Conducting an Experiment for Validating the Combined Model of

Immersion and Flow

Ehm Kannegieser

, Daniel Atorf

and Josua Meier

Fraunhofer Institute of Optronics, System Technologies and Image Exploitation,

Fraunhoferstraße 1, 76131 Karlsruhe, Germany

Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany

Keywords: Flow, Immersion, Measurement, Experiment and Validation.

Abstract: Detecting high intrinsic motivation and Flow states is key for successful adaptation processes that may be

used to improve learning outcome in Simulations and Serious Games. Until now, the method of choice to

measure Flow, is the usage of questionnaires. Because of the shortcomings of this method, the ultimate goal

is, to establish an alternative measuring method through correlations of physiological sensor data. Beforehand,

the theoretical model of Flow is enhanced with the more fine-grained model of immersion plus the design and

implementation of an experiment to validate said model is introduced. In conclusion a perspective towards

preliminary test results and upcoming data analysis is given.

1 INTRODUCTION

Education is one of the biggest challenges of the 21

century. What better way to improve it than using the

vast amount of technology available to us today. A

successful tool aiding in education are “Serious

Games” (Girard, Écalle and Magnan, 2013), games

which do not put entertainment value in the front, but

rather focus on achieving learning experiences in

players. One of the biggest questions in the field of

Serious Game analysis is: How can this learning

effect be improved? Previous studies find that the

learning effect of Serious Games is linked to the fun

they provide to players (Deci and Ryan, 1985; Krapp,

2009). Thus, the question becomes, how can fun be

improved? And based on that, how can fun be

measured?

When looking at the raw definition of fun,

becoming voluntarily engrossed into an activity,

similarities can be found to the definition of Flow

given by Csikszentmihalyi (Csikszentmihalyi, 1991),

which describes the optimal enjoyment of an activity.

As such, Flow becomes an interesting measurement

when analysing the fun experienced during gameplay

(Beume et al., 2008). In order to better measure the

range of the immersive experience, the sub-optimal

state of experience, Immersion, is also looked at.

Flow and Immersion are described in more detail in

sections 2.1 and 2.2 respectively, but for general

purposes, they can be thought of as states of high

concentration on the game. By measuring these states,

Serious Game developers can make judgements about

how fun, and respectively, how much learning value

is provided by their game.

However, there are still problems when using this

approach. As they are subjective experiences,

measuring Flow and Immersion is difficult. The

current approach to measure them is based on

questionnaires (Nordin, Denisova and Cairns, 2014).

These questionnaires can either be used during the

game – disrupting the player’s concentration – or

after the game, leading to imprecise results.

Additionally, questionnaires can only elicit subjective

measurements, further degrading the quality of the

data gathered.

For this reason, the development of a system for

automatic measurement of Immersion and Flow

becomes increasingly interesting. Instead of using

questionnaires filled out by participants, this system

uses the player’s physiological measurements to

determine their current Flow/Immersion state. In this

paper, a study towards the development of such an

automatic measurement system is presented. The

study attempts to link the experience of Flow and

Immersion in participants with reactions in their

physiology. First, a combined model of Flow and

Immersion is presented in section 3.1 in order to

252

Kannegieser, E., Atorf, D. and Meier, J.

Conducting an Experiment for Validating the Combined Model of Immersion and Flow.

DOI: 10.5220/0007688902520259

In Proceedings of the 11th International Conference on Computer Supported Education (CSEDU 2019), pages 252-259

ISBN: 978-989-758-367-4

measure the player experience in games in better

detail. The experiment structure is presented in

section 3.2. Section 4 presents how physiological

measurement data is used in evaluating the study, as

well as approaches for evaluating the model and the

physiological measurement data with Flow. In section

5, the validity of the study is analysed based on

internal and external factors. Finally, section 6

presents a conclusion and a future outlook.

2 RELATED WORK

2.1 Flow

The definition of Flow was first brought up by

Csikszentmihalyi and is commonly known as the state

of optimal experience of an activity and a state of

great concentration (Csikszentmihalyi, 1991). In the

context of sports, Flow might also be known as being

“in the zone”. Temporal and spatial dissociation are

the main symptoms of having reached the Flow state.

The concept of Flow is based on the intrinsic and

extrinsic motivation model of behavioural

motivation, which describes that actions can either be

motivated based on external factors, such as money

or fear of reprimand, or internal factors when

performing an action for the joy of the action itself.

