Machine Learning Model Validates Vibroacoustic Sensor Array for Fetal Movement Monitoring
A new machine learning framework has been developed to detect fetal movements using a wearable array of vibroacoustic sensors, offering a potential tool for assessing fetal health during pregnancy. The system combines piezoelectric and acoustic sensors to capture a range of movements, validated against ultrasound as the gold standard. An ensemble model achieved moderate precision and recall in identifying movements, demonstrating feasibility for low-cost monitoring, especially in resource-limited settings where stillbirth rates remain high. Background on Stillbirth and Fetal Movement Monitoring Stillbirth represents a significant global health challenge, with estimates indicating around two million cases annually, disproportionately affecting low- and middle-income countries. Regional disparities are stark, with rates exceeding 20 per 1,000 births in parts of Africa compared to under three in Western Europe. Underreporting in these areas exacerbates the issue, underscoring inequalities in healthcare access. Factors like maternal education,…

