Supporting Lifestyle Change in Obese Pregnant Mothers through Wearable Internet-of-Things
Amir M. RahmaniOther PIs/Investigators/PhD students:
TYKS Hospital Funding Agency:
Academy of Finland
Pregnant women with obesity have indisputably increased risk for gestational diabetes mellitus, depression, miscarriage, and preterm birth, just to mention few. These pregnancy complications clearly have negative effects on their unborn children. Due to the magnitude of this global challenge it calls for immediate action. During the course of this project, an Internet-of-Things-based intelligent monitoring system will be developed to detect and predict obesity-related pregnancy complications as early as possible. Cybernetic health concept will be utilized by intertwining lifestyle and environmental data together with bio-signals associated with medical knowledge to develop a closed-loop system to make maternity care more effective, dynamic and end-user driven. This is done via a platform that leverages portable devices and inexpensive wearable sensors, coupled with a multimodal event modeling, activity recognition, and life-logging engine. This research will deliver a ubiquitous pregnancy monitoring service to end-users, mothers, and healthcare providers, enabling pregnancy events detection, prediction, assessment, and prevention.Publications:
– Kirsi Grym, Hannakaisa Niela-Vilen, Eeva Ekholm, Lotta Hamari, Iman Azimi, Amir M. Rahmani, Pasi Liljeberg, Eliisa Löyttyniemi, and Anna Axelin, “A Feasibility Study of Wearable Wristbands as a Measurement Tool during Pregnancy and One-month Postpartum,” BMC Pregnancy and Childbirth, 2019.
– Olugbenga Oti, Iman Azimi, Arman Anzanpour, Amir M. Rahmani, Anna Axelin, and Pasi Liljeberg, “IoT-based Healthcare System for Real-time Maternal Stress Monitoring,” in ACM/IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE’18), 2018, USA.
– Juho Laitala, Mingzhe Jiang, Elise Syrjälä, Emad Kasaeyan Naeini, Antti Airola, Amir M. Rahmani, Nikil Dutt, Pasi Liljeberg, “Robust ECG R-peak Detection Using LSTM”, The 35th ACM/SIGAPP Symposium On Applied Computing (SAC’20), 2020, Czech.