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Cyber-Physical Platform for Preeclampsia Detection

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Computational Science and Its Applications – ICCSA 2020 (ICCSA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12253))

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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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Notes

  1. 1.

    Further details are subject to patent protection.

  2. 2.

    https://developer.apple.com/healthkit/.

  3. 3.

    https://www.google.com/fit/.

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Acknowledgement

This work was funded by a grant of the Romanian National Authority for Scientific Research and Innovation, CCCDI-UEFISCDI, project number 59/2017, Eurostars Project E10871, i-bracelet-“Intelligent bracelet for blood pressure monitoring and detection of preeclampsia”.

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Authors and Affiliations

  1. SC Info World SRL, Bucharest, Romania

    Iuliana Marin, Maria Iuliana Bocicor & Arthur-Jozsef Molnar

Authors
  1. Iuliana Marin
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  2. Maria Iuliana Bocicor
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  3. Arthur-Jozsef Molnar
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Corresponding author

Correspondence to Iuliana Marin .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Chair- Center of ICT/ICE, Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Clayton School of Information Technology, Monash University, Clayton, VIC, Australia

    David Taniar

  7. Department of Information Science, Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

  11. Department of Neurology, University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

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Marin, I., Bocicor, M.I., Molnar, AJ. (2020). Cyber-Physical Platform for Preeclampsia Detection. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12253. Springer, Cham. https://doi.org/10.1007/978-3-030-58814-4_48

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  • DOI: https://doi.org/10.1007/978-3-030-58814-4_48

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58813-7

  • Online ISBN: 978-3-030-58814-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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