, k is the number of the
moving windows).
G
n
(i) is the individual value in a moving window,
which cannot represent the whole group’s response,
considering some outliers, differences from person to
person, noise (e.g. body movements) and so on. 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 (ܩ
n
(i) and the
background noise. We took the lowest 10% of the
values in ܩ
n
(i) as background noise. ܩ
n
(i) 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 p-values lower than 5%, we considered the
response during that time sample to be significantly
different from the background noise.
The mean value of EDA of the first 32s blank
video (grey screen) were used as the baseline, which
was then subtracted from the raw
electrophysiological data, to remove individual
differences.
Z-score is the number of standard deviations
from the mean a data point is. Z-scores range from -
3 standard deviations (which would fall to the far
left of the normal distribution curve) up to +3
standard deviations (which would fall to the far right
of the normal distribution curve). The basic z score
formula for a sample is:
z = (x μ) / σ (2)
where, μ is the mean of the population, σ is the
standard deviation of the population. The absolute
value of z represents the distance between the raw
score and the population mean in units of the standard
deviation. z is negative when the raw score is below
the mean, positive when above.
Here, we use Z-score to standardize the EDA in
order to compare the changes of EDA when different
short videos were played.
The ANOVA is used to determine whether there
are any statistically significant differences between
the means of three or more independent (unrelated)
groups. When ANOVA gives a significant result, this
How Commercial Food Videos Affect Female Customers - Measuring Female Bio-response Towards Commercial Food Videos
39
indicates that at least one group differs from the other
group. Yet, the omnibus test does not indicate which
group differs. In order to analyse the pattern of
difference between means, the ANOVA is often
followed by specific comparisons, and the most
commonly used method is to compare two means.
Here, we used least significant difference (LSD) to
compute the smallest significant difference between
two means as if these means had been the only means
to be compared (i.e., with a t test) and to declare any
significant difference larger than the LSD. So, we
firstly used ANOVA to analyse weather the EDA is
different between some close-up shot and normal
frame, and then the LSD analysis was used to
compare them to see whose difference is significant.
4 RESULTS
In the Results section, there are four parts that help to
answer the research questions mentioned before.
First, the overview of the participants’ physiological
response is reported by showing the mean z-score of
the 16 participants. So we could build a general idea
of the participants’ response of the three videos
according to the first part. After that, the results of the
SCR of the participants are described in the second
part, and the results of the SCL are reported in the
third part. Both the general response to different
videos and the responses to specific scenes are
analysed, enabling us to understand the participants’
reaction to both the entire videos and the specific
moments in the videos like the food close-up. And
then, the results of the questionnaires are shown in the
last part of the section, helping us to compare the
results of the objective measurement (GSR sensor)
and the subjective measurement (questionnaire).
4.1 Overview of the Participants’
Physiological Response (R1)
First, the mean Z score levels corresponding to the
time, analysed from the data of 16 participants during
the whole experiment, are shown in Figure 5. An
overview of the participants’ physiological response
to the three videos could be thus built.
In Figure 5, there are three lines representing the
Z score of the participants’ galvanic skin response to
each video. Some descriptions of the moments in the
video corresponding to the peaks are also exhibited in
the figure. The peaks mean that the participants had
the emotional arousals at this moment.
Figure 5: The mean of Z score for each video. The red line
represents the Video A, which promotes the blueberries.
The green line represents the Video B, which addresses the
story of making ham. The blue line shows the Video C,
which advertises the beer chicken. The X-axis is the time
and the Y-axis is the GSR Z score. The red annotations are
corresponded to the peaks, indicating that the participants
had the emotional arousals.
From Figure 5, we can safely conclude that the
participants’ physiological response has three trends:
1) The participants’ response decreased during all
three videos, indicating that the participants were
losing their interests while watching the videos,
especially during the Video A. 2) It is easy to find that
the participants had the emotional arousal to the
close-up of the food when watching all videos. 3)
Besides, the arousal level in the three videos varies.