Based on this intrinsic motivation, Csikszentmihalyi

considers the optimal way to experience and enjoy an

activity as one that is intrinsically motivated and

fulfils a certain set of requirements. He first describes

this in a three-channel model, showing that a fine

balance between skill of the person and challenge of

the task at hand must be achieved in order to reach the

Flow state. What makes Flow special is that

compared to passive enjoyment of an activity, the

Flow state enables people to enjoy even traditionally

taxing actions such as demanding work.

All this makes Flow an interesting research topic

for video games. Unlike television or regular books,

games are meant to be enjoyed through active

participations. Furthermore, the Flow state is similar

to the effect experienced by many players, including

loss of a sense of time and spatial awareness. In order

to study the Flow state in games, Sweetser and Wyeth

map Flow to games in their Game Flow

Questionnaire, mapping the original components of

Csikszentmihalyi’s Flow model (Sweetser and

Wyeth, 2005). This Game Flow Questionnaire is later

adapted into the EGameFlow questionnaire for use

with Serious Games (Fu, Su and Yu, 2009).

Other variations of Flow questionnaires exist.

Rheinberg et al. design a Flow questionnaire to be

used iteratively and multiple times to elicit the Flow

state in rapid succession due to its low amount of

questions (Rheinberg, Vollmeyer and Engeser, 2003).

While it was originally for use with sports, its

questions are formulated in a way that it can also be

used in general purpose environments.

Flow is generally measured using questionnaires.

Attempts to link the Flow state with physiological

measurements have been made in the past but have

not yet reached a level that they can fully replace

questionnaire elicitation. For example, Cheng

measured Flow in relation to eye movements and

found that a lower amount of rest points may be an

indicator towards increased Flow (Cheng, 2014).

2.2 Immersion

When talking about Immersion, it is important to

realize that there are two concurrent definitions of

Immersion being used in parallel (Zhang, Perkis and

Arndth, 2017). The first definition of Immersion is

based on the term presence and refers to the feeling

of being physically inside a virtual world. The second

definition, which will be used throughout the rest of

this paper, is engagement-based Immersion and is

based on the effects of an activity on a person, similar

to Flow. As such, it has been called “the sub-optimal

experience of an activity” in reference to Flow as the

“optimal experience of an activity”.

Unlike with Flow, which is defined by the

definition given by Csikszentmihalyi

(Csikszentmihalyi, 1991), there are multiple different

approaches to defining and measuring Immersion.

Ermi and Mäyrä define Immersion based on three

dimensions in the model presented in their paper

(Ermi and Mäyrä, 2005). The first dimension, sensory

Immersion, refers to the visual and auditory

presentation of the game as the player takes it in. The

idea is that a game with better visuals and audio will

be better at immersing players in a virtual world,

whereas poor visual and audio quality will distract

from the experience presented. The second

dimension, imaginative Immersion, is based on story

elements and the world of the game. Finally, the third

dimension, challenge-based Immersion, is based on

the player’s engagement with the game world. Its

definition is synonymous with the definition of Flow

given by Csikszentmihalyi in that it refers to a balance

of skill and challenge to reach a higher state of focus.

However, it does not include the state of apathy,

added by Csikszentmihalyi to refer to situations in

which both skill and challenge are low. The model

described by Ermy and Mäyrä is special in that it

defines both a dimension based on player

Conducting an Experiment for Validating the Combined Model of Immersion and Flow

253

engagement, challenge-based Immersion, and two

dimension based on analysing the game, imaginative

Immersion and sensory Immersion.

The most exhaustive analysis of engagement-

based Immersion is given by Cairns et al. in their

series of papers (Cairns et al., 2006; Jennett et al.,

2008). They define Immersion as a hierarchal model

with three levels, each level representing a higher

level of Immersion. The first level, engagement,

refers to the basic idea of interacting with a game. The

second level, engrossment, is reached when players

become emotionally involved with the software

presented, either positively or negatively. In this state,

controlling the game starts feeling completely natural

and input devices such as the mouse or the keyboard

are no longer consciously part of the experience. The

final level, Total Immersion, is reached when players

completely become in sync with their avatars in the

game and lose their sense of both time and of their

surroundings completely.

The weakness of the model presented by Cairns et

al. is that while it presents a way to measure

Immersion, it can make no statement about which

level of the Immersion hierarchy players are in at a

given moment. Cheng et al. improve this model by

adding dimensions to each levels of the hierarchy

(Cheng et al., 2015). The first level engagement is

broken down into the three dimensions attraction,

time investment and usability. Attraction refers to the

ability of the software to make users use it. Time

investment refers to the entry barrier of the first

Immersion level, which is spending time with the

application. Usability refers to the software being

usable, as non-usability would prevent user

engagement. The second level, engrossment, consists

of emotional attachment and decreased perceptions.

