UNITE (UNderserved communITiEs) presents a community engagement model that is smart, deploying ubiquitous monitoring and lifelogging; connected, bringing together a diverse cast of community members including mothers, families, care providers, and outreach resources; and coordinated, using technology to proactively reach out to the community and use personalized intervention and education for improved self-management by the women. The UNITE project champions a model that is scalable in size, portable across different ethnic communities, and promises improved outcomes through better self-management and community enhanced motivational factors. The UNITE project performs a controlled study using a community of underserved Orange County mothers together with non-profit agencies, hospitals, and local support organizations to evaluate the efficacy of this new community-enhanced self-management approach, and its impact on community building. The project builds larger communities of healthcare providers, insurance providers, and governmental agencies that can work in concert to enhance the well-being and lifestyles of mothers and families across a diverse spectrum of socio-economically disadvantaged groups. The UNITE project also trains the next generation of healthcare providers to deploy socio-economically relevant Internet-of-Things (IoT) technology in a cost-effective and user-friendly manner.

– Iman Azimi, Olugbenga Oti, Sina Labbaf, Hannakaisa Niela-Vilen, Anna Axelin, Nikil Dutt, Pasi Liljeberg, and Amir M. Rahmani, “Personalized Maternal Sleep Quality Assessment: An Objective IoT-based Longitudinal Study”, IEEE Access, 2019
– Holly Borg, Hannah Vasquez, Michelle Heredia, Melissa Navarrete, Nikil Dutt, Amir M. Rahmani, and Yuqing Guo, “A Self-Management Model: Using Wearable Devices for Continuous Monitoring during the Perinatal Period”, National Perinatal Association Annual Conference Perinatal Care and the 4th Trimester” (NPA’20), 2020
– Lucretia Williams, Gillian R. Hayes, Yuqing Guo, Amir M. Rahmani, Nikil Dutt, “HCI and mHealth Wearable Tech: A Multidisciplinary Research Challenge”, ACM CHI Conference on Human Factors in Computing Systems, Case Study (CHI’20-Case Study), 2020
_ Juho Laitala, Mingzhe Jiang, Elise Syrjälä, Emad Kasaeyan Naeini, Antti Airola, Amir M. Rahmani, Nikil D. Dutt, Pasi Liljeberg, “Robust ECG R-peak Detection Using LSTM”, The 35th ACM/SIGAPP Symposium On Applied Computing (SAC’20), 2019