added user specific metrics

thesis/sections/background.tex

@@ -154,6 +154,27 @@ detecting the slight temperature rise that follows ovulation.
 Advances in wearable technology have further enabled continuous and automated temperature monitoring,
 improving accessibility and usability~\cite{alexander_fertilitatsmonitoring_2014, luo_detection_2020, yu_tracking_2022}.
 
+\subsubsection{Use Cases of Fertility Prediction}\label{subsubsec:use_cases_of_ovulation_prediction}
+The prediction of ovulation and corresponding fertility within a menstrual cycle serves two distinct use cases.
+Specifically, we will focus on \emph{natural family planning} (NFP), which includes preventing and achieving pregnancy.
+Individuals aiming to avoid pregnancy identify fertile days to abstain from intercourse,
+whereas those seeking pregnancy aim to focus intercourse around days with the highest fertility probability.
+
+Both use cases revolve around accurately predicting ovulation.
+However, the implications of prediction errors differ significantly.
+A false-positive prediction indicates high fertility despite actual fertility being low or nonexistent,
+whereas a false-negative prediction implies low fertility when fertility is actually high.
+
+For women aiming to avoid pregnancy, minimizing false-negative predictions is crucial due to the risk of unintended pregnancy.
+Although false-positives may lead to unnecessary abstinence, this outcome is generally considered less severe.
+Consequently, prediction algorithms should be conservative, erring on the side of higher fertility estimates to prioritize safety.
+
+Conversely, for women aiming to conceive, false-positive predictions could misdirect efforts toward incorrect cycle days,
+causing frustration or delays.
+False-negatives have fewer negative consequences.
+Therefore, algorithms for this group should prefer cautious fertility estimates,
+reducing the risk of misdirected effort.
+
 \subsection{Data Source and Characteristics}\label{subsec:data_background}
 This study is based on a dataset collected from users of the \emph{OvulaRing}~\cite{noauthor_ovularing_nodate},
 an intravaginal wearable sensor developed by VivoSensMedical GmbH, located in Leipzig, Germany~\cite{noauthor_vivosens_nodate}.
@@ -532,7 +553,6 @@ including time-series forecasting and biomedical modeling~\cite{wu_deep_nodate,z
 Its ability to model complex, long-range dependencies without recurrence makes it well-suited to domains like biomedical time-series,
 where signals are often irregular and span diverse temporal resolutions.
 
-
 \subsubsection{Convolutional Layers as Temporal Feature Extractors}
 For high-resolution time-series data, the input dimensionality can become large,
 especially in models like Transformers that process the entire sequence in parallel.