Emotional attachments can be either positive or

negative. Decreased perceptions refers to the loss of

sense of time and loss of spatial awareness. The

highest level, Total Immersion, is split into the two

dimensions presence and empathy. Presence refers to

the concept presented in the beginning of this section,

the feeling of being present in a virtual location

despite physically being present in the real world.

Empathy refers to the level of connection with the

player avatar and describes a state in which the player

can feel the emotions experienced by the avatar in the

game. They also present their findings in form of a

questionnaire for measuring Immersion.

2.3 Flow vs Immersion

When looking at the definition of engagement-based

Immersion as a hierarchical construct by Cairns et al.

and Cheng et al., a large amount of overlap can be

seen with the Flow definition presented by

Csikszentmihalyi in section 2.1. Both definitions

have requirements corresponding to the player feeling

in control and being presented with an adequate

challenge. Both Flow and the two higher levels of the

Cairns et al. Immersion model lead to an experience

of real-world disassociation, containing both a loss of

a sense of time and spatial awareness. The most

curious overlap is presented in the highest level of

Immersion, Total Immersion. In that state, players

appear to be cut off completely from the outside

world. This sounds similar to Flow, which has similar

symptoms. In fact, Georgiou and Kyza define the

empathy dimension of the extended model presented

by Cheng et al. in section 2.2 as Flow, considering it

as part of the Total Immersion state (Georgiou and

Kyza, 2017). The main difference between Flow and

Immersion is that Flow does not consider the player’s

emotional involvement in the game. A comparison

between the components of Flow and Immersion is

presented in table 1:

Table 1: Comparison between Flow and Immersion.

Flow

Immersion

Task

The Game

Concentration

Cognitive

Involvement

Skill/Challenge

Balance

Challenge

Sense of Control

Control

Clear Goals

Emotional

Involvement

Immediate Feedback

Reduced Sense of

Self and of Time

Real World

Dissociation

3 ONGOING RESEARCH

3.1 Proposed Model

One of the proposed ideas in this paper is a combined

model of Flow and Immersion (Kannegieser, Atorf

and Meier, 2018). For that purpose, the Flow model

as described by Csikszentmihalyi and the

engagement-based Immersion model described by

Cairns et al. are used. Cairns et al. have three layers

in their Immersion model, engagement, engrossment

and Total Immersion. The highest Immersion states,

engrossment and Total Immersion, share similar

phenomena, such as a loss of a sense of time and a

loss of spatial awareness. As such, a model is

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proposed, in which Immersion is a hierarchical

structure and Flow is a state at the top of the

hierarchy. The proposed model is shown qualitatively

in figure 1. This figure shows the relationship

between increased Flow and the Immersion levels

defined by Cairns et al. However, it must be noted that

there is no relationship between the skill/challenge

balance and Immersion.

Figure 1: Hierarchical Immersion model presented by

Cairns et al. and improved upon by Cheng et al. (left).

Proposed combined model of Flow and Immersion.

Qualitative view, the skill/challenge balance does not

influence Immersion.

3.2 Experiment

The experiment presented in this study has two goals.

First, the validation of the combined model of Flow

and Immersion. Second, gathering physiological data

that can be used to find a correlation between

physiological measurements and Flow/Immersion

states.

3.2.1 Physiological Measurements

Special care has to be taken when selecting

physiological metrics for use in a Flow experiment.

Certain types of measurement may hinder the Flow

experience and distract participants from becoming

immersed in the game. For this reason, metrics were

chosen that could be measured with a minimum of

intrusion and promised to yield relevant results.

The first physiological measurement type used is

Galvanic Skin Response (GSR). GSR measures skin

conductivity based on two electrodes placed on the

body. This metric was chosen due to being a useful

indicator in other psychological states, most notably

arousal (Mandryk and Atkins, 2007). Usually, GSR is

measured with electrodes placed on the hand, but

since hand movements would cause problems when

recording data, foot measurement provides an

alternate measuring possibility (Gravenhorst et al.,

2013). The skin conductance signal consists of two

different signals which are overlaid on top of each

other. One signal which changes quickly in response

to stimuli over seconds, and one signal that changes

slowly over minutes. The sensor used to record GSR

during the experiment is the Shimmer3 GSR+ unit. It

works using Bluetooth, which means participants do

not get obstructed by cables placed around their legs.

