LogMe: An Application for Generating Logs in Immersive Interactions

for UX Evaluation

Victor Klisman, Luan Souza Marques, Jo

ao Pedro de Lima, Leonardo Marques, Genildo Gomes,

Tayana Conte and Bruno Gadelha

Institute of Computing, Federal University of Amazonas, Manaus, AM, Brazil

Keywords:

User Experience, Immersive Experience, Log, Interaction, UX, Log Application.

Abstract:

Immersive applications aim to stimulate interactions between the physical, virtual, and simulated world. Such

applications stand out for transforming the public, often limited to a passive spectator, into an active participant

in an event. Assessing the experience promoted by immersive applications is a challenge, as it involves

difﬁculties inherent in the context of immersion. As an example, the user cannot be interrupted when he is

immersed in the experience. In this sense, a non-intrusive way of collecting data is needed that can indicate

whether the experience was positive or negative. The methods available in the literature are dependent on the

spoken and observed reports of users during the interaction, but they are not applicable in all dimensions of

evaluation and contexts of interaction, such as immersive events. The use of methods such as log capture can

assist in the investigation of user interactions. In this work, we propose an application capable of recording logs

from mobile devices while the user interacts with a certain immersive application. This will allow interactions

to be recorded as they actually are, facilitating the investigation of the user’s feelings when performing a

certain task.

1 INTRODUCTION

Technologies that promote the sensation of immer-

sion have been used to modify the way users inter-

act and engage in different immersive environments.

As an example of these technologies, we can men-

tion Augmented Reality (AR), Virtual R eality (VR),

and Mixed Reality (MR). These technologies tend to

eliminate or reduce the boundaries between the phys-

ical, virtual, and simulated worlds (Suh and Prophet,

2018).

Immersive environments should provide a good

user experience (UX)(Tcha-Tokey et al., 2017). Such

experience is a subjective concept, dependent on the

context and dynamics of the interaction(Law et al.,

2009). The user experience goes beyond performing

tasks in an application and focuses on hedonic aspects

of use, such as fun and pleasure. For this, evaluations

that focus on aspects beyond usability and their instru-

mental values must be applied(Hassenzahl, 2018).

In order to perform UX assessments, UX evalu-

ators use several techniques and methods available

in the literature. Rivero and Conte (2017) present

a mapping of these techniques and organize them

in seven categories: scales, such as Likert scale or

semantic differentials; forms, with open questions,

with free expression, and/or restricted, as multiple

choice; checklist; interviews, with questions pre-

deﬁned by the moderators; exploration with acquaint-

ances, where there is an exchange of users exper-

iences and thoughts about what is being evaluated;

probes, materials such as multimedia and objects to

involve users in the evaluation process; experience

sampling and controlled user monitoring, psycho-

physiological responses through coupled sensors.

When considering contexts of immersive exper-

iences, traditional UX techniques may not be feas-

ible to use, as the evaluation must be carried out in

such a way that the user experience is not interrup-

ted (Marques et al., 2020). In this sense, ways of

capturing user experience data in a non-intrusive way,

without interrupting the experience or disturbing the

user, should be considered.

Through continuous records of user data (logs), it

is possible to capture more information about the con-

text of the interaction and the user experience while

having an immersive experience (Menezes and Non-

necke, 2014). However, the vast majority of applica-

tions do not provide user logs or make them publicly

available for analysis. The sensors logs can help to

Klisman, V., Marques, L., Pedro de Lima, J., Marques, L., Gomes, G., Conte, T. and Gadelha, B.

LogMe: An Application for Generating Logs in Immersive Interactions for UX Evaluation.

DOI: 10.5220/0010460105490556

In Proceedings of the 23rd International Conference on Enterprise Information Systems (ICEIS 2021) - Volume 2, pages 549-556

ISBN: 978-989-758-509-8

549

infer the actual behavior of the user since the inter-

action is recorded exactly at the moment when it oc-

curs (Menezes and Nonnecke, 2014). But the absence

of this information by most applications demands the

use of intrusive UX assessment methods, requiring

direct contact between the moderator and the user.