According to the mean Z score, the arousal level in
the Video A is higher than the Video C, and the
arousal level in the Video B remains the lowest.
4.2 SCR Related Video Events
(R1&R2)
Based on the algorithm mentioned in the software
section, event related significant emotional arousal
were detected as shown in Figure 6, 7, and 8.
The SCR results from the three videos are rather
different. The participants were significantly aroused
in almost the entire watching process of the video C
(Figure 8). As a contrast, only two moments caught
the significant arousal in the video B (Figure 7)
and no significant arousal appeared in the video A
(Figure 6).
In addition, the SCR analysis also reveals which
events significantly aroused the viewers. For
instance, in the Video B, the participants were highly
engaged in the scenes where the giant showed the
PhyCS 2017 - 4th International Conference on Physiological Computing Systems
40
Figure 6: The extracted SCR signals of the participants
during the Video A. The x-axis is the time in seconds, and
the y-axis is the mean p value of the bilateral MWW test.
The blue line is the participants’ mean p value, the red line
represents the critical value (p = .05). When the blue line is
below the red line, it indicates that the participants had a
significant emotional arousal response at 0.05 p value level.
Figure 7: The extracted SCR signals of the participants
during the Video B. The x-axis is the time in seconds, and
the y-axis is the mean p value of the bilateral MWW test.
The blue line is the participants’ mean p value, the red line
represents the critical value (p = .05). When the blue line is
below the red line, it indicates that the participants had a
significant emotional arousal response at 0.05 p value level.
ham to his friend, and the slogan and logo shown up
at the end of the video, except for the moment (around
10 seconds) that they saw a sleeping cat. For the video
A, the participants did not show any significant
emotional moments. While for the video C, only two
events significantly stimulated the participants’
emotions.
4.3 SCL Levels in Three Videos
(R1&R2)
The user SCL level differences in the videos are
shown in Figure 9 and Figure 10, and the significant
bio-responses were observed in them.
Figure 8: The extracted SCR signals of the participants
during the Video C. The x-axis is the time in seconds, and
the y-axis is the mean p value of the bilateral MWW test.
The blue line is the participants’ mean p value, the red line
represents the critical value (p = .05). When the blue line is
below the red line, it indicates that the participants had a
significant emotional arousal response at 0.05 p value level.
Figure 9: The mean and standard error of the SCL data of
each video.
Figure 10: The mean and standard error of the SCL data of
each scene.
The SCL of the participants in the three videos are
significantly different (F (2, 14) = 393.36, p < .05).
By using the LSD method to do the pairwise
comparison, the participants’ SCL data is found to be
significantly different from each other. It shows that
How Commercial Food Videos Affect Female Customers - Measuring Female Bio-response Towards Commercial Food Videos
41
the SCL is the highest in the video C, while it is the
lowest in the video B. It is also spotted that the
viewers’ SCL showed significant differences among
the six scenes where the close-up scenes occurred
(three from the blueberry video, and only one close-
up from the other two videos respectively): F (5, 11)
= 53.62, p < .05). The LSD method shows that the
SCL data is significantly different from each other,
except for the second close-up of the blueberry and
the one of the beer chicken. In particular, the SCL of
the participants during the first close-up of the
blueberry is significant higher than the other scenes.
4.4 Questionnaires (R3)
There is no significant difference among the
evaluation of the videos: F (2, 14) = 5.20, p = .11
based on the repeated measuring ANOVA test of the
questionnaire.
Figure 11: The disliking ratio reported from the
questionnaires.
Figure 12: The liking ratio reported from the
questionnaires.
The ratio on the likes and dislikes videos shows
in Figure 11 and 12. More than a half of the
participants disliked the video B because of the dark
image and scary background music. For the most
favourite video, 44% of the participants chose the
video C, 37% of the participants chose the video A,
and only 19% of the participants chose the video B.
