Getting the Edge on Data-Driven Self-Managed Care: A Focus on Older Veterans in Arizona
Lead PI:
Ming Zhao

Older adults strive to be independent and healthy yet compared with younger individuals, they are at greater risk of chronic health conditions and social isolation. Solutions are needed that create ways for older adults to thrive, connect, contribute to, and shape their communities. The increasingly available Internet of Things (IoTs), particularly wearables and smart home devices (e.g., smartwatches, voice-activated home assistants), offer important opportunities for real-time interventions for self-management of care, especially if they are customized to meet each person’s unique needs and goals. This planning project focuses on older military veteran populations in Phoenix, Arizona. The project is building a multidisciplinary team to form a research agenda for studying, developing, and deploying an IoT based solution for providing high quality, low cost, and community-sensituve self-managed care. This project can benefit Arizona’s large populations of older adults and veterans, with potential to impact underserved aging populations across the U.S. The project also provides education and workforce development to all levels of students from multiple disciplines, and in particular creates innovations and access for many underrepresented students at Arizona State University, where the project is based.Through focus groups and pilot data collection, this planning project is laying the groundwork for innovations in real-time and privacy-preserving learning systems which that employ IoTs and edge computing to support learning on live, personal health data and provide real-time, personalized feedback without compromising user privacy. The project also entails heterogeneous data integration and learning techniques to detect social-emotional, and health changes and support prevention and early interventions. The project builds on human-systems engineering and health informatics. This approach is designed to meet the diverse needs of older adults and to allow them to more effectively take charge of their health; The project includes an analysis of social, cultural, and relationship factors and their effects on cognitive and affective processes in older veterans.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.

Ming Zhao
Ming Zhao is an associate professor of the Arizona State University (ASU) School of Computing and Augmented Intelligence (SCAI), where he directs the NSF IUCRC Center for Accelerated Real Time Analytics (CARTA) and the ASU research laboratory for Virtualized Infrastructures, Systems, and Applications (VISA). His research is in the areas of experimental computer systems, including cloud/edge, machine learning/big data, and high-performance systems as well as operating systems and storage in general. He is also interested in the interdisciplinary studies that bridge computer systems research with other domains. His work has been funded by the National Science Foundation (NSF), Department of Homeland Security, Department of Defense, Department of Energy, and industry companies, and his research outcomes have been adopted by several production systems in industry. Zhao is a recipient of the NSF Faculty Early Career Development (CAREER) award, the Air Force Summer Faculty Fellowship, the VMware Faculty Award, and the Best Paper Award of the IEEE International Conference on Autonomic Computing. He received his bachelor’s and master’s degrees from Tsinghua University, and his doctorate from University of Florida.
Performance Period: 05/01/2023 - 04/30/2024
Institution: Arizona State University
Award Number: 2231874