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: 2031495
• 负责人: John Rogers
• 金额: $ 8.38万
• 依托单位: Northwestern University
• 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=2031495&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.

Covid-19 病原体的新颖性、传播模式的多样性、缺乏通用检测能力、缺乏疫苗、缺乏有效治疗所需的医疗用品和医院人员是应对这一流行病的关键挑战。一般流行病，特别是 Covid-19 的一个关键问题是——任何医疗保健系统的能力有限——医院和医疗保健提供者如何努力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来应对这一挑战。低成本传感和现场数据分析平台技术能够对个人进行个性化、分布式和持续的健康监测，从而提供住院早期疾病检测能力，最大限度地减少不必要的就诊次数，并作为早期预警系统，使预防措施得到落实特别是针对最容易感染 Covid-19 的老年人和老年人等高风险人群及早采取该项目将能够：(1) 监测临床环境中医护人员传播的疾病早期迹象，(2)跟踪感染者的疾病进展，在家庭和医院中，以便有效地提供资源并捕获效果的基本方面，以及（3）准确而精确地测量目前正在接受的大量各种治疗方法的有效性和操作时间范围这些感觉处理平台的低成本和分布式特性将确保感染和死于 Covid-19 的高风险人群能够获得所需的医疗保健。总体而言，这项研究将实现快速、准确的诊断。和追踪 Covid-19 感染以一种普遍的方式——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。所提出的技术将获得基础的机械声学特征。使用安装在皮肤上的软电子计算平台（“The Patch”）对个人 Covid-19 进行测试，研究生理过程（例如通过听诊器测量的生理过程）和核心体运动的精确运动学，开发低复杂性数据分析使用数字信号处理（DSP）和机器学习（ML）混合的算法来高精度检测感染的存在，并将这些算法部署在资源有限的计算平台上以进行快速诊断。拟议的工作将增强补丁的功能。目前已在当地医院部署，配有脉搏血氧计 (SpO2) 传感器。拟议的工作包括：1) 使用当前部署的 Patch 获取的患者数据，开发用于 Covid-19 特定分析的低复杂度定点 ML 算法。 ；2)开发将此类算法节能嵌入到支持 SpO2 的贴片和相关硬件上的方法；3) 并在患者现场部署基于 ML 的 Covid-19 特定数据分析。无线电子设备、机械声学传感设备、节能推理架构和低复杂度数据分析，用于快速、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。在此过程中，将在基础层面和新应用方面产生新知识。该奖项反映了 NSF 的法定使命，并被视为是。值得通过使用基金会的智力优点和更广泛的影响审查标准进行评估来支持。