hopefully final commit

thesis/sections/introduction.tex

@@ -3,6 +3,14 @@
 
 
 \section{Introduction}\label{sec:introduction}
+Reproductive health and fertility prediction are of increasing importance at both the individual and societal level.
+Globally, maternal age at first pregnancy continues to rise, driven by social and economic factors~\cite{sauer_reproduction_2015}.
+This trend increases the prevalence of cycle irregularity and subfertility, amplifying the need for reliable,
+non-invasive methods of fertility tracking.
+At the same time, many individuals seek alternatives to hormonal contraception, fueling the growth of so-called
+\emph{FemTech} applications for digital health.
+Accurate fertility prediction thus carries implications not only for individual reproductive autonomy,
+but also for public health, demographic trends, and the development of safe, data-driven fertility support tools.
 
 While textbooks often describe a menstrual cycle as lasting 28 to 30 days with ovulation around day 14~\cite{owen_physiology_nodate},
 such regularity is the exception rather than the rule~\cite{munster_length_1992, bull_real-world_2019}.
@@ -31,9 +39,14 @@ others suggest its predictive value for ovulation~\cite{sato_novel_2024, royston
 However, most existing studies rely on small, idealized datasets that exclude cycles with irregular lengths or late ovulation.
 While such restrictions simplify the prediction task and yield high accuracy, they give a misleading impression of real-world model performance.
 It is thus not yet fully clear whether temperature can reliably be used as a predictive marker for ovulation or fertility.
-
 Additionally, other studies have focused on peripheral temperature measurements of skin or in-ear temperature,
 which are subject to many sources of noise that can significantly affect the quality of the resulting predictions.
+
+To date, no study has systematically evaluated machine learning architectures on a large-scale,
+high-resolution dataset of intravaginal core body temperature across both regular and irregular cycles.
+Such an evaluation is crucial to determine whether temperature-based models can provide robust predictions
+under real-world variability, rather than only on highly regular, curated subsets of data.
+
 Intravaginal temperature reflects true core body temperature and offers higher resolution and stability,
 as it is largely unaffected by external circumstances.
 This allows for more reliable detection of subtle thermal shifts associated with ovulation, especially in irregular cycles.
@@ -44,8 +57,10 @@ an intravaginal wearable device that continuously measures core body temperature
 The objective is to develop a machine learning model that performs reliably across diverse cycle types, including irregular ones.
 To this end, we compare a set of time series-based machine learning architectures and evaluate their performance for
 NFP and contraception.
-Finally, we demonstrate that high predictive accuracy on highly regular, curated datasets, as commonly reported in prior work,
-may overestimate real-world applicability, since such datasets tend to favor even simple, rule-based approaches.
+
+This thesis addresses this gap by providing the first large-scale, systematic comparison of LSTM and
+Transformer architectures on high-resolution intravaginal temperature data.
+It evaluates robustness across diverse cycle types and demonstrates their practical relevance for contraception and NFP\@.
 
 The main research objective is to evaluate the fundamental feasibility of predicting fertility and
 ovulation from body temperature data using machine learning,
@@ -60,4 +75,4 @@ across real-world variability in cycle patterns and user needs:
     \item To compare prediction performance across regular and irregular cycles to assess how cycle variability affects feasibility.
     \item To evaluate model outputs in the context of practical use cases,
     such as contraception and natural family planning, using task-specific evaluation criteria.
-\end{itemize}
+\end{itemize}