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

虽然激增的WiFi网络通常用于无线互联网连接，但它们在捕捉环境变化和识别各种尺度的人体运动方面具有巨大的潜力。这些动作的例子从日常活动到睡眠时的呼吸和心跳。这些不同尺度的运动可以被细粒度的WiFi信号捕获，以执行连续的健康监测。利用现有WiFi基础设施的健康监测特别有吸引力，因为它既不需要佩戴身体仪器，也不需要用户主动监测。这种方法将有助于在家庭环境中开展广泛的与医疗保健相关的应用，而无需经常去医院，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）。利用现有的WiFi基础设施进行健康监测，不仅可以推进和扩展WiFi网络支持的应用程序，而且由于WiFi网络的扩散，可以在非临床环境中轻松大规模地部署。此外，教育方面的努力还包括课程开发，向高中学生推广，以及让本科生和研究生参与研究。该项目专注于构建一个支持WiFi的连续健康监测框架，用于在家庭环境中进行细粒度的睡眠监测和生命体征跟踪。用户不需要佩戴任何传感器，也不需要主动参与监测过程。该框架旨在利用现有WiFi信号，推进无设备细粒度睡眠事件识别和睡眠期间生命体征跟踪技术。提出的框架开发了无设备定位策略、生命体征跟踪方法和统计学习技术，以描绘用户健康的全面图景。通过利用当今不断增长的移动环境，进一步利用这些健康信息来协助实时疾病预测。建立了分层多元逻辑回归模型，有效地挖掘健康状况并识别某些疾病的危险因素。发展某些健康问题的可能性，如心血管疾病，可以及时预测。该项目还为存档的健康监测信息提供以用户为中心的访问控制，以确保数据隐私并应对不受信任的服务器。

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.