In this sense, this paper presents LogMe, an ap-

plication that provides data inherent to the context,

capturing and storing logs of immersive applications

without interfering in the user experience. For this,

geolocation data, motion sensors, and external envir-

onment, such as luminosity, are collected to assist in

the analysis of the user behavior.

2 BACKGROUND

As new technologies emerge, organizers of entertain-

ment events seek to provide different ways of interac-

tion and engagement with the public at their events.

This industry branch is gaining prominence and is

constantly expanding (Martins et al., 2020).

An example of this expansion is reﬂected in the

creation of applications to increase audience engage-

ment or make it more active in entertainment events,

such as StageCast (Funkquist, 2019) and Echobo

(Lee and Freeman, 2013). Another example is the

Bumbometer (Martins et al., 2020), which consists

of a competitive and collaborative game for crowds.

The Bumbometer allows two opposing teams to play

matches through the continuous movement of their

mobile devices. A screen displays two thermomet-

ers that indicate each teams’ engagement (Figure 2

in Section 4.1). The speed of the thermometer is

deﬁned by adding the acceleration points (x, y, and

z) provided by the device’s accelerometer sensor. The

team that manages to get the thermometer to the top

wins.

For this type of technology to fulﬁll the role of

encouraging user interaction, it is necessary to ana-

lyze the experiences they provide to users. In this

sense, UX evaluations are a way to analyze these ex-

periences lived by the public. As stated earlier, tra-

ditional evaluation techniques of UX are not the best

alternative in the context of immersive experiences,

as they interrupt the user during immersion. On the

other hand, UX evaluations based on indirect obser-

vation can be a viable alternative (Marques et al.,

2020). They can be automatically done by captur-

ing the user’s logs, while the user interacts with the

application, allowing the understanding of their inter-

action and engagement without direct interference.

Hussain et al. (Hussain et al., 2018) proposed the

Lean UX Platform, a platform for capturing and ana-

lyzing user data of a given software at the moment of

interaction. The platform is focused on recording and

processing user data. The Lean UX Platform uses sev-

eral ways to collect interaction logs, such as facial ex-

pression analysis, eye tracking, video and voice cap-

ture, and electroencephalography (EEG). When ap-

plying a test on another platform, the authors realized

that capturing information in a synchronized way al-

lows a better interpretation of the data. And the use

of several methods and devices provide more accurate

information about the user when interacting with the

product.

The web also adheres to log capture as it is a

powerful resource for business research and market,

beneﬁting a series of applications(Preece et al., 2015).

The UX-Log tool uses such logs to infer and recre-

ate user behavior after an experiment (Menezes and

Nonnecke, 2014). A test was carried out where 10 us-

ability experts, watched the user experience recreated

by UX-Log and evaluated the use of the tool. The

experts were able to understand the user and his in-

tentions. The authors stated that the tool was efﬁcient

in recreating user behavior from logs.

Based on the aforementioned works, we can say

that interaction logs allow the analysis of the context,

attitudes, and emotions of the users during their ex-

perience. Logs analysis allows UX experts in car-

rying out UX evaluations without interfering in the

user’s experience. In this work, we propose an applic-

ation for capturing logs from mobile devices in order

to enable UX experts to evaluate UX on different mo-

bile applications. Our proposal differs from the others

presented in this section in that it focuses on logs of

immersive experiences.

3 METHOD

In order to propose a way of evaluating UX in im-

mersive experiences in a non-intrusive manner, we ad-

opted a research method based on 4 stages as follows:

(i) exploratory, where we reviewed literature in order

to understand both UX evaluation practices and im-

mersive technologies; (ii) experimentation, where we

proposed LogMe, a tool for capturing logs from dif-

ferent sensors in smartphones; (iii) Pilot Study, where

we conducted a pilot test; and ﬁnally the (iv) Feasibil-

ity Study, corresponding to the feasibility study on the

use of LogMe.

