A MENTAL HEALTH TRACKER APP USING FLUTTER
AND FIREBASE
A DESIGN PROJECT - I REPORT
Submitted by
SAIRISHI R (21137068)
ABHISHEK TIMOTHY (21137009)
Under the guidance of
Dr. ANTONIDOSS A
Associate Professor
in partial fulfillment for the award of the degree of
BACHELOR OF TECHNOLOGY
in
COMPUTER SCIENCE AND ENGINEERING
HINDUSTAN INSTITUTE OF TECHNOLOGY AND SCIENCE
CHENNAI - 603 103
MAY 2023
BONAFIDE CERTIFICATE
Certified that this design project I report Mental Health tracker app using
Flutter and Firebase is the bonafide work SAIRISHI R (21137068), K.
ABHISHEK TIMOTHY (21137009) who carried out the design project
work under my supervision during the academic year 2022-2023.
Dr. ANTONIDOSS A
Associate Professor
(Supervisor)
INTERNAL EXAMINER
EXTERNAL EXAMINER
Name:
Name:
Designation:
Design Project I Viva - voce conducted on
Designation:
TABLE OF CONTENTS
CHAPTER
NO.
Acknowledgement
Abstract
List of Figures
List of Abbreviations
1
2
3
vii
viii
xi
x
INTRODUCTION
1
1.1
Overview
1
1.2
Motivation for the project
1
1.3
1.4
1.5
Problem Definition and Scenarios
SIH Problem and description
Organization of the report
1
1.6
Summary
3
2
LITERATURE REVIEW
4
2.1
2.2
2.6
Introduction
Literature review
Conclusion/Gap
4
4
5
PROJECT DESCRIPTION
7
3.1
Objective of the Design Project Work
7
3.2
Existing System
7
3.3
3.4
4
Proposed System
Benefits of Proposed System
8
9
SYSTEM DESIGN
9
4.1
9
Architecture Diagram
5
PROJECT REQUIREMENTS
5.1 Hardware and Software Specification
5.2 Summary
14
14
15
6.
MODULE DESCRIPTION
6.1 Modules
6.2 Module 1: Face Recognition
6.3 Module 2: Gesture Recognition
6.4 Module 3: Integration of face
And gesture recognition
6.5 Summary
16
16
17
19
7.
IMPLEMENTATION
23
8.
RESULT IN ANALYSIS
25
9.
CONCLUSION AND FUTURE WORK
9.1 Conclusion
9.2 Future Work
9.3 Summary
30
30
30
31
10.
INDIVIDUAL TEAM MEMBER
REPORT
10.1 Individual Objective
10.2 Role of the Team Members
10.3 Contribution of Team Members
10.4 Summary
32
=
32
2
32
32
33
REFERENCES
34
22
APPENDIX A: SAMPLE SCREEN
APPENDIX B: SAMPLE CODE
APPENDIX C: PLAGIARISM REPORT
APPENDIX E: TEAM DETAILS
ACKNOWLEDGEMENT
First and foremost, we would like to thank ALMIGHTY who has provided
us the strength to do justice to our work and contribute our best to it.
We wish to express our deep sense of gratitude from the bottom of our
hearts to our guide Dr. ANTONIDOSS A, Associate Professor,
Computer Science, and Engineering, for his motivating discussions,
overwhelming
suggestions,
ingenious
encouragement,
invaluable
supervision, and exemplary guidance throughout this design project work.
We thank the management of the HINDUSTAN INSTITUTE OF
TECHNOLOGY AND SCIENCE for providing us with the necessary
facilities and support required for the successful completion of the design
project.
As a final word, we would like to thank every individual who has been a
source of support and encouragement and helped us to achieve our goal
and complete our design project work successfully.
ABSTRACT
Mental health is an important aspect of overall well-being and has a significant
impact on an individual's quality of life. However, it can be challenging to keep
track of one's mental health and identify patterns and triggers that affect it. To
address this issue, a mental health tracker app was developed. The mental health
tracker app uses machine learning algorithms to analyze user data and provide
personalized insights and recommendations for improving mental health. The
app allows users to track their mood, sleep, exercise, and other lifestyle factors
that affect mental health. The app also includes mindfulness exercises and
cognitive behavioral therapy (CBT) techniques to help users manage their
mental health.
