Comparative Analysis of Machine Learning Approaches for Fetal Movement Detection with Linear Acceleration and Angular Rate Signals

Comparative Analysis of Machine Learning Approaches for Fetal Movement Detection with Linear Acceleration and Angular Rate Signals

Reduced fetal movement (RFM) can indicate that a fetus is at risk, but current monitoring methods provide only a “snapshot in time” of fetal health and require trained clinicians in clinical settings. To improve antenatal care, there is a need for continuous, objective fetal movement monitoring syst...

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Bibliographic Details
Main Authors: Lucy Spicher, Carrie Bell, Kathleen H. Sienko, Xun Huan
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Sensors
Subjects:
bi-directional long short-term memory (BiLSTM)
convolutional neural network (CNN)
fetal monitoring
inertial measurement units (IMUs)
random forest (RF)
spectrogram
Online Access:https://www.mdpi.com/1424-8220/25/9/2944
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