%\documentclass[cs,phd,justified,12pt]{uccthesis}
\documentclass[cs,phd,12pt,final,justified]{uccthesis}

%\usepackage{ragged2e} %To justify the text!!
\usepackage{amssymb}
\usepackage{amsmath}
\usepackage{mathtools}
\usepackage{graphicx}
\usepackage{amsthm}
\usepackage{comment}
\usepackage{pdflscape}
\usepackage{rotating}
\usepackage{amsmath}
\usepackage{changepage}
\usepackage[hidelinks]{hyperref}
\usepackage[noend]{algorithmic}
\usepackage{algorithm}
\usepackage{xcolor}

% \usepackage{algorithm}
\usepackage{afterpage}

\newtheorem{theorem}{Theorem}[section]
\newtheorem{corollary}{Corollary}[theorem]
\newtheorem{lemma}[theorem]{Lemma}
\newtheorem{definition}[theorem]{Definition}
%\usepackage[utf8]{inputenc}
%\usepackage[english]{babel}

\usepackage{varwidth}
\usepackage{booktabs} % For formal tables
\usepackage{epsfig}
\usepackage{amsfonts}
\usepackage{array}
\usepackage[table, x11names, pdftex,dvipsnames]{xcolor}
\usepackage{rotating}
\usepackage{bbm}
\usepackage{tabularx}
\usepackage{color}
\usepackage{multirow}
\usepackage{url}
\usepackage{lscape}
\usepackage{subcaption}
\usepackage{xargs}

% Very useful abbrev
\def\prob{{\mathbb{P}}} % Probs
\def\norm{{\mathcal{N}}} % Normal
\def\real{{\mathbb{R}}} % Real numbers
\DeclareMathOperator*{\usup}{NEG-USER-SUPPORT}
\DeclareMathOperator*{\isup}{NEG-ITEM-SUPPORT}
\DeclareMathOperator*{\uvar}{USER-VARIANCE}
\DeclareMathOperator*{\ivar}{ITEM-VARIANCE}
\DeclareMathOperator*{\resample}{RESAMPLE}
\DeclareMathOperator*{\inject}{INJECT}
\DeclareMathOperator*{\fast}{FAST-RESAMPLE}
\DeclareMathOperator*{\ensemble}{ENSEMBLE}
\DeclareMathOperator*{\FunkSVD-CV}{EB-FunkSVD}
\DeclareMathOperator*{\Bias-CV}{EB-Linear}
\DeclareMathOperator*{\CPMF}{CPMF}
\DeclareMathOperator*{\BeMF}{BeMF}
\DeclareMathOperator*{\OrdRec}{OrdRec}
\DeclareMathOperator*{\divy}{div}

% Victor commands
\newcommand\ddfrac[2]{\frac{\displaystyle #1}{\displaystyle #2}}
\newcommand{\victor}[1]{\textbf{\color{magenta} *Victor: #1}}
\newcommand{\derek}[1]{\textbf{\color{brown} *Derek: #1}}
\newcommand{\db}[1]{\textbf{\color{green} **Derek: #1}}
\newcommand{\dgb}[1]{\textbf{\color{purple} **Derek: #1}}
\usepackage{tikz}
\usetikzlibrary{shapes, arrows}
\tikzstyle{block} = [rectangle, draw, text width=6em, text centered, rounded corners, minimum height=3em]
\tikzstyle{line} = [draw, -latex']
\usepackage[linesnumbered,ruled,vlined]{algorithm2e}
\usepackage{cleveref}
\setlength{\headheight}{46.48878pt}

% Acronyms
\usepackage[acronym]{glossaries}
\makeglossaries
\newacronym{ema}{EMA}{Ecological Momentary Assessment}
\newacronym{el}{EL}{Emotional Loneliness}

\newacronym{pam}{PAM}{Partition Around Medoids}
\newacronym{optics}{OPTICS}{Ordering Points To Identify the Clustering Structure}
\newacronym{idbscan}{IDBSCAN}{Incremental Density-based Spatial Clustering of Applications with Noise}

\newacronym{pca}{PCA}{Principal Component Analysis}
\newacronym{svm}{SVM}{Support Vector Machine}
\newacronym{xgboost}{XGBoost}{Extreme Gradient Boosting}
\newacronym{arm}{ARM}{Association Rule Mining}

\newacronym{gps}{GPS}{Global Positioning System}
\newacronym{ml}{ML}{Machine Learning}
\newacronym{panas}{PANAS}{Positive and Negative Affect Schedule}
\newacronym{prisma}{PRISMA}{Preferred Reporting Items for Systematic Reviews and Meta-Analyses}

\newacronym{phq}{PHQ}{Patient Health Questionnaire}
\newacronym{smote}{SMOTE}{Synthetic Minority Oversampling Method}
\newacronym{eps}{EPS}{epsilon}
\newacronym{MinPts}{MinPts}{Minimum Points}
\newacronym{or}{OR}{Odds Ratio}
\newacronym{evr}{EVR}{Explained Variance Ratio}

\newacronym{sl}{SL}{Social Loneliness}
\newacronym{ucla}{UCLA}{University of California, Los Angeles}
\newacronym{rapids}{RAPIDS}{Reproducible Analysis Pipeline for Data Streams}
\newacronym{globem}{GLOBEM}{Generalization of LOngitudinal BEhavior Modeling}
\newacronym{irb}{IRB}{Institutional Review Board}

\begin{document}
\title[Loneliness Detection\break Using Technology]{Loneliness Detection Using Technology}
\author{Malik Muhammad Qirtas}
\qualifications{}
\professor{Professor Utz Roedig}
\supervisor{Professor\ Dirk Pesch \\Professor Eleanor Bantry White}
% \sponsor{SFI}
\date{August 2024}
\maketitle

%\begin{dedication}
%Para Rafaela, Silmara e Marcos
%\end{dedication}

\begin{acknowledgements}

First, I thank my supervisor, Prof. Dirk Pesch, for his great support and guidance throughout my Ph.D. journey. His supervision, patience, and understanding over the past four years have been invaluable. This thesis would not have been possible without his mentorship and dedication to my success. Having Dirk as my supervisor was a privilege and an honour.