The results of the questionnaire are similar to the
results of the SCR and SCL results. For example, both
the SCR and SCL results indicate that the video C is
the most engaging one while the video B is the least
engaging one. To sum up, the results obtained from
the sensors are consistent with the ones obtained from
the questionnaires.
5 DISCUSSION
In this paper, we conducted an experiment by using
GSR sensors, where user bio responses towards the
three food TVCs were analyzed. Through a
comparison with the questionnaires, it has been
confirmed that the sensor measurement is consistent
with their subjective reports.
The study has answered the research questions
clearly. First, according to both the GSR results and
the questionnaires, the participants preferred the
video C (Beer Chicken) the most, and showed the
least preference to the video B (Ham). Second, some
specific scenes, like the close-up of the food, could
induce the participants’ emotional arousal. Third, the
results of the GSR data were grounded by the
questionnaires.
There are some interesting findings discovered
from the results of the study. First, the attention in all
videos is decreasing from the start to the end of each
video according to the z-score figure, which means
that the participants were losing their interest during
all the videos, especially after 90 seconds. It suggests
that the length of a TVC is crucial. This information
is extremely helpful for producers, especially when it
comes to the effectiveness of a TVC. Second, some
interesting phenomena were observed among the
female consumers. One of them is that they prefer
some specific moments in a video, e.g., the close-up
of the food, the good-looking figure of the actors, and
the appearance pets. These reasons may explain why
the video C is the most popular one. In addition, it
seems that women are sensitive to the bright scene
and the joyful music background, which were
reported from the post-interviews.
Nevertheless, there are some differences found
between the SCL and the SCR. According to the
results of the SCL, the arousal level of the video C is
the highest, while the video B is the lowest, which is
consistent with the results of the questionnaire.
However, according to the results of the SCR, there is
no significant arousal moments during the video A,
which means the video A did not significantly arouse
the viewers. We assume that the two patterns of the
EDA may reflect the different user experiences, and
we need further investigation for such phenomenon.
PhyCS 2017 - 4th International Conference on Physiological Computing Systems
42
This study proves again that our GSR sensor
system is robust for such studies. In particular, the
monitoring system has helped us learn the actual
status of sensors. Even if the sensor gets broken, we
can easily replace it with a new one. However, the
sensor prototype is a bit of bulky for users to wear. A
half size of the current one will be an optimal shape
for an unobtrusive watching experience.
In this study, we did not categorize which specific
emotions are induced in viewers, considering the fact
that we only have one type of sensors in our case.
Therefore, more sensors are required if user
emotional states need to be further classified. In the
future work, we can add other types of sensor (e.g.,
ECG sensors) to obtain more sensor data, which may
help us better define emotions elicited during video
consumption.
6 CONCLUSION
In this paper, we have reported the experiment on
female bio-response towards the three types of food
TVCs. The results have exhibited how those videos
could affect female reactions and their watching
experiences. Our study presents that physiological
data does have superior advantages on measuring user
experiences compared to subjective reports. By
following our method, researchers can design an
experiment with their own research purposes.
Furthermore, other similar studies, e.g., new media
design, can be also benefited from our learning
experience.
Besides, our work also demonstrates that the
combination of the hardware and software solution
can be rather helpful for commercial companies. By
using our method, they can pre-assess the effects of
TVCs, especially among targeted consumers who are
particularly interested in investigation. In such a
manner, it can reduce the risk before the launch of
products, and pre-sampling test method can help them
to adjust the marketing strategy.
In addition, it has been fully demonstrated that our
GSR sensor system is robust and can simultaneously
and accurately capture the GSR signals from users.
The system allows us to quantify user experience, and
at the same time keeps the confidentiality with user.
Currently we are working on the process of scaling up
the system and attempting with the integration of
different sensors
(e.g., ECG sensor and acceleration
sensors). The other types of sensor can be integrated
into our sensor network to provide a more complete
representation of user experience.
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