Preterm birth (PTB) is the most common cause of neonatal deaths. Due to the high rate of PTBs (15M/y), it is extremely beneficial to identify the women at risk at early stage and prevent PTB. Physiological parameters could help us to uncover and model multifactorial processes that lead to PTB. Continuous monitoring of such parameters holds significant promise to successful prevention. Internet of Things (IoT) technologies can be leveraged to create the ability to perform such monitoring throughout pregnancy. In this project, we tackle PTB issues by proposing an IoT platform tailored for PTB prevention for everyday settings. Our core contributions are 1) a customized architecture including a set of wearable electronic devices that are feasible for 7-9 months of continuous monitoring, 2) a personalized PTB prevention solution using artificial intelligence methods, and 3) a comprehensive performance assessment via implementation of this monitoring in clinical trials.

Site of Research: Department of Future Technologies (University of Turku), Department of Nursing Science (University of Turku), and Turku University Hospital (TYKS)

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