The app was developed using a user-centered design approach and underwent
extensive user testing to ensure that it is easy to use, engaging, and effective in
improving mental health outcomes. The app is available on both iOS and
Android platforms and has received positive feedback from users.
Overall, the mental health tracker app provides a user-friendly and accessible
tool for individuals to monitor and improve their mental health. The app has the
potential to improve mental health outcomes and contribute to the overall wellbeing of users.
LIST OF FIGURES
FIGURE
NO.
TITLE
PAGE
NO.
INTRODUCTION
Machine Learning (ML) is a subfield of artificial intelligence (AI) that involves the
development of algorithms that enable computers to learn and improve from data, without
being explicitly programmed. In other words, ML algorithms can analyze large
sets of data, identify patterns and relationships, and use this information to make
predictions or decisions.
Mental illness is a health problem that undoubtedly impacts the emotions,
reasoning, and social interaction of a person. These issues have shown that mental
illness gives serious consequences across societies and demands new strategies
for prevention and intervention. To accomplish these strategies, early detection
of mental health is an essential procedure. Medical predictive analytics will
reform the healthcare field broadly as discussed by Miner et al Mental illness is
usually diagnosed based on the individual self-report that requires questionnaires
designed for the detection of specific patterns of feeling or social interactions.
With proper care and treatment, many individuals will hopefully be able to
recover from mental illness or emotional disorder.
Machine learning is a technique that aims to construct systems that can improve
through experience by using advanced statistical and probabilistic techniques. It
is believed to be a significantly useful tool to help in predicting mental health. It
is allowing many researchers to acquire important information from the data,
provide personalized experiences, and develop automated intelligent systems.
The widely used algorithms in the field of machine learning such as support
vector machines, random forests, and artificial neural networks have been utilized
to forecast and categorize future events.
Supervised learning in machine learning is the most widely applied approach in
many types of research, studies, and experiments, especially in predicting illness
in the medical field. In supervised learning, the terms, attributes, and values
should be reflected in all data instances. More precisely, supervised learning is a
classification technique using structured training data. Meanwhile, unsupervised
learning does not need supervision to predict. The main goal of unsupervised
learning is handling data without supervision. It is very limited for the researchers
to apply unsupervised learning methods in the clinical field.
In this paper, the main objective is to provide a systematic literature review,
critical review, and summary of the machine learning techniques that are being
used to predict, diagnose, and identify mental health problems. Moreover, this
paper will propose future avenues for research on this topic. It would also give
attention to the challenges and limitations of applying machine learning
techniques in this area. Besides that, potential opportunities and gaps in this field
for future research will be discussed. Hence, this paper will contribute to the state
of the art in the form of a systematic literature review concerning the machine
learning techniques applied in predicting mental health problems. This paper
hence contributes a critical summary and potential research directions that could
assist researchers to gain knowledge about the methods and applications of big
data in the mental health fields.
Motivation For the Project
Mental health is essential to overall health and well-being. Good mental health allows
individuals to lead happy, fulfilling lives, manage stress, build positive relationships, and
make meaningful contributions to society. On the other hand, poor mental health can lead to a
range of negative outcomes, including impaired functioning, reduced quality of life, and
increased risk of physical health problems.
The motivation behind designing a project focused on saving mental health is to address the
growing need for mental health support and services. Mental health conditions are common
and can have a significant impact on individuals and society as a whole, including reduced
productivity, increased healthcare costs, and decreased quality of life. Additionally, mental
health conditions are often stigmatized and may prevent individuals from seeking the help
they need.
Problem Definition and Scenarios
The prevalence of mental health conditions is increasing globally, and there is a growing need
for accessible and effective mental health support. Traditional mental health services may be
expensive, hard to access, or stigmatized, preventing individuals from seeking the help they
need. Mental health apps can provide a solution by offering convenient, accessible, and
confidential support for individuals with mental health conditions.
