Dissertation defence (Information and Communication Technology): MSc Fatemeh Sarhaddi
Time
10.1.2024 at 12.00 - 16.00
MSc Fatemeh Sarhaddi defends the dissertation in Information and Communication Technology titled “Continuous IoT-based maternal monitoring: system design, evaluation, opportunities, and challenges” at the University of Turku on 10 January 2024 at 12.00 (University of Turku, Agora, lecture hall XXII, Turku).
Opponent: Associate Professor Frida Sandberg (Lund University, Sweden)
Custos: Professor Pasi Liljeberg (University of Turku)
Doctoral Dissertation at UTUPub: https://urn.fi/URN:ISBN:978-951-29-9582-0
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Summary of the Doctoral Dissertation:
This thesis presents a long-term IoT-based maternal monitoring system and explores its potential in maternal care. This system continuously provides maternal health monitoring services, such as stress monitoring, sleep monitoring, and physical activity monitoring, in everyday life. We evaluate the system’s feasibility, reliability, and energy efficiency. We also discuss the practical challenges of implementing the system.
We validate the heart rate (HR) and heart rate variability (HRV) parameters collected by the system. In addition, we propose a deep-learning-based method for quality assessment of HR and HRV parameters to discard unreliable data and improve health decisions. We use the system to collect data from 62 pregnant women during pregnancy and three-months postpartum. Then, the reliable HR and HRV parameters are used to track the trends during pregnancy and postpartum.
Finally, we investigate maternal loneliness as a major mental health problem. We develop two predictive models to detect maternal loneliness during late pregnancy and the postpartum period. These models use the objective health parameters passively collected by the system and achieve high performance.
Opponent: Associate Professor Frida Sandberg (Lund University, Sweden)
Custos: Professor Pasi Liljeberg (University of Turku)
Doctoral Dissertation at UTUPub: https://urn.fi/URN:ISBN:978-951-29-9582-0
***
Summary of the Doctoral Dissertation:
This thesis presents a long-term IoT-based maternal monitoring system and explores its potential in maternal care. This system continuously provides maternal health monitoring services, such as stress monitoring, sleep monitoring, and physical activity monitoring, in everyday life. We evaluate the system’s feasibility, reliability, and energy efficiency. We also discuss the practical challenges of implementing the system.
We validate the heart rate (HR) and heart rate variability (HRV) parameters collected by the system. In addition, we propose a deep-learning-based method for quality assessment of HR and HRV parameters to discard unreliable data and improve health decisions. We use the system to collect data from 62 pregnant women during pregnancy and three-months postpartum. Then, the reliable HR and HRV parameters are used to track the trends during pregnancy and postpartum.
Finally, we investigate maternal loneliness as a major mental health problem. We develop two predictive models to detect maternal loneliness during late pregnancy and the postpartum period. These models use the objective health parameters passively collected by the system and achieve high performance.
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