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; 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，其传播模式的多样性，缺乏通用测试能力，缺乏通用测试能力，缺乏医疗用品以及有效治疗所需的医院中的人代表了面对大流行的关键挑战。这个快速的项目解决了Pandemics的关键问题，尤其是COVID-19，即任何医疗保健系统的有限能力 - 医院和医疗保健提供者很难为需要治疗的患者提供有针对性的护理。该项目建议通过发展低成本的敏感性和原位数据分析平台技术，以实现个性化，分布和连续的健康监测，从而提供早期的疾病检测能力，从而在居民中提供早期的疾病检测能力，最小化不必要的医院访问的数量，并以预防性的措施为较高的预防措施，以便在较早的情况下采取较高的预测，以使其成为较高的人，尤其是较高的人，以便较高的人进行过较高的措施，以使其成为较高的人，以便在较高的范围内采取较高的措施。 新冠肺炎。该项目将启用：（1）监测临床环境中疾病的早期迹象，（2）跟踪感染者在家庭和医院中疾病的进展，以允许有效地提供资源，并捕获影响的基本方面，以及（3）准确地衡量了该效率和效率的效率和数量。这些感官处理平台的低成本和分布性质将确保高风险和屈服于Covid-19的人口将能够获得所需的医疗保健。总体而言，这项研究将以普遍的方式快速，准确地诊断和跟踪Covid-19感染 - 建立在目前在芝加哥医疗综合体中部署的独特无线设备平台上，从而极大地限制了当前和未来对社会的影响。 The proposed technology will acquire mechano-acoustic signatures of the underlying physical 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, developing low-complexity data analytic algorithms using a hybrid of digital signal processing (DSP) and machine learning (ML) to以高精度检测感染的存在，并在此类资源约束的计算平台上部署这些算法以进行快速诊断。拟议的工作将扩大使用脉搏氧（SPO2）传感器目前部署在当地医院的补丁。拟议的工作包括：1）使用贴片当前部署获得的患者数据，开发了CoVID-19特定分析的低复杂性固定点ML ML算法； 2）开发将这种算法嵌入到启用SPO2的补丁和相关硬件上的方法； 3）与患者在该领域中基于ML的COVID-19特定数据分析的部署。这项研究汇集了柔性无线电子设备，机械声感应设备，节能推理体系结构以及低复杂性数据分析的创新，以快速，早期和连续的诊断以及包括共同疾病和包括Covid-19的各种疾病和感染的目的。这项研究的垂直整合（材料对系统）的性质克服了传统的纪律界限。在此过程中，将在基本层面和新应用程序方面生成新知识。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响评估标准来评估值得支持。