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: 2306792
• 负责人: Harrison Bai
• 金额: $ 29.96万
• 依托单位: Johns Hopkins 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=2306792&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价格昂贵、佩戴不舒服，或者只能在医院环境中进行高度管理。随着物联网 (IoT) 和机器学习 (ML)/人工智能 (AI) 的进步，人们希望能够对这些系统进行管理。开发人工智能驱动的射频传感技术，使智能健康监测更便宜、使用更舒适、更容易为广大人群所接受，同时支持卓越的监测性能。实现这些目标的主要挑战是射频数据的噪声和强干扰。来自动态环境的六人多学科团队。具有互补专业知识的研究人员将密切合作，以改进基于射频传感的智能医疗保健配置的最先进水平，并在充分发挥物联网和机器学习/人工智能的潜力方面迈出重要一步。双方还联合开发了关于深度学习赋能射频健康传感的新研究生课程，并增强了本科生和研究生课程的水平。该项目还将通过提供无线前沿技术的实践经验来吸引学生。传感、深度学习和智能健康。该项目的成果将通过技术出版物、会议主题演讲、杰出讲座和教程、项目网站和开源存储库进行传播。研究人员致力于通过其机构外展计划和 NSF 研究经验扩大代表性不足群体的参与。本科生和教师研究经验项目。该项目开发基于射频识别（RFID）的智能健康监测传感系统，具体来说，将研究几个基本问​​题，并提出新颖的方案。该项目将针对基于 RFID 传感的智能健康应用开发 ML/AI 技术，利用无源 RFID 标签作为可穿戴传感器来监测人类健康状况，以帮助诊断帕金森氏症等疾病和 ML/AI 驱动的方法。将结合张量分解、迁移学习（通过领域适应和元学习）、深度高斯过程和联邦学习来开发针对这些具有挑战性的问题的有效解决方案。研究议程包括四个整合良好的主旨： (i) 传感器的挑战和基本性能限制；(ii) 开发基于 RFID 的呼吸频率、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、 PD 检测系统；以及 (iv) 开发用于处理健康数据的稳健且公平的联合学习模型。该项目的算法将通过在模拟和真实临床环境中进行广泛的实验来实施和验证，重点关注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疾病检测。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。