CHAPTER 3
LITERATURE REVIEW
Introduction
This chapter examines the numerous papers that have been published up to this
point., as well as the project details that are supplied and addressed in length in
the analysis of the article.
Literature Review
Towards the Development of a Mobile Application to Evaluate
Mental Health
Jorge A. Solís-Galván, Sodel Vázquez-Reyes(B), Margarita Martínez-Fierro, Perla VelascoElizondo, Idalia Garza-Veloz, and Claudia Caldera-Villalobos
The authors of this systematic literature review explored the development of a
mobile application to evaluate mental health. The goal of the project was to
identify existing studies that have used mobile applications to assess mental
health and to evaluate the feasibility and effectiveness of these applications.
The authors conducted a search of relevant databases, including PubMed,
Scopus, and Web of Science, to identify relevant studies published between
2010 and 2020. They included studies that developed mobile applications for
mental health assessment, regardless of the specific mental health conditions
being evaluated.
After reviewing the literature, the authors identified 31 studies that met their
inclusion criteria. The studies evaluated a range of mental health conditions,
including depression, anxiety, bipolar disorder, and schizophrenia. The mobile
applications used a variety of assessment methods, including self-report
questionnaires, cognitive tests, and physiological measures.
Overall, the authors found that mobile applications for mental health assessment
showed promise in terms of feasibility and effectiveness. The applications were
generally well-accepted by users and demonstrated good psychometric
properties. However, the authors noted that there were limitations to the current
literature, including a lack of diversity in study populations and a need for
further validation studies.
The authors concluded that the development of mobile applications for mental
health assessment is a promising area for future research. They suggest that
future studies should focus on validating the effectiveness of these applications,
as well as addressing issues of accessibility and acceptability for diverse
populations. Ultimately, the development of effective mobile applications for
mental health assessment could improve access to mental health care and
promote early intervention and treatment for mental health conditions.
Research Trends on Mobile Mental Health Application for
General Population: A Scoping Review
Won Ju Hwang, Ji Sun Ha, and Mi Jeong Kim
The authors of this scoping review examined the research trends on mobile
mental health applications for the general population. The goal of the project
was to identify the current state of research in this area and to highlight areas for
future research.
The authors conducted a search of relevant databases, including PubMed,
Scopus, and Web of Science, to identify relevant studies published between
2010 and 2019. They included studies that evaluated mobile mental health
applications for the general population, regardless of the specific mental health
conditions being addressed.
After reviewing the literature, the authors identified 87 studies that met their
inclusion criteria. The studies evaluated a range of mobile mental health
applications, including those focused on mindfulness, stress reduction, and
mood tracking.
The authors found that there is a growing interest in the use of mobile mental
health applications for the general population. They noted that the majority of
the studies were focused on mindfulness and stress reduction, suggesting that
these are areas of particular interest for researchers and developers.
The authors also identified several areas for future research, including the need
for a more rigorous evaluation of mobile mental health applications, the need
for studies that incorporate user feedback, and the need for studies that focus on
underrepresented populations.
Overall, the authors concluded that mobile mental health applications have the
potential to be a valuable tool for promoting mental health and well-being for
the general population. However, they noted that further research is needed to
fully understand their effectiveness and to address issues related to user
engagement and accessibility.
Mobile Applications in Mood Disorders and Mental Health:
Systematic Search in Apple App Store and Google Play Store and
Review of the Literature
by Sophie Eis, Oriol Solà-Morales, Andrea Duarte-Díaz, Josep Vidal-Alaball, Lilisbeth
Perestelo-Pérez, Noemí Robles and Carme Carrion
In this project, the authors conducted a systematic search in the Apple App Store
and Google Play Store to identify mobile applications related to mood disorders
and mental health. They also reviewed the literature to evaluate the
effectiveness and user satisfaction of these applications.
The authors identified a total of 2,209 applications related to mood disorders
and mental health in the app stores. After applying inclusion and exclusion
criteria, they selected 57 applications for further evaluation. These applications
covered a range of mental health conditions, including depression, anxiety,
bipolar disorder, and stress.
