further work on discussion

thesis/sections/related_work.tex

@@ -4,6 +4,12 @@
 
 \section{Related Work}\label{sec:related_work}
 
+This section will introduce related work of both ovulation detection and ovulation prediction.
+First, we'll introduce early work on the detection of the ovulation based on biomarkers.
+Then, we'll show how others have used body temperature to predict ovulation and what their limitations are.
+Lastly, we will take a closer look at related work that uses other biomarkers as base, or as an addition to the body
+temperature for ovulation and fertility prediction.
+
 A variety of approaches have historically been explored for ovulation detection and prediction,
 ranging from hormonal assays to physiological signal tracking.
 
@@ -73,7 +79,8 @@ For menstruation prediction, the model detected 70.70\% of menstruation days in
 These results indicate that the model performed well in regular cycles but struggled with irregularity, particularly in detecting the fertile window.
 
 Complementing academic efforts, several commercial products have adopted temperature-based tracking,
-such as \textit{Ava}~\cite{sl_ava_nodate}, \textit{Daysy}~\cite{electronics_zykluscomputer_nodate} or \textit{Trackle}~\cite{noauthor_trackle_nodate}.
+such as \textit{Ava}~\cite{sl_ava_nodate}, \textit{Daysy}~\cite{electronics_zykluscomputer_nodate}, \textit{Trackle}~\cite{noauthor_trackle_nodate}
+or \textit{Natural Cycles}~\cite{noauthor_natural_nodate}.
 However, these products typically rely on proprietary algorithms, and no peer-reviewed publications are available detailing their methodology or performance.
 This lack of transparency limits their scientific evaluation and comparability.
 In contrast, the present study provides an open and data-driven approach to ovulation prediction based on continuous temperature data, aiming to contribute reproducible evidence to the field.
@@ -81,7 +88,7 @@ In contrast, the present study provides an open and data-driven approach to ovul
 \subsection{Other Physiological Signals}\label{subsec:other_physiolocical_signals}
 In addition to temperature, other physiological signals have been explored for ovulation and cycle phase prediction.
 
-As early as \citeyear{moreno_temporal_1988}, researchers investigated ovulation prediction based on the electrical resistance of salivary and vaginal secretions~\cite{moreno_temporal_1988}.
+As early as~\citeyear{moreno_temporal_1988}, researchers investigated ovulation prediction based on the electrical resistance of salivary and vaginal secretions~\cite{moreno_temporal_1988}.
 Their study analyzed 29 cycles from 11 women, with daily recordings of BBT, urinary LH, pelvic ultrasound, and ovulation predictor kit results.
 Participants were under the age of 35, had cycle lengths between 25 and 35 days, and had abstained from hormone therapies for at least two months prior to the study.
 
@@ -92,7 +99,7 @@ These findings suggest that electrical resistance is a strong physiological mark
 A related modern implementation is the commercial product \textit{kegg}~\cite{noauthor_kegg_nodate}, which measures the electrical resistance of cervical mucus.
 The device uses an undisclosed algorithm to estimate fertility status based on these readings, although no peer-reviewed validation studies are currently available.
 
-\citeauthor{masuda_machine_2025} developed a machine learning algorithm to classify phases of the menstrual cycle
+In~\citeyear{masuda_machine_2025}, \citeauthor{masuda_machine_2025} developed a machine learning algorithm to classify phases of the menstrual cycle
 (follicular vs. luteal) based on sleeping heart rate, as recorded by a fitness tracker~\cite{masuda_machine_2025}.
 They used an XGBoost classifier for this binary task and additionally performed ovulation day prediction,
 although the details of this task were not fully specified.
@@ -119,7 +126,7 @@ waking—they report classification accuracies between 0.843 and 0.864, dependin
 with very similar numbers for precision, recall, specificity and F1 score.
 Ovulation day prediction yielded an average absolute error between 3.6 and 4.1 days.
 
-\paragraph{Summary:}
+\subsection{Summary}\label{subsec:related_work_summary}
 While various physiological signals and modeling strategies have been explored for ovulation prediction,
 many existing studies are limited by small, highly selective datasets, assumptions of cycle regularity, or reliance on proprietary algorithms.
 The present work extends prior approaches by leveraging a large, heterogeneous dataset of real-world cycles and applying transparent,