IoT and Machine Learning–based Maternal Health Monitoring System
Keywords:
Adaptive Noise Cancellation, Cardiotocography (CTG), Deep Learning, Fetal ECG (fECG), Fetal health classification-monitoring, Fibre Bragg Grating Sensor, Machine Learning, Maternal Health, Phonocardiography (fPCG)Abstract
Monitoring a baby’s health during pregnancy matters not just for the baby but for the mother as well. Doctors rely on tools like cardiotocography (CTG) and fetal electrocardiography (fECG) to spot trouble, whether it’s distress, hypoxia, or developmental issues. But these old-school methods trip up when it comes to interpreting signals, dealing with noise, or just getting access when and where they are needed. Now, the landscape is shifting. Artificial intelligence and machine learning are stepping in, along with smarter sensors and electronics. Suddenly, it’s possible to monitor fetal health more accurately, around the clock, and without being invasive. AI isn’t just a buzzword here it sharpens CTG analysis, lets us read signals with more confidence, and shrugs off a lot of the noise that used to muddy the waters. Deep learning and fuzzy logic help make sense of messy data. On top of that, new tech like fibre Bragg grating sensors, adaptive noise cancellation, and secure telemonitoring are changing the game. Healthcare providers can now keep an eye on both mother and baby in real time even when they’re not in the clinic thanks to IoT-connected wearables. Digital twin models and the use of AI in genomic analysis crank things up another notch. They open the door to predictive and personalized care, tailored to the specific needs of each mother and child. Of course, there’s still work to do cleaning up data, making sure results are understandable, and proving these tools actually work in the clinic. But as AI-driven algorithms, secure IoT systems, and advanced sensors come together, prenatal care is moving toward a future that’s more personal, more accurate, and a lot more accessible.
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