started related work section

thesis/sections/related_work.tex

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 %! Author = alex
 %! Date = 3/6/25
 
+
 \section{Related Work}\label{sec:related_work}
+There has been a variety of works in menstrual cycle analysis.
+
+\subsection{Body Temperature}
+
+\citeauthor{luo_detection_2020} used an in-ear wearable device that measured ear canal temperature every five minutes during sleep~\cite{luo_detection_2020}.
+They trained a Hidden Markov Model (HMM) to classify each data point into either a high- or low-temperature state,
+augmented with biorhythm information from the user.
+
+After filtering, the final dataset consisted of 64 cycles, each with at least 40\% data availability and at least one self-reported ovulation day, as determined by a hormone test kit.
+However, no information was provided regarding the distribution of cycle lengths or ovulation timing.
+
+Ovulation detection was considered successful if the predicted day fell within ±3 days of the self-reported value.
+The method achieved a sensitivity of 92.31\%, with 54.69\% of ovulation days detected exactly on the reported date.
+
+The model, however, relies on strong assumptions of phase regularity and fixed transition durations—such as a standard
+luteal phase length of 14 days—which do not reflect real-world variability.
+Moreover, HMM predictions are conditioned on either a previous cycle or population-level averages, limiting performance in irregular or anovulatory cycles.
+As a result, the approach performs well on regular, well-behaved data but lacks robustness in more diverse, real-world scenarios.
+
+
+In~\citeyear{yu_tracking_2022}, \citeauthor{yu_tracking_2022} employed an in-ear thermometer along with a fitness tracker
+for heart rate monitoring to predict the fertile window using machine learning~\cite{yu_tracking_2022}.
+Their study population consisted of 153 women, divided into a regular cycle group ($n = 103$) and an irregular group ($n = 50$).
+After filtering, 89 and 25 participants remained in the regular and irregular groups, respectively.
+
+The prediction task was to determine whether a given day falls within the fertile window, based on data from the preceding days.
+A second model was trained to predict whether menstruation occurs on a given day, again using preceding data as input.
+
+For the fertile window prediction, the model achieved a sensitivity of 69.30\% in the regular group and 21.00\% in the irregular group.
+For menstruation prediction, the model detected 70.70\% of menstruation days in the regular group and 36.30\% in the irregular group.
+
+These results indicate that the model performs reasonably well for individuals with regular cycles,
+but struggles significantly in the presence of menstrual irregularity—particularly in detecting the fertile window.
+
+
+Several studies have questioned the utility of BBT (Basal Body Temperature) for reliable ovulation prediction.
+For example \citeauthor{bauman_basal_1981} concluded, that BBT is not a robust standalone marker due to its
+retrospective nature and sensitivity to external factors and thus must be used with extreme caution clinical or research evaluations~\cite{bauman_basal_1981}
+
+\subsection{Alternative Pyhysiological Signals}
+
+In addition to temperature, other physiological signals have been explored for ovulation and cycle phase prediction.
+
+For example,\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.
+Ground truth labels were derived from self-reported data and daily ovulation predictor kits.
+
+The dataset used in their study was restricted to healthy, non-pregnant women aged 18–34 with natural menstrual cycles.
+Anovulatory cycles were excluded, along with users meeting the following criteria:
+\begin{itemize}
+    \item Use of medications, including hormonal contraceptives
+    \item Travel across time zones
+    \item Night-shift work
+    \item Pregnancy within the past year
+    \item Sleep disorders
+    \item Tobacco use
+\end{itemize}
+
+They further subdivided participants into groups with low and high sleep variability, called HVST and LVST respectively.
+As a result, the study population and cycle types were highly regular and homogeneous,
+with 30 cycles (18 women) in the HVST and 26 cycles (16 women) in the LVST category.
+No information about the distribution of both lengths or ovulation dates was given.
+
+Based on selected features—such as minimum sleeping heart rate and single-point basal body temperature (BBT) after
+waking—they report classification accuracies between 0.843 and 0.864, depending on the feature subset,
+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.
+
+When models are trained on highly constrained datasets with predictable patterns and clear ovulatory signals,
+complex methods often show limited gains over naive or rule-based approaches—as will be demonstrated in Section~\ref{sec:methodology}.
 
 
 %parts: traditional methods, ml approaches, tft in medical time series or other time series