AI Article Synopsis
- Pregnancy monitoring is crucial for the health of mothers and fetuses, with WHO reporting 287,000 maternal deaths in 2020, sparking a need for more accessible options than traditional hospital visits.
- Advances in wearable sensors and AI are making home-based, non-invasive health monitoring for pregnant women more feasible and convenient.
- The review highlights the role of wearable sensors in tracking physiological signals and how AI can enhance early detection and diagnosis, while also addressing ongoing challenges like accuracy and data privacy.
Article Abstract
Pregnancy monitoring is always essential for pregnant women and fetuses. According to the report of WHO (World Health Organization), there were an estimated 287,000 maternal deaths worldwide in 2020. Regular hospital check-ups, although well established, are a burden for pregnant women because of frequent travelling or hospitalization. Therefore, home-based, long-term, non-invasive health monitoring is one of the hot research areas. In recent years, with the development of wearable sensors and related data-processing technologies, pregnancy monitoring has become increasingly convenient. This article presents a review on recent research in wearable sensors, physiological data processing, and artificial intelligence (AI) for pregnancy monitoring. The wearable sensors mainly focus on physiological signals such as electrocardiogram (ECG), uterine contraction (UC), fetal movement (FM), and multimodal pregnancy-monitoring systems. The data processing involves data transmission, pre-processing, and application of threshold-based and AI-based algorithms. AI proves to be a powerful tool in early detection, smart diagnosis, and lifelong well-being in pregnancy monitoring. In this review, some improvements are proposed for future health monitoring of pregnant women. The rollout of smart wearables and the introduction of AI have shown remarkable potential in pregnancy monitoring despite some challenges in accuracy, data privacy, and user compliance.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11479201 | PMC |
| http://dx.doi.org/10.3390/s24196426 | DOI Listing |
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