Multi-modal detection of fetal movements using a wearable monitor

The importance of Fetal Movement (FM) patterns as a biomarker for fetal health has been extensively argued in obstetrics. However, the inability of current FM monitoring methods, such as ultrasonography, to be used outside clinical environments has made it challenging to understand the nature and evolution of FM. A small body of work has introduced wearable sensor-based FM monitors to address this gap. Despite promises in controlled environments, reliable instrumentation to monitor FM out-of-clinic remains unresolved, particularly due to the challenges of separating FMs from interfering artifacts arising from maternal activities. To date, efforts have been focused almost exclusively on homogenous (single) sensing and information fusion modalities, such as decoupled acoustic or accelerometer sensors. However, FM and related signal artifacts have varying power and frequency bandwidths that homogeneous sensor arrays may not capture or separate efficiently. In this investigation, we introduce a novel wearable FM monitor with an embedded heterogeneous sensor suite combining accelerometers, acoustic sensors, and piezoelectric diaphragms designed to capture a broad range of FM and interfering artifact signal features enabling more efficient isolation of both. We further outline a novel data fusion architecture combining data-dependent thresholding and machine learning to automatically detect FM and separate it from signal artifacts in real-world (home) environments. The performance of the device and the data fusion architecture are validated using 33 h of at-home use through concurrent recording of maternal perception of FM. The FM monitor detected an impressive 82 % of maternally sensed FMs with an overall accuracy of 90 % in recognizing FM and non-FM events. Consistency of detection was strongest from 32 gestational weeks onwards, which overlaps with the critical FM monitoring window for stillbirth prevention. We believe the multi-modal sensor fusion approach presented in this research will be a major milestone in the development of low-cost wearable FM monitors enabling pervasive monitoring of FM in unsupervised environments.

Automated summary

The inability of current FM monitoring methods to be used outside clinical environments has made it challenging to understand the nature and evolution of FM. This investigation introduces a novel wearable FM monitor with a heterogeneous sensor suite and a data fusion architecture to efficiently capture and separate FM from interfering artifacts. The performance of the device and architecture were validated through at-home use, demonstrating high accuracy in detecting and recognizing FM events. This research is a major milestone in the development of low-cost wearable FM monitors for pervasive monitoring of FM in unsupervised environments.