Project Description

When RFID Meets AI for Occluded Body Skeletal Posture Capture in Smart Healthcare

This project aims to develop RFID localization methods to obtain precise positions of attached RFID tags. The posture and motion of the body can be reconstructed by registering tags to a skeletal model. With the captured posture and motion of the body, an AI-based method shall be proposed to provide diagnostic information. The research agenda includes: (i) Create a prototype for the RFID posture scanner (RFPS); (ii) Enabling RFPS for a complex and large-scale environment; (iii) Developing an intelligent tool for extracting healthcare data. This project also includes a thorough integration and assessment plan to test how the proposed system can be used in a healthcare facility or home environment to monitor persons in need of preventative care.

Aug. 1, 2023 ~ July 31, 2026

Project Team

• Shiwen Mao

• Jian Zhang

• Xiangyu Wang

• Bernard Amoah

• Junwei Ma

• Yongshuai Wu

• Pritom Dutta

Related Publications (journal & magazine)

• X. Wang, J. Zhang, S. Mao, S. C.G. Periaswamy, and J. Patton, “A framework for locating multiple RFID tags using RF hologram tensors,” Elsevier/KeAi Digital Communications and Networks, to appear. DOI: 10.1016/j.dcan.2023.12.004. [link]

• X. Wang, J. Zhang, Z. Yu, S. Mao, S. C.G. Periaswamy, and J. Patton, “On remote temperature sensing using commercial UHF RFID tags,” IEEE Internet of Things Journal, vol.6, no.6, pp. 10715-10727, Dec. 2019.

Related Publications (conference)

• K. K. Podder, J. Zhang, and S. Mao, “Trustworthy hand signal communication between smart IoT agents and humans,” In Proc. IEEE GLOBECOM 2024, Cape Town, South Africa, Dec. 2024, under review.

• J. Ma, X. Wang, J. Zhang, S. Mao, S. Periaswamy, and J. Patton, “Navigating uncertainty: Ambiguity quantification in fingerprinting-based indoor localization,” invited paper, IEEE Annual Congress on Artificial Intelligence of Things (IEEE AIoT), Melbourne, Australia, July 2024.

• K. K. Podder, J. Zhang, and Y. Wu, “IHSR: A framework enables robots to learn novel hand signals from a few samples,” in Proc. 2024 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Boston, MA, July 2024.

• K.K. Podder, J. Zhang, Lingyan Wang, “Universal sign language recognition system using gesture description generation and large language model,” In Proc. The 18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications (WASA 2024), Qingdao, China, June 2024.

• J. Ma, X. Wang, C. Powell, J. Zhang, S. Mao, S. Periaswamy, and J. Patton, “Digital twin of retail stores with RFID tags localization,” invited paper, in Proc. The 9th International Conference on Smart and Sustainable Technologies (SpliTech), Split, Croatia, June 2024.

• Y. Wu, J. Zhang, S. Wu, S. Mao, and Y. Wang, “CMRM: A cross-modal reasoning model to enable zero-shot imitation learning for robotic RFID inventory in unstructured environments,” in Proc. IEEE GLOBECOM 2023, Kuala Lumpur, Malaysia, Dec. 2023, pp.5354-5359. (Best Paper Award of IEEE GLOBECOM 2023)

Related Publications (others)

• Jian Zhang, Shiwen Mao, Senthilkumar C.G. Periaswamy, and Justin Patton, “Standards for passive UHF RFID,” ACM GetMobile, vol.23, no.3, pp.10-15, Sept. 2019. DOI: 10.1145/3379092.3379098

We acknowledge the generous support from our sponsor

• National Science Foundation

This project is supported in part by the National Science Foundation under Grants CCSS-2245608 and CCSS-2245607. 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.