The authors found that the majority of the applications were focused on selfhelp and mood tracking. Some applications included features such as guided
meditation, cognitive-behavioral therapy exercises, and social support networks.
The authors noted that many of the applications lacked scientific evidence to
support their effectiveness.
To evaluate the effectiveness and user satisfaction of the applications, the
authors reviewed 28 studies that evaluated the use of mobile applications for
mood disorders and mental health. The studies included randomized controlled
trials, quasi-experimental studies, and observational studies.
Overall, the authors found that the use of mobile applications for mood
disorders and mental health showed promise in terms of improving mood and
reducing symptoms. However, they noted that the studies were often limited by
small sample sizes, a lack of follow-up data, and a lack of methodological rigor.
The authors concluded that mobile applications have the potential to be a
valuable tool for promoting mental health and well-being. However, they
emphasized the need for further research to evaluate their effectiveness and to
ensure that they are developed in a way that is evidence-based and usercentered.
Conclusion
CHAPTER 3
Project Description
3.1 Objective of the Project Work
 Objective 1
 Objective 2
 Objective 3
Existing system
There are several existing mental health tracker apps available in the market that
use machine learning algorithms and other advanced technologies to provide
personalized insights and recommendations for improving mental health. Some
of the popular mental health tracker apps are:
1. Moodfit - This app allows users to track their mood, sleep, exercise, and other
lifestyle factors that affect mental health. It uses machine learning algorithms to
analyze user data and provide personalized insights and recommendations for
improving mental health.
2. Pacifica - Pacifica is a mental health app that provides users with tools for
managing stress, anxiety, and depression. It uses CBT techniques, mindfulness
exercises, and other evidence-based interventions to improve mental health
outcomes.
3. Woebot - Woebot is a mental health chatbot that provides users with daily
check-ins and personalized conversations based on CBT techniques. It uses
machine learning algorithms to analyze user data and provide personalized
interventions.
4. Sanvello - Sanvello is a mental health app that provides users with tools for
managing stress, anxiety, and depression. It uses CBT techniques, mindfulness
exercises, and other evidence-based interventions to improve mental health
outcomes.
5. MoodMission - MoodMission is a mental health app that provides users with daily
missions to help them improve their mental health. It uses CBT techniques, mindfulness
exercises, and other evidence-based interventions to improve mental health outcomes.
These mental health tracker apps have received positive feedback from users and have the
potential to improve mental health outcomes. However, there is still a need for further
research and development in this area to ensure that mental health apps are effective and
accessible to all users.
Different algorithms can be used for mental health tracking and analysis, depending on the
specific application and purpose of the mental health tracker. Here are a few examples:
Bayesian networks - These are probabilistic graphical models that can be used for risk
assessment and diagnosis of mental health. Bayesian networks can help identify patterns and
correlations in symptoms and behaviors, and predict the likelihood of certain outcomes based
on the data.
Machine learning algorithms - These are used in mental health tracking to analyze large
datasets and identify patterns and trends. For example, supervised learning algorithms such as
decision trees, support vector machines, and neural networks can be used to predict mental
health outcomes based on input data, while unsupervised learning algorithms such as
clustering and association rules can help identify patterns in the data.
Natural Language Processing (NLP) - This is a subset of machine learning that focuses on
analyzing human language. NLP algorithms can be used in mental health tracking to analyze
text data, such as social media posts, to identify patterns in language use that may be
indicative of mental health issues.
Sentiment analysis - This is a type of NLP algorithm that can be used to analyze the
sentiment and emotional tone of text data. Sentiment analysis can help identify patterns in
language use that may be indicative of depression, anxiety, or other mental health issues.
These are just a few examples of algorithms that can be used for mental health tracking and
analysis. The choice of algorithm depends on the specific needs and goals of the mental
health tracker, as well as the available data and resources.
Proposed solution
Our proposed solution for mental health tracking is to use sentiment analysis
and lexicons. Sentiment analysis is a type of natural language processing that
involves analyzing the sentiment and emotional tone of text data. Lexicons are
dictionaries or lists of words that are categorized according to their sentiment or
emotional tone. By combining sentiment analysis and lexicons, it is possible to
identify patterns in language use that may be indicative of mental health issues.
