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

虽然不断增长的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。这种动作的例子很多，从日常活动到睡眠中的呼吸和心跳。这些不同规模的运动可以通过细粒度的WiFi信号捕捉到，以执行持续的健康监测。利用现有的WiFi基础设施进行健康监测特别有吸引力，因为它既不需要佩戴身体器械，也不需要用户主动监测。这种方法将促进家庭环境中与医疗保健相关的广泛应用，而无需频繁到医院就诊，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基础设施进行健康监测不仅可以改进和扩展WiFi网络可以支持的应用，而且由于WiFi网络的激增，还可以在非临床环境中轻松和大规模地部署。此外，教育努力包括课程开发，外展到高中生，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的持续健康监测框架，用于细粒度的睡眠监测和家庭环境中的生命体征跟踪。用户不需要佩戴任何传感器，也不需要积极参与监控过程。提出的框架旨在利用现有的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的技术。该框架开发了无需设备的定位策略、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景。通过利用当今不断增长的移动环境，这些健康信息进一步被用来帮助实时疾病预测。为了有效地挖掘健康状况，识别某些疾病的危险因素，建立了一个分层的多变量Logistic回归模型。可以迅速预测出现某些健康问题的可能性，例如心血管疾病。该项目还提供以用户为中心的对存档福利监测信息的访问控制，以确保数据隐私和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.