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Active STANDARD GRANT National Science Foundation (US)

Collaborative Research: CCSS: When RFID Meets AI for Occluded Body Skeletal Posture Capture in Smart Healthcare

$1.8M USD

Funder National Science Foundation (US)
Recipient Organization Kennesaw State University Research and Service Foundation
Country United States
Start Date Aug 01, 2023
End Date Jul 31, 2026
Duration 1,095 days
Number of Grantees 1
Roles Principal Investigator
Data Source National Science Foundation (US)
Grant ID 2245607
Grant Description

The human body motion or posture capture plays a critical role in the early detection of risk and treating, monitoring, and diagnosing certain health conditions. This project aims to create and evaluate a low-cost, unobtrusive, and robust posture capture system for healthcare and home environments, especially for non-visible scenarios. With passive Radio-Frequency Identification (RFID) tags attached or fabricated into fitness clothing, wireless signals of RFID can be captured and analyzed to detect body motions.

The posture capture system can be used in a healthcare facility or home environment for monitoring persons in need of preventative care, such as elders at high risk of falling, detecting abnormal body movements, and enhancing training and evaluation processes in rehabilitation settings. Successful completion of this project will significantly improve the state-of-the-art of wearable sensor networks and IoT systems.

The project's education plan includes curriculum development and enhancement, engaging students with hands-on projects, and promoting participation of under-represented groups in research.

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.

This project is jointly funded by the CCSS program in the ECCS division of the Engineering Directorate, and the Established Program to Stimulate Competitive Research (EPSCoR).

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

All Grantees

Kennesaw State University Research and Service Foundation

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