During test runs, participants have noted that they no

longer realized they were wearing the sensor,

suggesting it does not hinder the Flow experience.

The second measurement type used is an

electrocardiogram (ECG). Like GSR, ECG was used

successfully in previous studies regarding

physiological states, which made it interesting for the

study (Mandryk and Atkins, 2007). An ECG is used

to measure heart muscle activity from different angles

and can be used to extract heart rate and amplitude. It

is measured using electrodes place in the chest region.

For the experiment, five electrodes were used. The

sensor device used in the experiment is the Shimmer3

ECG unit. Like the GSR+ unit described above, it was

chosen due to its lack of cables, making the

measurement device less noticeable when

participants are wearing clothes.

The third measurement type is eye tracking.

During gameplay, player’s eye movements on the

screen are recorded. Eye movement is divided into

saccades, the movement, and fixation points, on

which the gaze is focused. Previous work in the

research has linked a lower number of fixation points

to higher Flow (Cheng, 2014), making this

measurement an interesting observation point. As

measurement is taken indirectly, it does also not

influence Immersion and Flow states. The camera

used in the experiment is the Gazepoint GP3 tracker.

The final measurement taken is web cam footage

of the player playing the game. Using this footage,

emotion recognition can be performed. The weakness

of this approach is that only emotions clearly

displayed on the face of the participant can be elicited

with great confidence. Other options for emotion

recognition, questionnaires and a facial EMG are

considered too distracting when playing games.

Electrodes on the face were found to be harder to

ignore than electrodes placed below clothes on the

chest. A full-HD camera records the centrally

positioned participant.

3.2.2 Experiment Structure

The experiment structure is based on a previous

experiment designed for measuring Flow in Serious

Games using physiological measurements (Atorf,

Hensler and Kannegieser, 2016).

The number of participants chosen for the

experiment is 40, as this number is similar to the

Conducting an Experiment for Validating the Combined Model of Immersion and Flow

255

number of participants used in other experiments in

this area (Cairns et al. 2006, Jennett et al., 2008). There

were no requirements for participants, as the

experiment is aiming for as close to a random selection

as possible.

The experiment consists of three phases. During

the setup phase, the game is selected, and the sensors

are placed on the participant. Game selection is free.

Participants can bring their own games or use a

distribution platform like Steam to install a game of

their choice. Free game selection was chosen to

improve the odds of players reaching higher Flow and

Immersion states, at the cost of game-specific analysis

options.

During the second phase of the experiment, the

gaming phase, participants play the game for 30

minutes. The duration was chosen based on test runs,

as 30 minutes were found to be enough to reach the

Flow / Total Immersion state. While the participant is

playing, the physiological measurements presented in

3.2.1 are recorded as well as gameplay footage.

Finally, during the assessment in the third phase,

participants watch a recording of their game session as

well as web cam footage of themselves. While

watching this footage, Flow and Immersion

questionnaires are filled out about how immersed the

participant was at the time of the recording. By making

participants fill out questionnaires while watching a

recording, more accurate data can be gathered without

interrupting the Flow/Immersion during the game

session itself to fill out questionnaires. A similar

approach using video footage is used in the study by

Rajava and Kivikonga (Ravaja and Kivikonga, 2008).

Three questionnaires are used in the experiment, with

one of them being split into two parts.

The first questionnaire used is the Immersion

questionnaire presented by Cheng et al. based on their

improvement upon the hierarchical model presented by

Cairns et al. (Cheng et al., 2015; Cairns et al. 2006).

The questionnaire was chosen, as it can be used to

measure the likeliness to be in each of the individual

Immersion levels, making it useful to compare Flow

with Total Immersion to test the theorized model

presented in 3.1. In order to track the participant’s

movement within the different Immersion levels, the

Immersion questionnaire is asked every three minutes

during the recording. However, test runs proved that

the Immersion questionnaire was too long at 17 bullet

points. Due to this, the quality of the responses given

by participants deteriorated. The questionnaire was

split into one immersive tendency part with ten

questions that is asked at the beginning of the

assessment phase, and one iterative part with seven

questions that is asked every three minutes. The

questions were chosen based on their contribution to

determining which state players are in, and the

questions with the most contribution for each

dimension were put into the iterative questionnaire.

