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<p>This study applies machine learning to identify menstrual cycle phases using physiological signals recorded from a wrist-worn device. These signals include skin temperature, electrodermal activity (EDA), interbeat interval (IBI), and heart rate (HR), and were collected without requiring participant input. Data from 65 cycles across 18 subjects were analyzed, and multiple classifiers including random forest (RF) models were trained to classify the phases. Using a leave-last-cycle-out approach, and features from non-overlapping fixed-size windows, the RF model achieved 87% accuracy and an area under the receiver operating characteristic curve (AUC-ROC) of 0.96 when classifying three phases (period, ovulation, and luteal). For daily phase tracking using a sliding window, the RF model achieved 68% accuracy and an AUC-ROC of 0.77 when classifying four phases (period, follicular, ovulation, luteal). While these results highlight the potential of wrist-based physiological signals to enable automated phase tracking, reduce the burden of self-reporting, and improve access to cycle tracking solutions, further validation is needed to enhance the results.</p>

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Machine learning-based menstrual phase identification using wearable device data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月13日

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  • Grentina Kilungeja,
  • Krystal Graham,
  • Xudong Liu,
  • Mona Nasseri

摘要

This study applies machine learning to identify menstrual cycle phases using physiological signals recorded from a wrist-worn device. These signals include skin temperature, electrodermal activity (EDA), interbeat interval (IBI), and heart rate (HR), and were collected without requiring participant input. Data from 65 cycles across 18 subjects were analyzed, and multiple classifiers including random forest (RF) models were trained to classify the phases. Using a leave-last-cycle-out approach, and features from non-overlapping fixed-size windows, the RF model achieved 87% accuracy and an area under the receiver operating characteristic curve (AUC-ROC) of 0.96 when classifying three phases (period, ovulation, and luteal). For daily phase tracking using a sliding window, the RF model achieved 68% accuracy and an AUC-ROC of 0.77 when classifying four phases (period, follicular, ovulation, luteal). While these results highlight the potential of wrist-based physiological signals to enable automated phase tracking, reduce the burden of self-reporting, and improve access to cycle tracking solutions, further validation is needed to enhance the results.