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

合作研究：SCH：面向智能健康应用的人工智能驱动的 RFID 传感

• 批准号: 2306789
• 负责人: Shiwen Mao
• 金额: $ 30万
• 依托单位: Auburn University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2027-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2306789&HistoricalAwards=false
• 关键词: Collaborative; Research; SCH; AI; driven

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.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.

许多现有的健康监测系统是昂贵的、佩戴不舒适的，或者只能在医院环境中管理。随着物联网（IoT）和机器学习（ML）/人工智能（AI）的进步，人们非常希望开发AI驱动的射频传感技术，以使智能健康监测更便宜，使用更舒适，更容易为广大人群所用，同时支持出色的监测性能。实现这些目标的主要挑战是来自动态环境的噪声RF数据和强干扰。一个由六名具有互补专业知识的研究人员组成的多学科团队将密切合作，以显着改善基于射频传感的智能医疗保健供应的最新技术，并在充分挖掘物联网和ML/AI的潜力方面迈出重要一步。研究人员团队还将联合开发一门关于深度学习增强RF健康传感的新研究生课程，并加强他们的本科和研究生课程。该项目还将通过提供无线传感，深度学习和智能健康前沿前沿技术的实践经验来吸引学生。该项目的成果将通过技术出版物、会议主旨发言、著名讲座和教程、项目网站和开放源码储存库传播。研究人员致力于通过他们的机构外展计划和NSF本科生研究经验和教师研究经验计划扩大代表性不足的群体的参与。该项目开发了基于射频识别（RFID）的智能健康监测传感系统。具体而言，将研究几个基本问题，并为基于RFID传感的智能健康应用开发新的ML/AI技术。该项目利用无源RFID标签作为可穿戴传感器，用于监测人体健康状况，以帮助诊断帕金森病和间质性肺病等疾病。ML/AI驱动的方法，如张量分解，迁移学习（通过域自适应和元学习），深度高斯过程和联邦学习将被纳入，以开发这些具有挑战性的问题的有效解决方案。研究议程包括四个综合性的重点：（i）调查传感器的挑战和基本性能限制;（ii）开发基于RFID的呼吸率，肺功能测试和心跳信号监测方案;（iii）开发基于RFID的姿势监测，活动识别和PD检测系统;以及（iv）开发用于处理健康数据的强大而公平的联邦学习模型。该项目的算法将在模拟和真实的临床环境中通过广泛的实验进行实施和验证，重点是两个重要的智能健康应用，帕金森病检测和基于呼吸的间质性肺病检测。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。