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。这种运动的示例包括从执行日常活动到睡眠期间的呼吸和心跳。这些不同尺度的运动可以通过细粒度的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。这样的方法将促进在家庭环境中的广泛的健康护理相关应用，而无需频繁的医院访问，诸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基础设施进行健康监测不仅可以促进和扩展WiFi网络支持的应用程序，而且由于WiFi网络的激增，还可以在非临床环境中进行简单和大规模的部署。此外，教育工作包括课程开发，外展到高中学生，并聘请本科生和研究生在research.This项目的重点是建立一个WiFi启用的连续健康监测框架，细粒度的睡眠监测和生命体征跟踪在家庭环境中。用户无需佩戴任何传感器，也无需主动参与监测过程。所提出的框架旨在通过利用现有WiFi信号来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的技术。该框架开发了无设备本地化策略，生命体征跟踪方法和统计学习技术，以描绘用户健康的全面画面。通过利用当今不断增长的移动的环境，这种健康信息被进一步用于辅助实时疾病预测。一个分层多变量逻辑回归模型的开发，有效地挖掘通过健康状况，并确定某些疾病的危险因素。发展某些健康问题的机会，如心血管疾病，是及时预测。该项目还提供了以用户为中心的存档健康监测信息的访问控制，以确保数据隐私和应对不可信的服务器。

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.