In the (i) exploratory stage, we aimed at under-

standing UX concepts and techniques, and immers-

ive applications. During this stage we analyzed some

papers that dealt with the exploration of interaction

logs and data cataloging. A snippet of this review

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is presented in Section 2. These papers gave us the

background to understand the ways of capturing logs

and their application for analyzing users’ behavior.

Also, they aimed at a better interpretation of the ﬁnal

experience of those who used a particular application

(Menezes and Nonnecke, 2014).

After reviewing the literature, we proceeded to the

(ii) experimentation stage, which started with a com-

parative study between technologies for developing

mobile applications. During this study, we decided

on the most suitable technology for log collection in

mobile devices. After that, we began with the devel-

opment of LogMe in an incremental-iterative way. In

each iteration, we made some improvements and ad-

ded more sensors’ data. Section 4 details the develop-

ment of LogMe.

Before proceeding with the feasibility studies, the

(iii) Pilot Study stage took place, which consists of

conducting a pilot test with four simultaneous users

through video conferencing. The purpose of the

test was to verify the usability and efﬁciency of the

LogMe data capture, while the participants used a

speciﬁc interaction app that stimulates the movement

of the device. The pilot test can be seen in more detail

in Section 4.1.

In (iv) Feasibility Study stage, we conducted a

feasibility study to verify the use of LogMe in prac-

tice. We aimed at verifying whether the quality of the

logs recorded by LogMe were as good as those of the

Bumbometer itself. For such comparison, the analysis

was performed based on the data collected by both

LogMe and Bumbometer apps. The study was con-

ducted entirely online via video conference and had

a total of 13 simultaneous participants. Section 4.2

details this study.

4 LogMe

Smartphone apps can mediate many immersive ex-

periences. But, in most apps, we do not have ac-

cess to log ﬁles with information about user interac-

tions. This fact makes the evaluation of the UX during

immersive experiences in a non-intrusive way more

challenging. This scenario motivates the development

of LogMe.

LogMe is a mobile app developed for collect-

ing and registering sensor and contextual data from

mobile devices. LogMe reads data from a set of

sensors comprising: accelerometer, gyroscope, mag-

netometer, and light sensor. Besides sensors data,

LogMe also reads and registers the location and bat-

tery level as contextual data.

The choice of these sensors is justiﬁed by the fact

that through them it is possible to infer the behavior

of users, through body gestures and the way they ma-

nipulate the device. Thus, it is possible to associate

such behaviors with sentimental characteristics such

as stress, dissatisfaction, and ﬂuidity. It is also pos-

sible to record contextual information of the user, for

example, it is possible to collect the signal strength of

the wi-ﬁ network in relation to the distance from the

router, understand when the user is moving or how

the device reacts to the battery charge level. This

last point is emphasized due to some functionalities of

mobile devices being reduced or disabled when they

are at low load levels.

The context of the use of the immersive exper-

ience under evaluation must be taken into account

when analyzing the logs provided by LogMe. Apps

used in entertainment events, for example, can gener-

ate a lot of movement data and this does not charac-

terize stress or anger on the part of the user, but joy or

enthusiasm.

The usage of LogMe is simple. The user must

activate the app, set the time interval for collecting

sensors and contextual data and press the ”Start” but-

ton (”Iniciar”, in Portuguese) (Figure 1 on the left).

After pressing the start button, LogMe keeps running

in background and registers sensors data while the

user interacts normally with the smartphone during an

immersive experience. After the experience, the user

activates LogMe and press the ”Stop” button to ﬁnish

the data collection.

During data collection, LogMe records the data

in a text-based log ﬁle. LogMe captures information

from sensors and components that can be classiﬁed

into 4 groups: Motion Sensors, capable of capturing

any movement of the device; Position Sensors, allows

you to recognize the device’s position in the physical

space; Environment Sensors, allows you to capture

information external to the device; Location Com-

ponents, allows you to collect the geographic loca-

tion of the device; In addition to these, the timestamp

provides the date and time for each line in the log ﬁle.

