Many countries, including the United States and Japan, are facing a rapidly aging population. Improving the quality of healthcare and quality of life for senior citizens while managing and even reducing the health and social care costs is a critical challenge. The growth and accessibility of wearable devices enable continuous monitoring of a person’s vital signs and other health indicators. These wearable data, combined with medical records and other community and environment data, are particularly valuable for senior communities in order to identify new conditions or relapses for early intervention. Collectively, such data from different individuals, communities, and countries, can be used to learn better predictive models and improve population health at large. This project aims to build a multidisciplinary team including academic researchers with complementary expertise (big data, privacy and security, machine learning, human-computer interaction, sociology, and mobile health) and community stakeholders (seniors, community service providers, healthcare providers, and government agencies), to understand the unique challenges and form a research agenda for developing and deploying a health monitoring system for senior communities.

The project will study: 1) data integration and machine learning techniques to integrate data from multiple sources in real time for monitoring and intervention; and to leverage the data from different communities to improve healthcare outcome and medical research; 2) privacy-enhancing techniques including differential privacy and federated learning to ensure the system is compliant with regulations, while balancing the privacy protection and utility of the system; and 3) social implications and cultural differences of the technology in the two countries via online surveys and qualitative studies to identify challenges and barriers in health monitoring for senior communities, and their impact on the design and adoption of the proposed technology. The project includes a set of community engagement activities in order to develop a research agenda that can not only empower the senior communities and improve their health and well-being, but also enable data-driven medical research that improves population health at large.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Agency
National Science Foundation (NSF)
Institute
Division of Computer and Network Systems (CNS)
Type
Standard Grant (Standard)
Application #
1952192
Program Officer
Sandip Roy
Project Start
Project End
Budget Start
2020-10-01
Budget End
2021-09-30
Support Year
Fiscal Year
2019
Total Cost
$75,000
Indirect Cost
Name
Emory University
Department
Type
DUNS #
City
Atlanta
State
GA
Country
United States
Zip Code
30322