SHB: Type I (EXP): Collaborative Research: EasySense: Contact-less Physiological Sensing in the Mobile Environment Using Compressive Radio Frequency Probes

该合作研究项目(IIS-1231754，Santosh Kumar，孟菲斯大学；IIS-1231525，Mustafa al‘Absi，明尼苏达双城大学；IIS-1231577，Emre Ertin，俄亥俄州立大学)正在开发和评估一种名为EasySense的移动传感器，它可以使用射频(RF)探头在野外环境中提供连续的无皮肤接触的生理监测。这种方法解决了今天的生理监测问题，这种监测需要皮肤接触，如用于心电的电极，因此多年来无法扩大到对患者和健康成年人的广泛监测。关键的挑战是在低功率移动平台上开发高分辨率传感，能够从身体和传感器的粗大运动中分离出心和肺的微弱运动信号。该项目正在开发一种压缩超宽带(UWB)射频传感器的理论和设计，该传感器将所需的采样率降低两个数量级，从而使其能够在低功率移动外形因数中实现。EasySense使用动态压缩感知算法通过信息的时间整合来提高感知质量，并使用干扰子空间抵消方法来利用从加速度计和陀螺仪获得的数据来消除运动伪影。该项目正在传感器节点上实现所有需要的硬件、固件、用于采样、处理和无线通信的嵌入式软件，以及用于数据收集、存储和可视化的手机软件。EasySense是通过对涉及压力和锻炼方案的人类受试者进行的实验室和现场研究，与传统的生理传感器进行比较的。通过实现野外环境中生理的非接触式传感，EasySense将实现大规模的长期生理监测，这对于确定癌症和心血管疾病等缓慢积累的致命疾病的潜在原因和早期生物标志物至关重要。除了在健康研究和实践中被广泛使用外，EasySense还可以用于健康教育的动手演示。有关该项目的信息、开发的硬件和软件设计文件以及与试验台基础设施有关的代码将通过项目网站(http://www.easysense.org).)以开源形式提供

The collaborative research project (IIS-1231754, Santosh Kumar, University of Memphis; IIS-1231525, Mustafa al'Absi, University of Minnesota Twin Cities; IIS-1231577, Emre Ertin, Ohio State University) is developing and evaluating a mobile sensor called EasySense that can provide continuous physiological monitoring without skin contact in the field environments using radio frequency (RF) probes. This approach addresses the problem of physiological monitoring today that requires skin contacts such as electrodes for ECG, and hence cannot scale to widespread monitoring of patients and healthy adults for years. The key challenge is to develop high-resolution sensing on low-power mobile platforms that can separate out the weak motion signals of heart and lung, from the gross motion of the body and the sensor. The project is developing theory and design for a compressive ultrawideband (UWB) RF sensor that achieves two orders of magnitude reduction in the required sampling rate to make it feasible to realize in a low-power mobile form factor. EasySense employs dynamic compressive sensing algorithms to improve the quality of sensing through temporal integration of information and employs interference subspace cancelation methods to cancel out motion artifacts using data obtained from accelerometers and gyroscopes. The project is implementing all the needed hardware, firmware, embedded software on the sensor node for sampling, processing, and wireless communication, and mobile phone software for data collection, storage, and visualization. EasySense is evaluated against traditional physiological sensors via lab and field studies on human subjects involving stress and exercise protocols. By realizing contactless sensing of physiology in the field environment, EasySense will enable long-term physiological monitoring at large-scale that is essential for determining potential causes and early biomarkers of fatal diseases of slow accumulation such as cancer and cardiovascular diseases. In addition to being used widely in health research and practice, EasySense can be used for hands-on demonstration in health education. Information on the project, developed hardware and software design files and code relating to the testbed infrastructure will be accessible in open source form via the project web site (http://www.easysense.org).