NeTS: Medium: Collaborative Research: Exploiting Fine-grained WiFi Signals for Wellbeing Monitoring

NeTS：媒介：协作研究：利用细粒度 WiFi 信号进行健康监测

• 批准号: 1514436
• 负责人: Yingying Chen
• 金额: $ 30万
• 依托单位: Stevens Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1514436&HistoricalAwards=false
• 关键词: NeTS; Medium; Collaborative; Research; Exploiting

While proliferating WiFi networks are usually used for wireless Internet connections, they have great potential to capture environment changes and identify human motions of various scales. Examples of such motions range from performing daily activities to breathing and heartbeat during sleep. These various scales of motions can be captured by fine-grained WiFi signals to perform continuous wellbeing monitoring. Wellbeing monitoring leveraging existing WiFi infrastructure is particularly attractive as it requires neither wearing body instrumentation nor active monitorng by the user. Such an approach would facilitate a broad range of healthcare related applications at home environments without frequent hospital visits, such as real-time prediction and prevention of certain health problems (e.g., cardiovascular diseases and sleep apnea). Using existing WiFi infrastructure for wellbeing monitoring not only advances and extends the applications that could be supported by WiFi networks but also enables easy and large-scale deployment in non-clinical settings due to the proliferation of WiFi networks. Additionally, the educational efforts include curriculum development, outreaching to high school students, and engaging both undergraduate and graduate students in research.This project focuses on building a WiFi enabled continuous wellbeing monitoring framework for fine-grained sleep monitoring and vital signs tracking at home environments. Users do not need to wear any sensors or actively participate in the monitoring process. The proposed framework targets to advance techniques in device-free fine-grained sleep events identification and vital signs tracking during sleep by utilizing existing WiFi signals. The proposed framework develops device-free localization strategies, vital signs tracking methods and statistical learning techniques to depict a comprehensive picture of users' wellbeing. Such wellbeing information is further utilized to assist in real-time disease prediction by leveraging today's ever-growing mobile environments. A hierarchical multivariate logistic regression model is developed to effectively mine through health conditions and identify risk factors of certain diseases. Chances of developing certain health problems, such as cardiovascular diseases, is promptly predicted. The project also provides user-centric access control of archived wellbeing monitoring information to ensure data privacy and coping with distrusted servers.

虽然扩散的WiFi网络通常用于无线互联网连接，但它们具有捕获环境变化并确定各种规模的动作的巨大潜力。此类动作的例子包括在睡眠期间进行日常活动到呼吸和心跳。这些各种运动尺度可以通过细粒的WiFi信号捕获，以执行连续的健康监测。利用现有WiFi基础设施的福利监视特别有吸引力，因为它既不需要佩戴身体仪器，也不需要用户主动的Monitorng。这种方法将促进在家庭环境中不经常访问的家庭环境中广泛的与医疗保健相关的应用，例如实时预测和预防某些健康问题（例如心血管疾病和睡眠呼吸暂停）。使用现有的WiFi基础架构进行福祉监控，不仅可以进步，还扩展了WiFi网络可以支持的应用程序，而且由于WiFi网络的扩散，可以在非临床设置中轻松而大规模的部署。此外，教育工作包括课程开发，向高中生进行宣传，并让本科生和研究生参与研究。该项目致力于建立一个启用WiFi的连续健康监测框架，以进行细粒度的睡眠监控和在家庭环境中跟踪生命体征。用户无需佩戴任何传感器或积极参与监视过程。提出的框架目标是通过利用现有的WiFi信号来推进无设备的细粒睡眠事件识别和睡眠过程中的生命体征跟踪的技术。所提出的框架开发了无设备的本地化策略，生命体征跟踪方法和统计学习技术，以描绘用户福祉的全面图片。通过利用当今不断增长的移动环境，这种福祉信息进一步用于协助实时疾病预测。开发了分层多元逻辑回归模型，以通过健康状况有效地挖掘并确定某些疾病的危险因素。迅速预测，出现某些健康问题（例如心血管疾病）的机会。该项目还提供了以用户为中心的访问控制，对存档福利监视信息，以确保数据隐私并使用不信任的服务器应对。