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Updated: May 24, 2025

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Exploring Random Forest Machine Learning for Fetal Movement Detection using Abdominal Acceleration and Angular Rate

Lucy Spicher, Carrie Bell, Xun Huan

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 5, 2025
    PubMed
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    Accurate fetal movement detection using wearable sensors and machine learning (ML) is crucial for fetal wellbeing. Combining acceleration and angular rate data significantly improved ML model performance for detecting fetal movements.

    Area of Science:

    • Biomedical Engineering
    • Maternal-Fetal Medicine
    • Signal Processing

    Background:

    • Reduced fetal movement is a critical indicator of potential adverse perinatal outcomes.
    • Improved fetal movement monitoring is essential for enhanced clinical decision-making.
    • Wearable sensors and machine learning (ML) offer promising avenues for accurate fetal movement detection.

    Purpose of the Study:

    • To train and validate ML models for fetal movement detection.
    • To explore the utility of angular rate data, in addition to acceleration, for ML model training.
    • To compare the performance of ML models using acceleration features, angular rate features, and combined features.

    Main Methods:

    • Ten pregnant participants were enrolled in the study.

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  • Four abdominal inertial measurement units (IMUs) and one chest reference sensor were utilized.
  • Three random forest classifiers were trained using acceleration, angular rate, and combined feature sets.

    • Main Results:

    • All trained ML models demonstrated good classification performance, with Area Under the Receiver Operating Characteristic Curve (AUROC) ranging from 0.70 to 0.77.
    • The model incorporating both acceleration and angular rate features exhibited a superior Positive Predictive Value (PPV) compared to models using single feature sets.
    • This suggests that the combined feature set possesses enhanced discriminative power for fetal movement detection.

    Conclusions:

    • ML models trained on wearable sensor data can effectively detect fetal movements.
    • Integrating angular rate data alongside acceleration data improves the accuracy and reliability of fetal movement detection.
    • This approach holds potential for advancing fetal wellbeing monitoring and clinical decision support.