Collaborative Research: SCH: AI-driven RFID Sensing for Smart Health Applications

List of Personnel

Principle Investigator

• Xuyu Wang, Assistant Professor

Graduate Students

• Ningning Wang

Undergraduate Students

• Jason Jiang in Summer 2024

• Yanelli Gloria in Spring 2024

Project Description

Many existing health monitoring systems are expensive, uncomfortable to wear, or can only be administered in a hospital environment. With advances in the Internet of Things (IoT) and Machine learning (ML)/artificial intelligence (AI), it is highly desirable to develop AI-driven radio frequency sensing techniques to make smart health monitoring cheaper, more comfortable to use, and more accessible to the broad population, while supporting excellent monitoring performance. The main challenges to achieving such goals are the noisy RF data and strong interference coming from the dynamic environment. A multi-disciplinary team of six investigators with complementary expertise will work closely together to significantly improve the state-of-the-art of radio frequency sensing based smart healthcare provisioning and make a significant step forward to fully harvest the potential of the IoT and ML/AI. The team of investigators will also jointly develop a new graduate-level course on Deep Learning Empowered RF Health Sensing and enhance their undergraduate and graduate level courses. The project will also engage students by providing hands-on experience with cutting-edge technologies that are at the very frontier of wireless sensing, deep learning, and smart health. Outcomes from this project will be disseminated through technical publications, conference keynotes, distinguished lectures and tutorials, a project website, and open-source repositories. The investigators are committed to broadening participation from underrepresented groups, through their institutional outreach programs and the NSF Research Experiences for Undergraduates and Research Experiences for Teachers programs.

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 and interstitial lung disease. ML/AI-driven methods, such as tensor decomposition, transfer learning (via domain adaptation and meta-learning), deep Gaussian Processes, and federated learning will be incorporated to develop effective solutions to these challenging problems. The research agenda consists of four well integrated thrusts: (i) to investigate the challenges and fundamental performance limits of the sensors; (ii) to develop RFID-based 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 project?s algorithms will be implemented and validated with extensive experiments in emulated and real clinical environments, with a focus on two important smart health applications, Parkinson?s disease detection and breathing-based interstitial lung disease detection.

Related Journal and Book Chapter Publications

1. Tianya Zhao, Xuyu Wang, Shiwen Mao, Slobodan Vucetic, and Jie Wu, “Adversarial deep learning for indoor localization,” Chapter X in Network Security Empowered by Artificial Intelligence.

2. Azhar Chara, Tianya Zhao, Xuyu Wang, and Shiwen Mao, “Respiratory biofeedback using acoustic sensing with smartphones,” Elsevier Smart Health Journal, vol. 28, pp.100387, June 2023.

3. Chao Yang, Xuyu Wang, Shiwen Mao, “TARF: Technology-agnostic RF sensing for human activity recognition,” IEEE Journal of Biomedical and Health Informatics, Special Issue on Cognitive Cyber-Physical Systems with AI based Solutions in Medical Informatics, to appear.

4. Jing Hou, Xuyu Wang, and Amy Z Zeng, “Inter-Temporal Reward Strategies in the Presence of Strategic Ethical Hackers,” in IEEE/ACM Transactions on Networking, 2024.

5. Chao Yang, Xuyu Wang, and Shiwen Mao, “RFID tag localization with a sparse tag array,” IEEE Internet of Things Journal, Special Issue on Knowledge and Service Oriented Industrial Internet of Things: Architectures, Challenges and Methodologies, to appear.

6. Chao Yang, Lingxiao Wang, Xuyu Wang, and Shiwen Mao, “Environment adaptive RFID based 3D human pose tracking with a meta-learning approach,” IEEE Journal of Radio Frequency Identification, Special Issue on Wireless Motion Capture and Fine-Scale Localization, to appear.

7. Chao Yang, Xuyu Wang, and Shiwen Mao, “RFID based 3D human pose tracking: A subject generalization approach,” Elsevier/KeAi Digital Communications and Networks, Special Issue on Edge computation and intelligence, to appear.