The Flow questionnaire used in the experiment is

the Short Flow Scale Questionnaire developed by

Rheinberg et al. (Rheinberg et al., 2003) It was

chosen due to its low amount of ten questions. While

it was originally designed for use with sports, its

method of measuring Flow for activities is formulated

in a general-purpose sense. During the experiment,

social factors of online games are not taken into

account, meaning the social component the

GameFlow questionnaire adds to the original Flow

model can be ignored for this setup. The Flow Short

Scale questionnaire is asked every six minutes, along

with every second elicitation of the Immersion

questionnaire.

Finally, the third questionnaire used in the

assessment phase is the Game Experience

Questionnaire (IJsselsteijn, de Kort and Poels, 2013).

It is used as a support questionnaire alongside the

other two questionnaires. It is not focused on Flow or

Immersion but asks a wide range of questions about

how the participant felt during the game session. By

gathering more general info about the player

experience and linking it with the Flow/Immersion

data measured, new insights can be provided in what

experiences facilitate a higher Flow or Immersion

state. The questionnaire is asked once after the video

playback of the game footage has concluded.

An overview of the different phases is presented

in figure 2.

Figure 2: Phases of the experiment.

4 RESULTS

4.1 Preparing the Physiological Data

When working with GSR values from participants,

the first step is normalizing the data in a way results

from different participants can be compared reliably.

Normalization is performed by treating values of the

skin conductance measurement as a percentage of the

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participant's minimum and maximum value (Mandryk,

2008; Lykken and Venables, 1971). The normalization

equation follows to:

After this normalization, the calculation becomes a

regular correlation analysis.

Evaluations regarding ECG focus on calculating

heart rate changes. For this reason, both heart rate

changes and heart rate standard deviation are

calculated from the signal. Heart rate is calculated

using the time between two consecutive maximums in

the signal. This can be achieved by comparing sum-

differentiated peaks and threshold detection. Based on

(Pan and Tompkins, 1985).

Based on previous studies, the most interesting

aspect of eye tracking is the number of fixation points.

The input data are gaze points of the player, in order to

remove noise and get the final amount of fixation

points, a spatial threshold is used as outlined in the

thesis presented by Olsson (Olsson, 2007).

Emotion recognition data is based on the web cam

footage recorded during the gaming session. First, for

every second of the video, a cropped image of the

participant’s face is generated. This cropped image is

scaled to 256x256 and used as input for a pretrained

Convolutional Neural Network that recognizes

emotions in cropped face images (Levi and Hassner,

2015). As the web cam footage features players

frontally, this CNN delivers good results for the data

presented. The output of the net are probabilities for the

seven states anger, disgust, fear, happiness, neutral,

sadness and surprisedness.

4.2 Model Analysis Approach

The original assumption of the Flow/Immersion model

presented in this paper is that Flow and Immersion are

distinct concepts, and that Flow is considered the final

state of the Immersion hierarchy. From this definition,

it is assumed that Flow and the Total Immersion state

strongly correlate. This result can be proven by

correlation analysis between the Flow questionnaire

results and the Immersion questionnaire results. For

this correlation analysis, Spearman rank correlation is

used. When correlating questionnaire results, one

problem that comes up during correlation analysis is

that, they can only produce discrete results. This makes

Pearson analysis impossible if questionnaire results do

not follow a normal distribution (Sullivan and Artino,

2013). For this reason, Spearman analysis is chosen for

the correlation analysis. For each Flow questionnaire,

a normalized Flow value describing the likelihood for

the participant to be in Flow is calculated. This Flow

value is compared to the values of the three Immersion

levels calculated from the corresponding Immersion

questionnaire.

4.3 Physiological Analysis Approach

The first test when checking how physiological

measurements can be used to determine Flow and

Immersion is to look at the direct correlation between

the measurements taken and the values calculated by

the Immersion and Flow questionnaires. The metrics

used in this correlation are the ones described in section

4.1. Based on the raw correlation results, further steps

can be taken to measure Immersion and Flow using

physiological metrics.

First, these metrics are directly correlated to the

Flow and Immersion questionnaire results using the

Spearman correlation method explained in section 4.2.

These coefficients may still be too low to use them as

standalone indicators for measuring Flow, however,

their existence may help towards finding future

connections.

The first advanced technique used to try and link

Flow with physiological measurements is fuzzy logic.

Mandryk and Atkins took a similar approach in their

measurement of arousal and valence using GSR, ECG

and EMG values (Mandryk and Atkins, 2007). Using

fuzzy logic rules, new values are created using the

measured metrics. For example, excitement is defined

as high GSR AND high HR, meaning the minimum of

both values. Using this approach, a set of rules is

defined and correlated with Immersion and Flow. The

main difficulty with this technique is coming up with

useful rules based on the raw correlation results. If a

correlation between physical measurements and

Immersion exists, it may be harder to find than having

an expert define a set of rules.

