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
Related Publications (journal & magazine)
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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, under review.
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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)
Related Publications (others)
We acknowledge the generous support from our sponsor
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.