RAPID: Collaborative Research: Data Analytics for Mechano-acoustic and Physiological Monitoring of COVID19 Symptoms

RAPID：协作研究：新冠肺炎症状的机械声学和生理监测数据分析

• 批准号: 2031395
• 负责人: Naresh Shanbhag
• 金额: $ 8.38万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2031395&HistoricalAwards=false
• 关键词: RAPID; Collaborative; Research; Data; Analytics

The novelty of the Covid-19 pathogen, diversity of its transmission modes, lack of universal testing capability, absence of a vaccine, lack of medical supplies and personnel in hospitals needed for effective treatment represent key challenges in confronting the pandemic. This RAPID project addresses a key issue with pandemics in general and Covid-19 in particular – the limited capacity of any health-care system – whereby hospitals and health-care providers struggle to provide targeted care to patients needing treatment. This project proposes to address this challenge by developing low-cost sensing and in-situ data analytics platform technologies to enable individualized, distributed and continuous health monitoring of individuals and thereby provide early disease detection capabilities in-residence, minimize the number of unnecessary hospital visits, and act as an early warning system to enable preventive measures to be taken early on especially for high-risk individuals such as seniors and elderly individuals who are most vulnerable to Covid-19. This project will enable: (1) monitoring of early signs of disease spread across health care workers in clinical settings, (2) tracking of the progression of the disease in infected individuals, both in the home and the hospital to allow for efficient provisioning of resources and also to capture basic aspects of the effects, and (3) accurately and precisely measuring the effectiveness and the timescale of operation of the large number of various therapeutics that are currently under evaluation. The low-cost and distributed nature of these sensory processing platforms will ensure that populations at high-risk of contracting and succumbing to Covid-19 will be able to access the health care needed. Overall, this research will enable rapid and accurate diagnosis and tracking of the Covid-19 infection in a pervasive manner – building on unique wireless device platforms that are currently deployed in the Chicago medical complex -- and thereby contribute significantly to limiting the impact the current and future pandemics on society. The proposed technology will acquire mechano-acoustic signatures of the underlying physiological processes (such as those measured by a stethoscope) and precision kinematics of core-body motions using a skin-mounted soft electronics compute platform (“The Patch”) from individuals tested for Covid-19, develop low-complexity data analytic algorithms using a hybrid of digital signal processing (DSP) and machine learning (ML) to detect the presence of infection with high accuracy, and deploy these algorithms on such resource-constrained compute platforms for rapid diagnosis. Proposed work will augment the Patch, which is currently deployed at the local hospitals, with pulse oximeter (SpO2) sensors. The proposed work includes: 1) development of low-complexity fixed-point ML algorithms for Covid-19 specific analytics using patient data acquired by the current deployment of the Patch; 2) development of methods for energy-efficient embedding of such algorithms on to the SpO2-enabled Patch and associated hardware; 3) and deployment of the ML-based Covid-19 specific data analytics in the field with patients. This research brings together innovations in flexible wireless electronics, mechano-acoustic sensing devices, energy-efficient inference architectures, and low-complexity data analytics for the purposes of rapid, early and continuous diagnosis and monitoring of various diseases and infections including Covid-19. The vertically-integrated (materials-to-systems) nature of this research overcomes traditional disciplinary boundaries. In this process, new knowledge will be generated both at a fundamental level and in terms of new applications.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.

新型冠状病毒病原体的新奇、传播方式的多样性、缺乏通用检测能力、缺乏疫苗、医院缺乏有效治疗所需的医疗用品和人员，这些都是应对这一大流行病的关键挑战。这个快速项目解决了大流行病特别是新冠肺炎的一个关键问题--任何医疗系统的能力都有限--医院和医疗服务提供者难以为需要治疗的患者提供有针对性的护理。该项目建议通过开发低成本传感和原位数据分析平台技术来应对这一挑战，以实现对个人的个性化，分布式和连续健康监测，从而提供早期疾病检测能力，最大限度地减少不必要的医院就诊次数，并作为一个早期预警系统，使人们能够及早采取预防措施，特别是对高危人群，高风险人群，如老年人和最容易感染COVID-19的老年人。该项目将使：（1）在临床环境中监测疾病在卫生保健工作者中传播的早期迹象，（2）在家中和医院中跟踪感染个体中的疾病进展，以允许有效地提供资源并且还捕获效果的基本方面，以及（3）准确和精确地测量当前正在评估的大量各种治疗剂的有效性和操作时间尺度。这些感官处理平台的低成本和分布式特性将确保感染和死于新冠肺炎的高风险人群能够获得所需的医疗保健。总的来说，这项研究将能够以普遍的方式快速准确地诊断和跟踪新冠肺炎感染-建立在目前部署在芝加哥医疗综合体的独特无线设备平台上-从而为限制当前和未来流行病对社会的影响做出重大贡献。该技术将获得潜在生理过程的机械声学特征（例如由听诊器测量的那些）和使用皮肤安装的软电子计算平台的核心身体运动的精确运动学（“补丁”）从个人测试的新冠肺炎，使用数字信号处理（DSP）和机器学习（ML）的混合开发低复杂度的数据分析算法以高精度检测感染的存在，并将这些算法部署在这种资源受限的计算平台上以进行快速诊断。拟议的工作将增加贴片，目前部署在当地医院，脉搏血氧仪（SpO 2）传感器。 拟议的工作包括：1）开发低复杂度定点ML算法，用于使用当前部署的Patch获取的患者数据进行Covid-19特定分析; 2）开发将此类算法节能嵌入SpO 2启用的Patch和相关硬件的方法; 3）并在患者现场部署基于ML的Covid-19特定数据分析。这项研究汇集了灵活的无线电子设备，机械声学传感设备，节能推理架构和低复杂性数据分析的创新，旨在快速，早期和持续诊断和监测包括Covid-19在内的各种疾病和感染。这项研究的垂直整合（材料到系统）性质克服了传统的学科界限。在这个过程中，新的知识将在基础层面和新的应用方面产生。这个奖项反映了NSF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。