SCH: INT: Collaborative Research: Non-Contact Smart Monitor for Sleep Disorder Diagnosis and Intervention

睡眠是人类的生理需求。睡眠不足或紊乱会增加白天的疲劳和困倦，降低认知功能。不幸的是，准确的睡眠评估有许多挑战。这些问题包括在不干扰睡眠过程的情况下进行测量的困难，个人独特的基线特征，以及冗长数据集的繁琐手工分析。在实验室环境中，多普勒雷达技术已被证明可以提供一种通过衣服和床上用品测量生命体征的非侵入性手段。我们建议开发一种基于雷达的测量系统，可用于提供自主的家庭睡眠测量和分析。拟议的努力将推进智能雷达技术测量生理学，个性化医疗的大数据分析；以及对睡眠生理学的理解。它还将支持两者交叉领域的潜在技术突破，包括智能互联健康的新诊断技术。该项目通过探索基于雷达技术的生物医学和生物行为睡眠医学研究挑战的解决方案，将基础研究和睡眠研究联系在一起。提出的研究将通过引入新的收发器架构来准确地解决必要的横截面和位移参数，同时隔离两个共同感测对象中的一个，以及新的深度学习模型与物理模型相结合，在个性化水平上识别呼吸暂停事件和睡眠障碍，从而提高对多普勒雷达生理监测的认识和理解。它将通过引入这种非接触式工具来推进睡眠医学，这种工具可以将患者监测扩展到家中，并提供以患者为中心的分析。它还将支持两者交叉领域的潜在技术突破，包括智能互联健康的新诊断技术。这项研究提出了一个激励的教育机会，利用夏威夷人对远程医疗工具的独特需求，并向历史上一直没有得到当地教育和工业机会的少数民族学生伸出援助之手。通过课程（生物医学，传感器和信息学课程），本科研究项目和K-12 STEM外展活动，培训具有医疗保健问题意识的新一代工程师，将研究成果与教育相结合。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Sleep is a biological imperative for humans. Insufficient or disordered sleep increases daytime fatigue and sleepiness, decreases cognitive function. Unfortunately, there are numerous challenges associated with accurate sleep assessment. These include difficulties in making measurements without interfering with the sleep process, unique baseline characteristics for individuals, and cumbersome manual analysis of lengthy data sets. In the laboratory environment, Doppler radar technology has been demonstrated to provide a non-invasive means of measuring vital signs through clothing and bedding. We propose to develop a radar-based measurement system can be used to provide autonomous in-home measurement and analysis of sleep. The proposed effort will advance knowledge in smart radar technology measuring physiology, big data analytics for personalized medicine; and the understanding of sleep physiology. It will also support potential technological breakthroughs at their intersection, including new diagnostic techniques for smart connected health.This project bridges fundamental research and sleep research together by exploring radar technology-based solutions for bio-medical and bio-behavioral sleep medicine research challenges. The proposed research will advance knowledge and understanding of Doppler radar physiological monitoring by introducing new transceiver architectures to accurately resolve requisite cross-section and displacement parameters while isolating one of two jointly sensed subjects, and new deep learning models integrating with physical models for recognition of apnea events and sleep disorders at personalized level. It will advance sleep medicine by introducing this non-contact tool that can extend patient monitoring to the home and provide patient-centered analysis. It will also support potential technological breakthroughs at their intersection, including new diagnostic techniques for smart connected health. The research poses a motivating educational opportunity that leverages the unique needs of Hawaiians for remote healthcare tools, and reaches out to a diverse population of ethnic minority students that have been historically under-served by local educational and industrial opportunities. Research outcomes will be integrated with education through training a new generation of engineers with the awareness of healthcare issues through courses (biomedical, sensors and informatics courses), undergraduate research projects, and K-12 STEM outreach activities.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.