An example of a generated ﬁle is also presented in

Figure 1.

Depending on the context in which the applica-

tion is used, it is more feasible to use a certain capture

time. For this, the option of conﬁguring the time in-

terval to capture sensor data was added. In this way,

the user can choose intervals from 0.1 to 2.0 seconds.

This allows greater control and makes it more ﬂexible

during use combined with other applications.

LogMe enables the sharing of the generated log

ﬁle. In the case of a UX evaluation, the user can

send their ﬁle to UX evaluators. This ﬁle-sharing is

performed manually by the user after the log genera-

LogMe: An Application for Generating Logs in Immersive Interactions for UX Evaluation

551

Figure 1: On the left, a LogMe home screen. On the right, an example log ﬁle.

tion has ended, through an option that appears on the

screen, where the user determines where to send the

log ﬁle.

4.1 Pilot Study

After developing LogMe, we conducted a pilot study

in order to verify if the generated log is comparable

with a native log of an application that provides im-

mersive experiences. The pilot test aimed to improve

the data collection plans and procedures that will be

followed to validate the study. For this, the steps that

comprise this test are described below.

4.1.1 Preparation

In order to conduct the study, we had to select an app

that provides an immersive experience. The require-

ments for this selection were: (1) the interaction in

the app must be motion-based; (2) the app must be

used in an on-line setting (given the scenario of social

isolation due to the COVID-19 pandemic) and; (3) the

app must provide a log of user interaction to serve as

basis for comparison with LogMe generated logs.

Based on these requirements, we chose Bum-

bometer as an app for conducting the pilot study.

Bumbometer, as mentioned in Section 2, is an app

designed to be used during an entertainment event

where two teams compete by shaking their smart-

phones to see who is the most animated. Bum-

bometer provides an immersive experience during en-

tertainment events engaging its users in the battle.

Bumbometer works entirely on-line which enables its

use in a non-presencial environment. Bumbometer’s

backend application generates an log with the inter-

action of its users. This turns possible to compare the

data with log provided by LogMe.

The pilot study was conducted with four parti-

cipants that installed both Bumbometer and LogMe

apps in their smartphones. Due to the context of

the Covid-19 pandemic (Roser et al., 2020), it was

conducted entirely on-line via video conference us-

ing Google Meet. Google Meet enabled the sharing

of the Bumbometer’s feedback screen, displaying the

termomethers as we can see in Figure 2.

4.1.2 Execution

With the applications installed on their devices, the

participants started LogMe and were instructed to ini-

tially set the interval time of 0.1 seconds for cap-

turing sensors data. Soon after, they were able to

start recording the logs and leave the application run-

ning in the background. That done, they started the

Bumbometer app and choose their favorite team (Blue

Team - Boi Caprichoso or Red Team - Boi Garantido).

The feedback screen with the teams’ thermomet-

ers was presented and the round started. Thus, the

participants were able to shake their smartphones to

take their team to victory. Normally, interaction with

the Bumbometer takes an average time of 20 seconds.

After the round, the participants stopped the record

of logs in LogMe and shared the generated log ﬁle

to the researcher that conducted the study. Figure 2

illustates the feedback screen shared in Google Meet

during the study.

Figure 2: Pilot test being performed remotely.

Four more rounds were performed with the following

intervals deﬁned in LogMe: 0.5 seconds, 1 second,

1.5 seconds, and 2 seconds. At the end of all rounds,

the participants shared all LogMe generated ﬁles with

the researcher that conducted the study. We also

collected Bumbometer’s ﬁve log ﬁles related to the

ﬁve rounds during the study. In the next section we

present the analysis of the log ﬁles from both LogMe

and Bumbometer.

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4.1.3 Results Analysis

The structure of Bumbometer’s native log is different

from LogMe generated logs. The data is divided by

teams, where each individual accelerometer value is

summed to form the team’s acceleration value. Bel-

low the team information, the log presents informa-

tion about each user comprising: id of the user, ac-

celerometer value, and GPS location values. Figure 3

shows an example of Bumbometer’s native log ﬁle for

one round of the study.

