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

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使用。随着物联网（IoT）和机器学习(ML)/人工智能（AI）的发展，人们非常希望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，同时支持出色的监测性能。实现这一目标的主要挑战是噪声射频数据和来自动态环境的强干扰。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，显著提高基于射频传感的智能医疗保健供应的最新水平，并在充分挖掘物联网和机器学习/人工智能潜力方面迈出重要一步。研究小组还将共同开发一门关于深度学习授权射频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。该项目还将通过为学生提供无线传感、深度学习和智能健康等前沿技术的实践经验，吸引学生。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，扩大代表性不足群体的参与。该项目开发基于射频识别（RFID）的传感系统，用于智能健康监测。具体来说，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来监测人类健康状况，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（通过域适应和元学习）、深度高斯过程和联邦学习将被纳入其中，以开发针对这些具有挑战性问题的有效解决方案。研究议程包括四个整合良好的重点：(i)调查传感器的挑战和基本性能限制；（ii）发展以射频识别技术为基础的呼吸频率、肺功能测试和心跳信号监测方案；（iii）开发基于rfid的姿态监测、活动识别和PD检测系统；（iv）开发稳健和公平的联邦学习模型来处理健康数据。该项目的算法将在模拟和真实临床环境中通过大量实验进行实施和验证，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。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.