The next idea is to build a classifier via the data that

was gathered in the experiment. Using deep learning, a

relation can be found between its input features and the

two classes called Flow/non-Flow, which are separated

by a threshold value based on the questionnaire value.

For this purpose, physiological data is generated for

every second of the experiment that was measured.

Flow and Immersion are extrapolated over the intervals

they cover, three minutes and six minutes, respectively.

The next step becomes selecting the input features used

in the net. For GSR, these features are the mean, the

standard deviation, the maximum, the minimum, the

maximum ratio, the minimum ratio, the mean of

negative values and the ratio of negative values. These

values are calculated for the first and second order

Conducting an Experiment for Validating the Combined Model of Immersion and Flow

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derivatives as well. For heart rate, the mean and

standard deviation are calculated for the signal itself

and its first and second order derivatives. Eye tracking

is represented with its amount of fixation points, as

well as a heat map of fixation points and how often they

have been visited. The final set of features analyzed is

the cropped face image of the participant’s web cam

recording.

5 CRITICAL ANALYSIS

5.1 Internal Validity

There are several factors that need to be taken into

consideration when regarding the internal validity of

the study performed. As the study was short in length,

with randomly selected participants, effects that

usually occur in studies running over a longer period

do not appear. Maturation, the changes in participants

over time, Mortality, participants dropping out during

the study, and repeated testing, influences when

repeating the test multiple times, can be ruled out as

threats to internal validity. As the study took place over

four months, history might provide a threat to internal

validity. External influence that changed over time is

the weather, which was hot in summer, but less so in

autumn. Measures were taken to reduce the influence

outside weather has on the experiment via heat

regulation of the room the experiment was performed

in. Instrumentation was kept consistent during the

experiment. Fixes to the experiment setup were made

based on test runs performed before the real study took

place.

Participants were not filtered and are self-selected.

As such, there might be a bias towards people who

enjoy playing video games, and who have time to

participate in the study. Selection interaction between

participants informing other potential participants of

the experiment has happened, but as all participants

received the same information before the experiment

started, this does not influence the result of the study.

As participant selection was not chosen based on the

observed variables, Immersion and Flow, Statistical

Regression does not become a problem either.

As participants have no way of knowing their own

physiological measurements during the experiment,

they are unable to fill out questionnaires with a

meaningful bias either in favor of the hypothesis or

against it. Participants are not informed of the goal of

the study to further link Flow and Immersion, so they

do not introduce any bias when filling out both Immer-

sion and Flow questionnaires at the same time.

5.2 External Validity

External validity refers to how generalizable the results

found in the study are. The experiment presented in this

paper puts a strong focus on making its result

transferable to a large number of situations. Games are

self-selected by participants, erasing the problem of

results presented in this paper being valid only for a

game or a genre of games. The games chosen by the

participants were spread across different game genres.

Results also transfer well to the general population, as

participants were selected randomly. However, as they

are self-selected, they may introduce a bias towards

people interested in games.

The effects of the experiment environment were

attempted to be kept to a minimum. Of course, the

experiment computer differs from the personal

computer participants use at home, but having

participants bring along their own gaming hardware

would have increased the experiment duration and

effort unreasonably. External influencing factors, such

as noise or irritating lighting were avoided. Participants

wear either full size headphones or in-ear headphones,

based on their preferences. This helps block out noise,

so results in the Immersion experiment may not

transfer well to situations in which these quiet, non-

disturbing conditions cannot be achieved.

6 CONCLUSIONS

In the previous chapters, a study for linking

physiological measurements and Flow/Immersion is

presented. Preliminary results suggest that those

physiological measurements show no direct correlation

to Immersion levels or Flow. A more complex system

for evaluating these physiological measurements is

needed in order to gather meaningful results. The deep

learning approach presented in chapter four promises

to deliver useful results based on the input features

selected.

In section 3.1, the theory that Flow and Immersion

are linked is presented, which states that Flow is

observed along with Total Immersion, making it the

optimal experience of an activity compared to the sub-

optimal experience of an activity provided by the

hierarchical Immersion model. Preliminary results

support this theory. However, more in-depth

correlation analysis is needed to make further

statements. Future focus of this research will be put on

analysing both the model and the physiological data

that was gathered with the help of the deep learning

strategy presented in chapter 4.

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