Different from Bumbometer’s log ﬁles, LogMe

generated ﬁle stores data for just one user. So, to

analyze and compare both log ﬁles, we needed to

collect LogMe generated logs of each participant of

the study. After that, we had to organize the logs

from all the participants in the same structure as Bum-

bometer’s native log. Table 1 shows LogMe ﬁles’ data

organized for comparison with Bumbometer’s native

log ﬁle.

Figure 3: Example of log generated by the Bumbometer

application.

Table 1: Organization of LogMe data for comparison with

Bumbometer logs.

X aceller-

ation

Y aceller-

ation

Z aceller-

ation

Total In-

dividual

Team

Total

Blue Team

participant

4.298.388 3.996.705 6.078.269 14.373.362

participant

2.473.210 3.335.123 12.919.112 18.727.445 33.100.807

Red Team

participant

0.683.738 5.584.887 8.002.572 13.587.459

participant

0.689.530 2.861.071 9.907.207 12.768.278 23.355.737

For each second counted in LogMe, the accelerometer

data (x, y, z axes) for each user were added and later

added to the total value of the chosen team, resulting

in a single value. This value was compared with the

opposing team. The time interval for the execution of

each test was also considered.

Among the 5 rounds performed, we considered

only rounds 2 and 3 (intervals of 0.5 and 1 second)

for the comparison with the records generated by the

Bumbometer app. This was due to the interval times

for rounds 2 and 3 are the closest to the Bumbometer’s

native log, obtaining a more consistent result.

The test with the capture interval of 1 second had a

total interaction time of 15 seconds. In comparison

with the results of the Bumbometer’s native log, 11

results were obtained with an accuracy of 78.57%.

The ﬁnal result of the winning team was conﬁrmed by

the logs collected from LogMe. The variation in team

acceleration can be seen in Figure 4 and the winner of

the round in Table 2.

Figure 4: Graph of the acceleration variation during the in-

teraction between the teams in round 3 (1-second interval).

Table 2: Total acceleration for each team and the winner

within 1 second.

Team Blue 1.935.595.156

Team Red 732.001.560

Round Winner Team Blue

For the test with the interval time set to 0.5 seconds,

the LogMe data had to be considered in pairs. Every

two lines of 0.5 seconds the data were added to match

the time of 1 second in the Bumbometer’s log. The in-

teraction lasted 11 seconds and obtained 7 coincident

values and the conﬁrmation of the winner by LogMe

(seen in Table 3).

Table 3: Total acceleration for each team and the winner

within 0.5 second.

Team Blue 1.467.652.366

Team Red 2.362.262.779

Round Winner Team Red

It is considered that, due to the ﬂexibility of cap-

ture time and individual registration, LogMe logs can

bring more efﬁcient results when analyzing the inter-

action. This ﬂexibility allows verifying the user who

collaborated and engaged the most (Table 4). In addi-

tion, based on individual data, it is possible to ﬁnd the

winning team in a clearer, more objective way and

without loss of information. The Bumbometer also

obtains this information, but calculates the accelera-

tion value over a very large interval and still passes it

LogMe: An Application for Generating Logs in Immersive Interactions for UX Evaluation

553

to a database via the internet, which can be lost during

this process.

Table 4: Total acceleration of participants and who contrib-

uted most during the round.

Team Blue Team Red

Participant 1 1.436.432.830 Participant 3 414.089.883

Participant 2 499.162.326 Participant 2 317.911.677

Biggest contributor: participant 1

LogMe’s utility stands out for collecting data from

other sensors present in the device, allowing to un-

derstand the context and mode of experience, engage-

ment, and interaction of each user more deeply. In

general, the pilot test met the expectations of the study

concerning ease of use and gaining information that

may have been compared to the logs of the applica-

tions to be evaluated.

