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昂贵，穿着不舒服，或者只能在医院环境中进行管理。随着物联网（IoT）和机器学习（ML）/人工智能（AI）的进步，开发AI驱动的射频传感技术是非常可取的，以使智能健康监控更便宜，更舒适，并且更易于使用，并且更容易访问广泛的人群，同时支持出色的监测性能。实现此类目标的主要挑战是噪声RF数据和来自动态环境的强烈干扰。由六名具有完善专业知识的六名调查员组成的多学科团队将密切合作，以显着提高基于射频传感的智能医疗保健供应的最新技术，并迈出了重要的一步，以完全收获IoT和ML/AI的潜力。调查人员团队还将共同开发一门新的研究生课程，以深入学习授权RF健康感知并增强其本科和研究生水平课程。该项目还将通过提供无线感官，深度学习和智能健康方面的尖端技术的实践经验来吸引学生。该项目的结果将通过技术出版物，会议主题，杰出的讲座和教程，项目网站和开源存储库来传播。调查人员致力于通过其机构外展计划以及针对教师计划的本科生和研究经验的NSF研究经验来扩大代表性不足的群体的参与。该项目开发基于智能健康监测的项目开发射频射频识别（RFID）感应系统。具体而言，将研究几个基本问​​题，新型ML/AI技术ML/AI驱动的方法，例如张量分解，转移学习（通过域适应和元学习），深度高斯过程以及联合学习将纳入这些挑战性问题的有效解决方案。研究议程由四个良好整合的推力组成：（i）调查传感器的挑战和基本性能限制； （ii）开发基于RFID的呼吸速率，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； （iv）开发出健壮且公平的联合学习模型来处理健康数据。该项目的算法将在模拟和真实的临床环境中进行广泛的实验来实施和验证，重点是两种重要的智能健康应用，帕金森氏病检测和基于呼吸的室内肺疾病检测发现。这奖反映了NSF的法定任务，并通过使用基础的智力效果和宽阔的评估来进行评估，以评估良好的支持。