8. Chao Yang, Xuyu Wang, and Shiwen Mao, “RFID-Pose: Vision-aided 3D human pose estimation with RFID,” IEEE Transactions on Reliability, vol.70, no.3, pp.1218-1231, Sept. 2021.

9. Xiangyu Wang*, Xuyu Wang*, Shiwen Mao, Jian Zhang, Senthilkumar CG Periaswamy, and Justin Patton, “Adversarial deep learning for indoor localization,” IEEE Internet of Things Journal, vol.9, no.19, pp.18182--18194, Oct. 2022. DOI: 10.1109/JIOT.2022.3155562.. (*means co-first authors)

10. Chao Yang, Xuyu Wang, and Shiwen Mao, “Unsupervised drowsy driving detection with RFID,” IEEE Transactions on Vehicular Technology, vol.69, no.8, pp. 8151-8163, Aug. 2020.

11. Chao Yang, Xuyu Wang, and Shiwen Mao, “Respiration monitoring with RFID in driving environments,” IEEE Journal on Selected Areas in Communications, Special Issue on Internet of Things for In-Home Health Monitoring, vol.39, no.2, pp.500-512, Feb. 2021.

12. Xiangyu Wang, Xuyu Wang, Shiwen Mao, Jian Zhang, Senthilkumar CG Periaswamy, and Justin Patton, “Indoor radio map construction and localization with deep Gaussian Processes,” IEEE Internet of Things Journal, vol.7, no.11, pp. 11238-11249, Nov. 2020.

13. Chao Yang, Xuyu Wang, and Shiwen Mao, “Unsupervised detection of apnea using commodity RFID tags with a recurrent variational autoencoder,” IEEE Access Journal, Special Section on Advanced Information Sensing and Learning Technologies for Data-centric Smart Health Applications, vol.7, no.1, pp.67526-67538, June 2019.

Related Conference Publications

1. Ningning Wang, Tianya Zhao, Shiwen Mao, Harrison X. Bai, Zhicheng Jiao, and Xuyu Wang, “ECG-grained Cardiac Monitoring Using RFID,” in Proc. IEEE ICCCN 2024, Big Island, Hawaii, July 2024.

2. Tianya Zhao, Ningning Wang, Guanqun Cao, Shiwen Mao, and Xuyu Wang, “Functional Data Analysis Assisted Cross-Domain Wi-Fi Sensing Using Few-Shot Learning,” in Proc. IEEE ICC 2024, Denver, CO, June 2024.

3. Nawrin Tabassum, Ka-Ho Chow, Xuyu Wang, Wenbin Zhang, and Yanzhao Wu, "On the Efficiency of Privacy Attacks in Federated Learning," in 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024) Workshops - FedVision.

4. Jing Hou, Xuyu Wang, and Amy Z Zeng, “Inter-Temporal Reward Decisions with Strategic Ethical Hackers,” in Proc. IEEE CNS 2023, Orlando, FL, Oct. 2023.

5. Chao Yang, Lingxiao Wang, Xuyu Wang, and Shiwen Mao, “Demo Abstract: Environment-adaptive 3D human pose tracking with RFID,” in Proc. IEEE INFOCOM 2022, Virtual Conference, May 2022. (The IEEE INFOCOM 2022 Demo Award).

6. Chao Yang, Xuyu Wang, and Shiwen Mao, “Demo Abstract: Technology-agnostic approach to RF based human activity recognition,” in Proc. IEEE INFOCOM 2022, Virtual Conference, May 2022.

7. Chao Yang, Lingxiao Wang, Xuyu Wang, and Shiwen Mao, “Meta-Pose: Environment-adaptive human skeleton tracking with RFID,” in Proc. IEEE GLOBECOM 2021 (GLOBECOM'21), Madrid, Spain, Dec. 2021.

8. Chao Yang, Xuyu Wang, and Shiwen Mao, “Subject-adaptive skeleton tracking with RFID,” in Proc. The 16th IEEE International Conference on Mobility, Sensing and Networking (MSN 2020), Tokyo, Japan, Dec. 2020 (8 pages).