The proposed solution involves collecting data from social media platforms,
such as Twitter or Facebook, and using sentiment analysis to analyze the
language used in the posts. Lexicons can be used to categorize the words in the
posts according to their emotional tone, such as positive, negative, or neutral.
By analyzing the frequency and distribution of these categories, it is possible to
identify patterns in language use that may be indicative of mental health issues.
For example, studies have shown that people with depression tend to use more
negative language in their social media posts, such as words related to sadness,
loneliness, and hopelessness. By identifying these patterns in language use, it
may be possible to detect signs of depression or other mental health issues at an
early stage.
The proposed solution also involves developing a user-friendly interface for the
mental health tracker, where users can input their social media handles or
upload text data for analysis. The interface could provide users with feedback
on their language use and offer suggestions for improving their mental health,
such as connecting them with mental health resources or encouraging them to
seek professional help if necessary.
Overall, using sentiment analysis and lexicons for mental health tracking has the
potential to provide a valuable tool for the early detection and intervention of
mental health issues. However, there are also ethical and privacy considerations
to take into account, such as ensuring the confidentiality and security of user
data and avoiding stigmatization or discrimination based on mental health
status.
Benefits of using the proposed system
There are several benefits of using sentiment analysis and lexicons in a mental
health app:
1. Early detection: By analyzing the language used in social media posts or
other text data, sentiment analysis can help identify signs of mental health
issues at an early stage. This can enable timely intervention and prevent the
condition from worsening.
2. Objective assessment: Sentiment analysis provides an objective way to assess
the emotional tone of language use, which can be more reliable and consistent
than self-reported measures.
3. Customization: By using lexicons to categorize words according to their
emotional tone, sentiment analysis can provide a more personalized assessment
of mental health. This can enable the app to offer tailored recommendations for
improving mental health, based on the user's specific needs and challenges.
4. Real-time feedback: By providing real-time feedback on language use, the
app can help users become more aware of their mental health and encourage
them to take action to improve it.
5. Accessibility: Using social media data or other text data for analysis can make mental
health tracking more accessible and affordable for a wider range of people, regardless of their
location or financial resources.
Overall, using sentiment analysis and lexicons in a mental health app can provide a valuable
tool for promoting early detection and intervention for mental health issues, and for
supporting users in improving their mental well-being.
CHAPTER 4
SYSTEM DESIGN
4.1 SYSTEM ARCHITECTURE
The above diagram depicts the system's architecture, which is used to follow the model's flow
and design for the prediction of the mental state of the user
1. Front-end: This component would include the user interface and user-facing features of the
app. It would allow users to interact with the app, input data, and view their mental health
status. This component would typically be developed using mobile app development
technologies such as React Native or Flutter.
2. Back-end: This component would include the server-side infrastructure that manages the
data and logic of the app. It would handle user authentication, data storage, and retrieval, as
well as any machine learning models that are used for sentiment analysis. The
back end would also be responsible for communicating with third-party APIs
that provide access to lexicons and other natural language processing tools.
3. Natural Language Processing (NLP) API: This component would provide the
NLP capabilities needed to perform sentiment analysis on user-generated text. It
would include access to a sentiment lexicon or sentiment analysis algorithm that
can identify and categorize words according to their emotional tone. The NLP
API would also provide features such as stemming and lemmatization to
improve the accuracy of sentiment analysis.
4. Database: This component would store user data, including text data that is
analyzed using sentiment analysis. The database would also store user
preferences and settings, as well as any insights or recommendations generated
by the app.
5. Analytics Engine: This component would process the sentiment analysis
results and generate insights and recommendations based on the user's
emotional tone. It could also use machine learning algorithms to identify
patterns and trends in user data and generate personalized recommendations for
improving mental health.
6. External Integrations: The app could integrate with other mental health
resources, such as mindfulness meditation apps or cognitive behavioral therapy
programs, to provide users with a more holistic approach to mental health.
These integrations could be managed through APIs or other forms of data
exchange.
Overall, this system architecture would enable a mental health app to provide
users with valuable insights into their emotional state and provide personalized
recommendations for improving mental health based on their unique needs and
challenges.
