Project Grant 2306790

Award Date 8/15/23
Completion Date 7/31/27
Dollars Obligated $300K
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Providence, RI 02903, USA
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This Project Grant award for $299,997.00 from the National Science Foundation's Computer and Information Science and Engineering Program (CFDA 47.070) supports a collaborative research effort led by Auburn University. The project aims to develop AI-driven radio frequency identification (RFID) sensing techniques to enable more affordable, comfortable, and accessible smart health monitoring systems. The key products and services to be delivered include: Investigating challenges and performance...
The National Science Foundation (NSF) awarded a $299,612 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to The Johns Hopkins University to develop AI-driven Radio Frequency Identification (RFID) sensing systems for smart health monitoring applications. The 4-year project aims to create more affordable, comfortable, and accessible health monitoring solutions by leveraging advances in the Internet of Things and machine learning/artificial...
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This is a Project Grant awarded by the National Science Foundation (NSF) Division of Information and Intelligent Systems under the Computer and Information Science and Engineering program (CFDA 47.070). The $299,708 grant, with a period of performance from August 15, 2023 to July 31, 2027, supports collaborative research to develop AI-driven radio frequency identification (RFID) sensing techniques for smart health monitoring applications.

The research aims to create more affordable, comfortable, and accessible health monitoring systems that leverage advances in the Internet of Things and machine learning/AI. The project will investigate fundamental challenges in RFID sensing, develop methods for monitoring respiration, pulmonary function, and heartbeat signals, as well as systems for pose monitoring, activity recognition, and Parkinson's disease detection. The team will also develop robust and fair federated learning models to handle health data. The research will be validated through experiments in emulated and real clinical environments, with a focus on Parkinson's disease and interstitial lung disease detection.

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