Cyber-Physical Platform for Preeclampsia Detection
- URL: http://arxiv.org/abs/2009.02093v1
- Date: Fri, 4 Sep 2020 10:15:00 GMT
- Title: Cyber-Physical Platform for Preeclampsia Detection
- Authors: Iuliana Marin and Maria Iuliana Bocicor and Arthur-Jozsef Molnar
- Abstract summary: The present paper details a cyber-physical system built around an intelligent bracelet for monitoring hypertension-related conditions tailored to pregnant women.
The bracelet uses a microfluidic layer that is compressed by the blood pressing against the arterial wall. Integrated sensors register the waveform and send it to the user's smartphone, where the systolic and diastolic values are determined.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Hypertension-related conditions are the most prevalent complications of
pregnancy worldwide. They manifest in up to 8% of cases and if left untreated,
can lead to serious detrimental effects. Early detection of their sudden onset
can help physicians alleviate the condition and improve outcomes for both
would-be mother and baby. Today's prevalence of smartphones and cost-effective
wearable technology provide new opportunities for individualized medicine.
Existing devices promote heart health, they monitor and encourage physical
activity and measure sleep quality. This builds interest and encourages users
to require more advanced features. We believe these aspects form suitable
conditions to create and market specialized wearable devices. The present paper
details a cyber-physical system built around an intelligent bracelet for
monitoring hypertension-related conditions tailored to pregnant women. The
bracelet uses a microfluidic layer that is compressed by the blood pressing
against the arterial wall. Integrated sensors register the waveform and send it
to the user's smartphone, where the systolic and diastolic values are
determined. The system is currently developed under European Union research
funding, and includes a software server where data is stored and further
processing is carried out through machine learning.
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