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
Project Team
Related Publications (journal & magazine)
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Merih Deniz Toruner, Victoria Shi, John Sollee, Wen-Chi Hsu, Guangdi Yu, Christian Merlo, Karthik Suresh, Zhicheng Jiao, Xuyu Wang, Shiwen Mao, and Harrison Bai, “Artificial intelligence driven wireless sensing for health management,” The LANCET—Digital Health, under review.
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Kaiyuan Ma, Shunan Song, Xuyu Wang, Lingling An, and Shiwen Mao, “APC: Contactless healthy sitting posture monitoring with microphone array,” Elsevier Smart Health Journal, vol.32, pp.1-17, June 2024. DOI: 10.1016/j.smhl.2024.100463.
Related Publications (conference)
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Ningning Wang, Tianya Zhao, Shiwen Mao, Harrison X. Bai, Zhicheng Jiao, and Xuyu Wang, “ECG-grained cardiac monitoring using RFID,” invited paper, in Proc. 2024 International Conference on Computer Communications and Networks (ICCCN 2024), Big Island, HI, July 2024.
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Kaiyuan Ma, Shunan Song, Xuyu Wang, Lingling An, and Shiwen Mao, “APC: Contactless healthy sitting posture monitoring with microphone array,” presented at The 9th IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE 2024), Wilmington, DE, June 2024.
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Ningning Wang, Tianya Zhao, Shiwen Mao, and Xuyu Wang, “AI generated wireless data for enhanced satellite device fingerprinting,” in Proc. IEEE ICC 2024 Workshop on Machine Learning and Deep Learning for Wireless Security (MLDLWiSec), Denver, CO, June 2024, pp.88-93.
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Ziqi Wang and Shiwen Mao, “Demo Abstract: AIGC for RFID-based human activity recognition,” in Proc. IEEE INFOCOM 2024, Vancouver, Canada, May 2024. (Best Demo Award of IEEE INFOCOM 2024)
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Tianya Zhao, Xuyu Wang, Junqing Zhang, and Shiwen Mao, “Explanation-guided backdoor attacks on model-agnostic RF fingerprinting,” in Proc. IEEE INFOCOM 2024, Vancouver, Canada, May 2024.
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Ziqi Wang* and Shiwen Mao, “AIGC for RF sensing: The case of RFID-based human activity recognition,” invited paper, in Proc. 2024 International Conference on Computing, Networking and Communications (ICNC 2024), Big Island, HI, Feb. 2024, pp.1092-1097.
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.