I am deeply grateful to my co-supervisor, Prof. Eleanor Bantry White, whose interdisciplinary approach was really helpful in broadening the scope and impact of my research. Her input, mentorship, and ideas for bridging computer science and social sciences concepts greatly improved my research. This was very important in shaping how my final thesis turned out.

I want to say thanks to Dr. Evi Zafeiridi, whose support throughout my research, particularly during our data collection efforts, was invaluable. Her willingness to lend an ear and offer help with daily challenges made a significant difference in my experience.

To my fellow colleagues and friends, your support, both direct and indirect, has been a source of encouragement and inspiration throughout this journey. I am grateful for the collaborative and supportive environment you all helped to create.

I want to acknowledge the important role of my mentor and former manager, Sadia Ashraf, in my intellectual growth. Her guidance and support have been instrumental in my career as a software developer and researcher. I am deeply grateful for the lasting impact she has had on both my professional and personal growth.

I would like to express my deepest gratitude to my parents for their unwavering love, support, and encouragement throughout my academic journey. Their belief in my abilities and constant guidance have been my foundation, providing me with the strength to pursue my goals even in the most challenging moments. This accomplishment would not have been possible without their sacrifices and endless support.

Finally, I am grateful to the ADVANCE CRT for this opportunity. I want to say thanks to the program coordinators Luis Gómez De Membrillera and James Duggan, who always helped me with every aspect and whenever I needed any help. Cheers!


\vspace{0.3cm}

\begin{flushright}
Malik Muhammad Qirtas

\vspace{-0.25cm}
Cork, August 2024
\end{flushright}

\end{acknowledgements}

\sloppy
\overfullrule=0pt


\begin{abstract}
Over the last decade, especially during the COVID-19 pandemic, loneliness has emerged as a global issue. Although loneliness is a common issue that most people experience at some stage in their lives, it can have harmful effects on both physical and mental health if this condition becomes chronic. To avoid the long-term effects, detecting it in the early stages is critical, which would lead to timely intervention and treatment. Modern smartphones and wearables are equipped with a wide range of sensors that can provide a vast amount of information on people's daily lives and behaviours in real time, which can help detect early signs of loneliness. Researchers collect extensive data on a user's daily life and behavioural patterns, including social activities, mobility patterns, communication, and activity data through sensors like accelerometers, heart rate sensors, microphones, and GPS and Bluetooth connectivity acting as proximity sensors. All these capabilities of smartphones and wearables have made them a powerful tool for monitoring users' health and well-being passively. Many studies have used passive sensing for loneliness detection in recent years, but these methods have severe limitations. The main issues are related to relying on generic models, and neglecting the dynamic, multifaceted nature of loneliness. This thesis made several contributions to address these critical gaps in the literature.

%First, we explored the use of federated learning for loneliness detection while preserving the privacy of sensitive users' data. Our findings show that centralized models performed slightly more accurately in detecting loneliness, but still, federated learning balances privacy and performance.

First, we presented an approach for personalized loneliness detection through the behavioural grouping of passive sensing data. We used unsupervised machine learning for this and then trained customized models for each identified group that outperformed generic models for loneliness detection. Building upon this work, we addressed its limitation of static approach for loneliness detection. So, we proposed an approach for dynamic loneliness detection that can adjust to users' changing behaviours. We also analyzed how users' behaviours changed throughout the study period and how these changes are linked with their loneliness levels. This contribution helps detect loneliness in its early stages and highlights the importance of recognizing individual differences in how loneliness manifests.

Previous research on loneliness with passive sensing data often analyzed loneliness as a single concept. This thesis explored the different types of loneliness and their behavioural manifestations. Mainly, we investigated how behavioural patterns can distinguish between social and emotional loneliness and what is the predictive power of these digital biomarkers to detect the specific type of loneliness in order to develop more targeted interventions. Research shows that loneliness and depression often co-occur, but their behavioural overlap wasn't well understood in terms of behavioural patterns extracted through passive sensing. We investigated the complex relationship between these two conditions and identified both overlapping and distinct behavioural markers captured through passive sensing along with the predictive power of these markers. This work helps us understand how they interact with each other, and this could lead to improved detection and intervention strategies for individuals experiencing these conditions.

\end{abstract}

\clearpage

\input{chapters/1 - introduction}
\input{chapters/2 - literature-review}
\input{chapters/3 - methodology}
\input{chapters/4 - personalising-loneliness-detection}
\input{chapters/5 - Evolving AI for Wellness}
\input{chapters/6 - Social and Emotional Loneliness}
\input{chapters/7 - Loneliness and Depression}
\input{chapters/8 - Conclusion}

\clearpage
\glsaddall
\printglossary[type=\acronymtype, nonumberlist]

\bibliography{bibliography}
% \nocite{*}

\appendix
\clearpage
\renewcommand{\thepage}{\Alph{chapter}\arabic{page}}
\setcounter{page}{1}
%\include{chapters/appendix_a}

\end{document}


