Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1)
Long-Term IoT-Based Maternal Monitoring: System Design and Evaluation
Julkaisun tekijät: Sarhaddi Fatemeh, Azimi Iman, Labbaf Sina, Niela-Vilén Hannakaisa, Dutt Nikil, Axelin Anna, Liljeberg Pasi, Rahmani Amir M
Kustantaja: MDPI
Julkaisuvuosi: 2021
Journal: Sensors
Tietokannassa oleva lehden nimi: SENSORS
Lehden akronyymi: SENSORS-BASEL
Volyymi: 21
Julkaisunumero: 7
Sivujen määrä: 21
eISSN: 1424-8220
DOI: http://dx.doi.org/10.3390/s21072281
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/55078775
Pregnancy is a unique time when many mothers gain awareness of their lifestyle and its impacts on the fetus. High-quality care during pregnancy is needed to identify possible complications early and ensure the mother's and her unborn baby's health and well-being. Different studies have thus far proposed maternal health monitoring systems. However, they are designed for a specific health problem or are limited to questionnaires and short-term data collection methods. Moreover, the requirements and challenges have not been evaluated in long-term studies. Maternal health necessitates a comprehensive framework enabling continuous monitoring of pregnant women. In this paper, we present an Internet-of-Things (IoT)-based system to provide ubiquitous maternal health monitoring during pregnancy and postpartum. The system consists of various data collectors to track the mother's condition, including stress, sleep, and physical activity. We carried out the full system implementation and conducted a real human subject study on pregnant women in Southwestern Finland. We then evaluated the system's feasibility, energy efficiency, and data reliability. Our results show that the implemented system is feasible in terms of system usage during nine months. We also indicate the smartwatch, used in our study, has acceptable energy efficiency in long-term monitoring and is able to collect reliable photoplethysmography data. Finally, we discuss the integration of the presented system with the current healthcare system.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
