Excellence in Research: Privacy preserved data fusion for personalized recommendation in an Edge-assisted IoT smart health network

卓越研究：边缘辅助物联网智能健康网络中隐私保护数据融合的个性化推荐

• 批准号: 2200673
• 负责人: Madhuri Siddula
• 金额: $ 27.16万
• 依托单位: North Carolina Agricultural & Technical State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2200673&HistoricalAwards=false
• 关键词: Excellence; Research; Privacy; preserved; data

Remote patient health monitoring systems have been around for quite a while but only recently did the concept of smart health monitoring systems gain popularity. With the deterioration in people's health and increase in mortality rate, researchers are always looking for ways to provide more support to the patients by improving critical delays in responding to a life-threatening event, incorrect medications, and day-to-day improvements that can be done to elongate patient's health. Given that the numbers of devices connected to the internet by the people are growing rapidly, health insurance companies see this as an opportunity to help people with their care. The rise of wearable and implantable devices in healthcare provides patients and providers with remote diagnostic tools, further reducing the need for expensive care. This project will focus on combining data from various sources, often referred to as data fusion, to provide patients with more accurate prescriptions, treatment, and suggestions to improve their health.The goal of this project is to investigate and develop a framework that collects and merges data from various smart health sensors to predict user behavior and alert concerned authorities for emergencies. This framework allows the server to collect the context of the user health without violating user privacy. Researchers would use not only user's health sensors that are used on a daily basis but also appropriate data from hospitals, labs, and pharmacies to benefit the smart prediction system. The specific objectives are to do the following:1. Design and develop an application to collect sensor data and merge external databases2. Design and develop framework to address missing values in the data3. Design and develop framework to input context information in a privacy enhanced fusion4. Investigate the trade-off between context-aware fusion Vs non-context-aware fusion5. Design and develop framework to address data dependencies for user behavior and generate personalized results.The project also heavily depends on the involvement of undergraduate and graduate students. Project-based learning approach will be followed to integrate research and education for the students. Courses taught in the department, including machine learning and big data, and development of new courses, including mobile application development and Introduction to IoT, will serve as significant contributors towards students learning.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.

远程患者健康监测系统已经存在了很长一段时间，但直到最近智能健康监测系统的概念才得到普及。随着人们健康状况的恶化和死亡率的增加，研究人员一直在寻找为患者提供更多支持的方法，包括改善对危及生命的事件、不正确的药物治疗的关键延迟，以及可以延长患者健康的日常改善。鉴于人们连接到互联网的设备数量正在迅速增长，健康保险公司认为这是一个帮助人们进行护理的机会。医疗保健领域可穿戴和植入式设备的兴起为患者和医疗服务提供者提供了远程诊断工具，进一步减少了对昂贵护理的需求。该项目的重点是将来自不同来源的数据结合起来，通常称为数据融合，为患者提供更准确的处方，治疗和建议，以改善他们的健康。该项目的目标是调查和开发一个框架，收集和合并来自各种智能健康传感器的数据，以预测用户的行为，并在紧急情况下提醒有关当局。该框架允许服务器在不侵犯用户隐私的情况下收集用户健康的上下文。研究人员不仅会使用用户每天使用的健康传感器，还会使用来自医院、实验室和药房的适当数据来使智能预测系统受益。具体目标是做到以下几点：1.设计和开发一个应用程序来收集传感器数据和合并外部数据库2.设计和开发框架，以解决数据中的缺失值3.设计和开发框架，在隐私增强的融合中输入上下文信息4。研究上下文感知融合与非上下文感知融合之间的权衡5。设计和开发框架，以解决用户行为的数据依赖性，并生成个性化的结果。该项目还严重依赖于本科生和研究生的参与。将遵循基于项目的学习方法，将学生的研究和教育结合起来。该部门教授的课程，包括机器学习和大数据，以及新课程的开发，包括移动的应用程序开发和物联网入门，将为学生的学习做出重大贡献。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。