4.2 Feasibility Study

In order to conﬁrm the results from the Pilot Study,

we conducted a feasibility study. This study aimed

to verify the feasibility of analyzing multiple LogMe

generated ﬁles from many users interacting in the

same immersive experience. So, in this study, we in-

vited 13 participants to engage and interact virtually

using Bumbometer. The study is detailed in the fol-

lowing sections.

4.2.1 Preparation

For this study, we elaborated an Informed Consent

Form (ICF) explaining the motivation and execution

process of the research highlighting the information

that would be collected from the participants. All par-

ticipantes digitally signed the ICF that was available

through the Google Forms platform

. As in the pi-

lot test, the study was carried out by videoconference

using the same interaction application.

The study started with a short presentation to the

participants contextualizing them and explaining the

functioning of the LogMe and Bumbometer apps.

The procedures of the study was also explained to the

participants.

As in the Pilot Study, the Bumbometer’s feedback

screen with each team’s thermometers was shared

with all participants in Google Meet. We also created

a group on a message exchange platform (Telegram

)

with all the participants to send their log ﬁles for ana-

lysis.

https://docs.google.com/forms

https://telegram.org/

4.2.2 Execution

In total, 13 people participated in this study. All

of them downloaded both LogMe and Bumbometer

apps. During the presentation about the study, we

answered all participant’s questions and doubts. We

also asked them to sign the ICF through an online

form link, where everyone agreed to use their data an-

onymously for scientiﬁc purposes.

After signing the ICF, the rounds were initiated

and carried out like the pilot test, Session 4.1. For

each round, the LogMe data logging interval varied

between 2 seconds for the ﬁrst round, 1.5 seconds for

the second round, 1 second for the third round and 0.5

for the fourth round. The Figure 5 shows the feedback

screen and the participants shaking their smartphones.

In the end, the individual logs generated by LogMe

were sent to a group on Telegram. These logs were

received for further analysis.

Figure 5: Test performed with 13 people remotely.

4.2.3 Results Analysis

Through the analysis of the logs, we could infer the

total of both individual and collective engagement

during the interactions. We could also observe in

which rounds the participants collaborated more and

which team was more engaged in the immersive ex-

perience. Figure 6 shows the total engagement for

each round. As we can see, the Blue team won rounds

1, 2 and 3. The Red Team won only the last round.

As in the Pilot Study, for comparison purposes,

we organized LogMe generated logs following the

same organizational structure of the data generated by

Bumbometer’s native-logs.

The third round had to be discarded from the ana-

lysis. The reason for that is that one of the parti-

cipants had trouble with sharing the log referring to

this round. Bumbometer’s native log has data from

this participant, but without the log from LogMe, the

comparison was not possible.

The comparison of the data returned by the Bum-

bometer, the adapted data from LogMe and the per-

centage of equivalence is shown in Table 5. It is

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Figure 6: Comparison table of the winners during the 4

rounds.

Table 5: Matching results between Bumbometer and

LogMe.

Round 1 Round 2 Round 4

Capture time 2 sec 1,5 sec 0,5 sec

Values returned

by the Bumbometer

15 14 14

Corresponding values

with LogMe

12 13 13

Accuracy 80% 92,85% 92,85%

possible to observe that the adaptation of the LogMe

data, in its great majority, becomes equivalent to the

data generated by the Bumbometer, achieving excel-

lent accuracy.

The study showed that the logs generated by

LogMe are as good as those generated by the evalu-

ated application. During the three valid rounds carried

out in this study, we obtained an average of 88.56%

of correct answers. This allowed us to verify that the

logs generated by LogMe could be used to correctly

determine the winner of all valid rounds (1,2 and 4).

5 DISCUSSIONS

The objective of this work is to investigate whether

LogMe is capable of producing useful data for the

evaluation of UX, specially on applications that

provides immersive experiences. As seen during the

study, it is feasible to use logs to assess user engage-

ment, interaction and collaboration. According to the

results obtained through the studies, we noticed that

the LogMe data compared to the Bumbometer, had

signiﬁcantly similar results, which can be veriﬁed in

the Session 4.2.

