Project Description
AI-driven RFID Sensing for Smart Health Applications
This project develops Radio Frequency Identification (RFID) based sensing systems for smart health monitoring. Specifically, several fundamental problems will be investigated, and novel ML/AI techniques will be developed for RFID sensing based smart health applications. This project leverages passive RFID tags as wearable sensors for monitoring human health conditions to help diagnose diseases such as Parkinson’s disease (PD) and Interstitial Lung Disease (ILD). ML/AI-driven methods, such as tensor decomposition, transfer learning (e.g., domain adaptation and meta-learning), deep Gaussian Process, and federated learning will be incorporated to develop effective solutions to the challenging problems. The research agenda consists of four well integrated thrusts: (i) to investigate the challenges and fundamental performance limits; (ii) to develop RFID-based re respiration rate, pulmonary function test, and heartbeat signal monitoring schemes; (iii) to develop RFID-based pose monitoring, activity recognition, and PD detection systems; and (iv) to develop robust and fair federated learning models for handling health data. The proposed algorithms will be implemented and validated with extensive experiments in emulated and real clinical environments, with focus on two important smart health applications, i.e., PD detection and breathing-based ILD detection.
Aug. 15, 2023 ~ July 31, 2027
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We acknowledge the generous support from our sponsor
This project is supported in part by the National Science Foundation under Grants IIS-2306789, IIS-2306790, IIS-2306791, and IIS-2306792. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the foundation.