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

SCH：INT：合作研究：用于睡眠障碍诊断和干预的非接触式智能监测仪

• 批准号: 1915738
• 负责人: Victor Lubecke
• 金额: $ 59.4万
• 依托单位: University of Hawaii
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1915738&HistoricalAwards=false
• 关键词: SCH; INT; Collaborative; Research; Non

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.

睡眠对于人类来说是一种生理需要。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，准确的睡眠评估存在许多挑战。其中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对冗长数据集进行繁琐的手动分析。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方法。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。拟议的工作将推进智能雷达技术测量生理学、个性化医疗大数据分析方面的知识；以及对睡眠生理学的理解。它还将支持其交叉点上的潜在技术突破，包括用于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解决方案来应对生物医学和生物行为睡眠医学研究挑战，将基础研究和睡眠研究联系在一起。拟议的研究将通过引入新的收发器架构来准确解析必要的横截面和位移参数，同时隔离两个联合感知对象之一，以及与物理模型集成的新深度学习模型，以在个性化水平上识别呼吸暂停事件和睡眠障碍，从而增进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，该工具可以将患者监测扩展到家庭并提供以患者为中心的分析。它还将支持其交叉点上潜在的技术突破，包括用于智能互联健康的新诊断技术。该研究提供了一个激励性的教育机会，利用了夏威夷人对远程医疗工具的独特需求，并覆盖了历史上当地教育和工业机会服务不足的多元化少数民族学生群体。研究成果将与教育相结合，通过课程（生物医学、传感器和信息学课程）、本科生研究项目和 K-12 STEM 推广活动来培训具有医疗保健问题意识的新一代工程师。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

An Adaptive Filter Technique for Platform Motion Compensation in Unmanned Aerial Vehicle Based Remote Life Sensing Radar

• DOI: 10.23919/eumc48046.2021.9338011
• 发表时间: 2021-01
• 期刊: 2020 50th European Microwave Conference (EuMC)
• 作者: S. M. Islam;Lana C. Lubecke;Christian Grado;V. Lubecke

Extracting Individual Respiratory Signatures from Combined Multi-Subject Mixtures with Varied Breathing Pattern Using Independent Component Analysis with the JADE Algorithm

使用独立成分分析和 JADE 算法从具有不同呼吸模式的组合多受试者混合物中提取个体呼吸特征

• DOI: 10.1109/apmc47863.2020.9331715
• 发表时间: 2020
• 期刊: 2020 IEEE Asia-Pacific Microwave Conference (APMC
• 作者: Islam, Shekh M.;Lubecke, Victor M.
• 通讯作者: Lubecke, Victor M.

Insider-Resistant Context-Based Pairing for Multimodality Sleep Apnea Test

用于多模态睡眠呼吸暂停测试的内部抗拒上下文配对

• DOI: 10.1109/globecom46510.2021.9685852
• 发表时间: 2021
• 期刊: 2021 IEEE Global Communications Conference (GLOBECOM
• 作者: Zheng, Yao;Islam, Shekh Md;Pan, Yanjun;Millan, Marionne;Aggelopoulos, Samson;Lu, Brian;Yang, Alvin;Yang, Thomas;Aelmore, Stephanie;Chang, Willy
• 通讯作者: Chang, Willy

Sleep Posture Recognition With a Dual-Frequency Microwave Doppler Radar and Machine Learning Classifiers

• DOI: 10.1109/lsens.2022.3148378
• 发表时间: 2022-03-01
• 期刊: IEEE SENSORS LETTERS
• 影响因子: 2.8
• 作者: Islam, Shekh Md Mahmudul;Lubecke, Victor M.
• 通讯作者: Lubecke, Victor M.

Identification of COVID-19 Type Respiratory Disorders Using Channel State Analysis of Wireless Communications Links

使用无线通信链路的信道状态分析识别 COVID-19 类型呼吸系统疾病

• DOI: 10.1109/embc46164.2021.9630016
• 发表时间: 2021
• 期刊: Annu Int Conf IEEE Eng Med Biol Soc
• 作者: Lubecke, Lana C.;Ishmael, Khaldoon;Zheng, Yao;Boric-Lubecke, Olga;Lubecke, Victor M.
• 通讯作者: Lubecke, Victor M.