This allows us to state that LogMe provides in-

formation comparable to the information provided by

Bumbometer’s native log. Information on logs from

LogMe manages to be more accurate with less loss

of information. It is worth mentioning that the pres-

ence of more sensors available, allows loading de-

tailed information of the interaction, thus generating

more consistency in the inferences of users behavior

during immersive experiences. LogMe can also be

used in other contexts using different applications if

the interaction can be analyzed by logs of motion and

contextual sensors.

As explained in Marques (Marques et al., 2020),

some UX measures need the user’s direct opinion,

but there are indirect measures, such as engagement.

These measures can be found through LogMe since it

is possible to assess how much the user has actively

contributed to the interaction.

Therefore, LogMe can help UX evaluators to ad-

opt non-intrusive techniques as an alternative to tra-

ditional UX techniques. This is an important issue

given the context of immersive entertainment, where

the user experience cannot be interrupted. In this

scenario, the evaluation through traditional UX tech-

niques may not be feasible. In this sense, LogMe cap-

tures and provides data for the UX assessment to be

carried out indirectly.

About the limitations of LogMe, we can point

that the app was developed exclusively for Android

devices, limiting the reach of users. LogMe is still a

proof of concept. We developed it ﬁrstly for Android

devices because Android is the most used mobile op-

erating system (Alzaylaee et al., 2020). We intend to

develop an IOS version of LogMe, covering a wider

range of devices.

Another limitation of LogMe as to do with its log

sharing function. This function is not yet exclusive to

LogMe, that is, the user can choose the destination to

which he wants to make the generated logs available

(WhatsApp, Telegram, etc.). This can lead to sharing

errors and ﬁle loss. Subsequently, we intend to create

automated ﬁle management, where log ﬁles would be

uploaded to the cloud and stay accessible only for UX

evaluators, thus reducing the chance of errors in shar-

ing.

6 CONCLUSION AND FUTURE

WORK

Immersive applications are gaining ground in enter-

tainment events, and evaluating the user experience

(UX) while using the application without directly in-

terfering is a challenge. For that, we proposed an ap-

plication to capture data inherent to the context, cap-

able of generating interaction logs through the sensors

of mobile devices. And through the analysis of these

interaction logs, infer the user’s real behavior. Thus,

the LogMe application was developed and subjec-

ted to feasibility studies and comparison analyzes to

verify the efﬁciency of log captures.

LogMe: An Application for Generating Logs in Immersive Interactions for UX Evaluation

555

Through the results collected, LogMe proved to be

a satisfactory product, achieving an excellent similar-

ity rate in contrast to other applications, proving to be

efﬁcient and effective in capturing details of the user’s

behavior without interfering with their experience. It

also allows the understanding of user behavior, and

the extension of the utility to test any types of applic-

ations that require behavior inference.

As future work, we intend to expand the use of

the application by creating a multiplatform version of

LogMe, as well as adding support for more sensors,

registering the name of the application used in the

foreground, and allowing automatic sharing of the log

ﬁles. Besides, there is also a need to validate LogMe

together with other applications, since in this paper,

the data were compared only with an immersive ap-

plication.

ACKNOWLEDGMENT

This research, carried out within the scope of the

Samsung-UFAM Project for Education and Research

(SUPER), according to Article 48 of Decree no

6.008/2006(SUFRAMA), was funded by Samsung

Electronics of Amazonia Ltda., under the terms

of Federal Law no 8.387/1991, through agreement

001/2020, signed with Federal University of Amazo-

nas and FAEPI, Brazil. Also supported by Coordin-

ation for the Improvement of Higher Education Per-

sonnel - Brazil (CAPES) - Financing Code 001, CNPq

process 311494/2017-0, and Foundation for Research

Support of the State of Amazonas (FAPEAM) - POS-

GRAD and process 062.00150/2020.

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