further work on introduction

thesis/sections/related_work.tex

@@ -16,6 +16,8 @@ confirming that LH surges reliably indicate an imminent ovulation event.
 
 Despite their diagnostic value, many of these biomarkers are difficult to measure continuously and reliably in everyday settings,
 limiting their practicality for real-time or large-scale applications.
+Among the physiological indicators explored, body temperature has gained particular attention due to its accessibility
+and suitability for passive, continuous monitoring.
 
 \subsection{Temperature-Based Approaches}\label{subsec:temperature_based_approaches}
 Body temperature has emerged as a more accessible physiological signal for ovulation tracking,
@@ -34,7 +36,7 @@ In contrast, this study, along with several recent works, leverages continuous o
 This richer signal provides a more robust foundation for detecting ovulatory patterns and addresses many of the limitations historically associated with BBT-based methods.
 
 This was further supported by a study from \citeauthor{zhu_accuracy_2021}, who compared the accuracy and sensitivity of traditional BBT measurements with continuous skin temperature recordings from a wrist-worn device~\cite{zhu_accuracy_2021}.
-They found that continuous temperature measurements had significantly higher sensitivity in detecting ovulation, though at the cost of increased false positives and lower specificity.
+They found that continuous temperature measurements achieved higher sensitivity but at the cost of more false positives and reduced specificity.
 Importantly, the continuous data showed a greater average temperature difference between the follicular and luteal phases.
 The authors conclude that for women seeking to optimize their chances of conception, continuous temperature tracking offers measurable benefits—primarily due to improved phase delineation enabled by the richer signal.
 
@@ -43,33 +45,34 @@ who used an in-ear wearable device that measured ear canal temperature every fiv
 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 65 cycles, each with at least 40\% data availability and at least one self-reported ovulation day, as determined by a hormone test kit.
+After filtering, the final dataset consisted of 65 cycles, each with at least 40\% data availability and at least one self-reported ovulation day, as determined by a hormone test kit,
+a notable contrast to the 40,000 cycles analyzed in this study.
 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.
+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
+Building on this idea, \citeauthor{yu_tracking_2022} combined temperature with additional physiological signals to improve predictive performance.
+In~\citeyear{yu_tracking_2022}, they 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.
+They developed a probability function based on a changepoint analysis of the smoothed waveforms of the BBT and heart rate.
+A second function was developed in a similar way 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.
+These results indicate that the model performed well in regular cycles but struggled with irregularity, particularly in detecting the fertile window.
 
-In addition to academic research, several commercial products use temperature-based methods for fertility tracking,
+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}.
 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.
@@ -109,17 +112,15 @@ Anovulatory cycles were excluded, along with users meeting the following criteri
 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.
+No information about the distribution of both cycle 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.
 
-
-
 \paragraph{Summary:}
 While various physiological signals and modeling strategies have been explored for ovulation prediction,
-many existing studies are limited by small, highly selected datasets, assumptions of cycle regularity, or reliance on proprietary algorithms.
+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,
 data-driven modeling to better capture individual variability.