CHAPTER 5
PROJECT REQUIREMENTS
5.1
Hardware and Software Specification
To make a mental health tracker app using Flutter and Firebase, the following
hardware and software requirements are recommended:
Hardware Requirements:
Windows 11 operating system:
In October 2021, Microsoft launched Windows 11, the most recent major
release of its Windows NT operating system.
It's a free update to Windows 10 (2015), and it's available for Windows 10
devices that meet the new Windows 11 system requirements
RAM: At least 8 GB of RAM is recommended.
Storage: At least 10 GB of free disk space is recommended.
Software Requirements:
Flutter SDK: Download and install the latest version of the Flutter SDK from
the official Flutter website.
Android Studio: Download and install the latest version of Android Studio from
the official Android Studio website.
Dart Plugin: Install the Dart plugin in Android Studio.
Firebase Account: Create a Firebase account and set up a new Firebase project
for the mental health tracker app.
Additional Libraries and Packages:
Flutter-fire: A set of Firebase plugins for Flutter that help you use Firebase
services.
sentiment_dart: A Dart library for performing sentiment analysis.
Summary
This chapter discusses the software used in this project, and the technology
applied to design the mental health tracker app. The software used for coding
the sign language detection system was selected and used after a lot of research
and deliberation.
CHAPTER 6
IMPLEMENTATION
The implementation for the development of a mental health app using Flutter
and Firebase can be divided into several steps:
1. Design: The first step is to design the user interface and user experience
(UI/UX) of the app. This involves creating wireframes and prototypes that show
how the app will look and function. We tried our best to make the UI of the app
more interactive and user-friendly
2. Authentication: The app does have a login system that ensures only
authorized users can access it. Firebase Authentication can be used for this
purpose.
3. Database: We use Firebase to store user data such as user profile information,
mood tracking data, and other relevant data. Firebase Realtime Database or
Cloud Firestore can be used for this purpose. We also used private cloud storage
for user’s privacy
4. Mood tracking: The app allows users to track their moods over time. The app
can use sentiment analysis and lexicons to analyze the user's mood based on
their input.
5. Notifications: The app have precise notification system that reminds users to
track their moods and provides feedback based on their moods.
6. Dashboard: The app does have a dashboard that displays the user's mood
history and other relevant information.
7. Machine learning: Machine learning algorithms is be used to analyse user
data and provide personalized feedback and recommendations.
8. Testing and deployment: when the app is developed, we tested extensively to
ensure it works as intended. The app can then be deployed to app stores for
users to download and use.
Overall, the implementation of a mental health app using Flutter and Firebase
requires careful planning, design, and development to ensure a user-friendly and
effective app that helps individuals track and improve their mental health.
CHAPTER 8
RESULT & ANALYSIS
8.1 Results Obtained
The above figure depicts the real-time use of the app such as the ability to track
moods, journaling, meditation exercises, or access to mental health resources.
CHAPTER 10
INDIVIDUAL TEAM MEMBER
REPORT
10.1 Individual Objective
Name: Sairishi R
The objective was to plan the phases and set up a proper roadmap for the project
in the first place. Also, plan and design the flow, time planner, and
implementation. Contributing to the research paper.
Name: Abhishek Timothy
The objective was to review the available concepts and literature and refer to
documents on various sites. To do the testing part and identify the errors and
various corner cases. Contribute to the team report and presentation.
10.2 Role of the Team Members
Name: Sairishi R
Role: Programmer and Project Management
Name: Abhishek Timothy
Role: Programmer and Documenting work.
10.3 Contribution of the Team Members
Name: Sairishi R
Contribution: Project Management; Coding for sentiment analysis using
machine learning, Team Report.
Name: Abhishek Timothy
Contribution: Designed the app UI; Testing; Documentation; Programming;
Team Presentation; Finding publication resources.
10.4 Summary
This chapter talks about the role and individual contribution of each member,
and how every input added contributed significantly to the overall success of the
project. The combined efforts of every member were equally important in every
aspect and helped achieve the target set out for the project.
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