9. Chao Yang, Xuyu Wang, and Shiwen Mao, “Demo Abstract: Vision-aided 3D human pose estimation with RFID,” Demo, in Proc. The 16th IEEE International Conference on Mobility, Sensing and Networking (MSN 2020), Tokyo, Japan, Dec. 2020 (2 pages).

10. Chao Yang, Xuyu Wang, and Shiwen Mao, “RFID-based driving fatigue detection,” in Proc. IEEE GLOBECOM 2019, Waikoloa, HI, Dec. 2019. (The IEEE GLOBECOM 2019 Best Paper Award).

11. Chao Yang, Xuyu Wang, and Shiwen Mao, “SparseTag: High-precision backscatter indoor localization with sparse RFID tag arrays,” in Proc. IEEE SECON 2019, Boston, MA, June 2019.

12. Chao Yang*, Xuyu Wang*, and Shiwen Mao, “AutoTag: Recurrent variational autoencoder for unsupervised apnea detection with RFID tags,” in Proc. IEEE GLOBECOM 2018, Abu Dhabi, United Arab Emirates, Dec. 2018. (*means Co-first authors)

Open Wireless Localization, Sensing and Spectrum Dataset

• 2024.06   The public wireless dataset about “Wireless Localization, Sensing, and Spectrum Dataset ” was collected.

Outreach Activities

• 2024.07   I am hosting an NSF Broadening Participation in Computing (BPC) workshop at FIU from July 1 to July 8. The workshop will involve 13 K-12 teachers and 8 REU students, featuring 7 talks and one wireless spectrum project.

• 2024.06   I am invited to serve as a TPC co-chair of IEEE GLOBECOM 2024 Workshop on Machine Learning and Deep Learning for Wireless Security, Cape Town, South Africa in Dec. 2024. Please consider submitting your papers.

• 2024.06   I am invited to serve as a TPC co-chair of IEEE GLOBECOM 2024 Workshop on Machine Learning and Deep Learning for Wireless Security, Cape Town, South Africa in Dec. 2024. Please consider submitting your papers.

• 2024.06  I co-orgnized a tutorial at CVPR 2024 on June 18 about "Contactless AI Healthcare using Cameras and Wireless Sensors" and gave a talk about "Wireless-based Vital Sign Monitoring and Activity Monitoring".

• 2024.06  I served as a co-chair for the IEEE ICC 2024 Workshop on Machine Learning and Deep Learning for Wireless Security in Denver, Colorado, USA on June 9, 2024.

• 2024.06  Jason Jiang (an undergraduate student from FIU) has an in-person REU internship at FIU AIoT Lab in Summer 2024. He is conducting research on WiFi CSI and BFI sensing, supported by NSF REU Site program.

• 2024.04   I am invited to serve as a symposium co-chair of AI and Machine Learning for Communications and Networking (AMCN) in ICNC 2025. Please consider submitting your papers.

• 2024.02   I am invited to serve as a Tutorials Co-Chair of IEEE CCNC 2024. Please consider submitting your papers and tutorial proposals.

• 2024.02   Yanelli Gloria (an undergraduate student from UCSC) has a remote internship at FIU AIoT Lab in Spring 2024. She is conducting research on the application of Large Language Models (LLM) in IoT sensing, supported by the CASHI Spring 2024 program.

• 2023.12   I am invited to serve as a Workshop Chair of IEEE Healthcom 2024. Please consider submitting your papers.

• 2023.12   I am invited to serve as a judge for Fall 2023 KFSCIS Capstone Showcase on Dec.1, 2023.

• 2023.10   I am selected in the 2023 Stanford's list of World Top 2% scientists.

• 2023.10   I am invited to serve as a co-chair of IEEE INFOCOM 2024 Workshop on Deep Learning for Wireless Communications, Sensing, and Security (DeepWireless), Vancouver, Canada in May 2024. Please consider submitting your papers.

Award Information

This project is supported by the National Science Foundation (NSF) under Grant IIS-2306791 from 10/01/2023 to 09/30/2027. 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. [NSF link with IIS-2306791]