NSF CPS Project Web-portal

Inkjet Printed Flexible Electronic CPS with Context-aware Events of Interest Detection

Funding Source: National Science Foundation (NSF)

Program: Cyber Physical Systems (CPS) of CISE Directorate

NSF Logo
Award number: CNS-1932281
Duration: 2020-2022 (3 years)
NSF Project Award page link
Project Synopsis:

This project will develop novel, body-worn, flexible sensors fabricated using low-cost inkjet printing technology on thin film polymers, develop novel algorithms capable of automatically detecting health events in different contexts, and develop a novel data reliability metric by analyzing sensor and context data in real-time. The project will produce practice components for test and validation in a clinical setting with cardiac patients to determine their effectiveness for monitoring heart conditions. If successful, the project will provide patients and clinicians with a tool to improve health monitoring in natural environments. The research is expected to impact additive manufacturing methods, flexible electronics, health monitoring, and smart and connected communities initiatives. It will also provide training for undergraduate and graduate students and expose the next generation of scholars and workers to these technologies through a Summer Code Camp for high school students.

Next generation Cyber-Physical Systems (CPS) must utilize resilient and reliable cyber/physical interfacing, be economically viable, and be capable of processing extremely large data automatically and reliably. Achieving this requires overcoming current technological barriers associated with seamless integration of computation and physical domains and meaningful interpretation of multimodal and multigrain data of scalable CPS. Balancing theory with experimentation, this project will: 1) produce foundational engineering process for CPS interface with thin-film flexible electronic electrodes and low cost sensors fabricated with inkjet printing; 2) develop new algorithms for autonomous processing of sensor data to detect context-aware events of interest and data reliability metric for closed-loop CPS using real-time machine learning implemented at edge; and 3) deploy CPS practice components in a real-life pilot study to explore detection of cardiac episodes and explore various closed loop feedback approaches.

Project Team:
Project PI: Dr. B. I. Morshed, Associate Professor, Electrical and Computer Engineering (EECE), University of Memphis
Co-PI: Dr. T. Fujiwara, Associate Professor, Department of Chemistry, University of Memphis
Co-PI: Dr. F. Andrasik, Distinguished Professor and Chair, Department of Psychology, University of Memphis
Co-PI: Mr. R. Hewitt, Associate Professor, Engineering Technology, University of Memphis
Co-PI: Dr. R. Kabra, Cardiologist, Associate Professor of Medicine, University of Tennessee Health Science Center (UTHSC)
Consultant: Dr. M. Rahman, Physician, Baptist Minor Medical Center and Henderson County Community Hospital

Project Activities:

Related Links: