Continuous Monitoring of COVID-19 Symptomatology for Elderly Patients in Long Term Care Facilities Using Advanced, Soft, and Flexible Sensors Mounted on the Suprasternal Notch

项目摘要：COVID-19在老年人中的致命性要高得多1， 在长期护理机构（LTC）接受护理的人中风险最大， 从19%到72%不等。监测LTC中的COVID-19感染仍然是一个 特别具有挑战性。现有的和预计将继续存在的充足 分子COVID-19检测加上高达15%的假阴性率， 迫切需要能够监视、预警和跟踪的新的补充技术 该人群中的COVID-19感染。我们的团队是开发 新颖的软电子学。我们最近的出版物得到了我们积极的第一阶段STTR的支持， 发表在《自然生物医学工程》上，详细介绍了下一代超低 外形柔软灵活的传感器（ADAM），可持续测量细微的声学信号， 身体通过嵌入式高频3轴传感器产生机械信号 加速度计与皮肤直接机械通信。ADAM传感器 通过蓝牙与我们的自定义移动的应用程序进行通信，以实现真实的实时流媒体 以及能够独立操作的传感器数据存储。所有数据流 云同步（符合HIPAA）。高度新颖的柔软，灵活的性质允许 ADAM传感器可安装在高信息密度的不寻常位置上。 具体来说，我们利用SN-身体上唯一没有 在胸腔内皮肤水平处的阻尼效果。这使得SN- 安装的ADAM传感器，用于捕获心率（HR）、呼吸率（RR）、温度 身体活动（PA）、吞咽计数和通话时间，以及沿着额外的小说 与COVID-19相关的呼吸道生物标志物。在本建议中，我们建议制定一个 新的COVID-19软件包，机器学习增强我们的咳嗽 算法，并在LTC与老年患者和工作人员进行验证，以评估 可用性、可行性和依从性。与合作伙伴的高水平技术准备 LTC使我们能够有效地部署，为未来的FDA生成基本数据 紧急使用授权。我们的专家团队在工程，皮肤科， 老年学和机器学习是非常有资格开发这个COVID-19 具有广泛的商业和临床价值的监测系统 适用于广泛的其他呼吸和慢性疾病后， 大流行平息。

PROJECT SUMMARY: COVID-19 is significantly more lethal in the elderly1 with the greatest risk in those cared for in long-term care facilities (LTCs) where mortality rates range from 19% to 72% worldwide. Monitoring COVID-19 infections in LTCs remains a particular challenging. The existing and a continued expected shortage of sufficient molecular COVID-19 testing coupled to false negative rates as high as 15% necessitates a critical need for new and complementary technologies that can surveil, alert, and track COVID-19 infections in this population. Our group are pioneers in the development of novel soft electronics. Our recent publication, supported by our active Phase I STTR, was published in Nature Biomedical Engineering detailing a next generation ultra-low profile, soft, and flexible sensor (ADAM) that continuously measures subtle acousto- mechanic signals generated by the body via an embedded high-frequency, 3-axis accelerometer in direct mechanical communication with the skin. The ADAM sensor communicates via Bluetooth with our custom mobile application for real time streaming as well as on sensor data storage enabling stand-alone operation. All data streams are cloud synchronized (HIPAA compliant). The highly novel soft, flexible nature allows for the ADAM sensor to be mountable on unusual locations of high information density. Specifically, we exploit the SN—the only location on the body where there is no dampening effect at the skin level with the intrathoracic cavity. This enables a SN- mounted ADAM sensor to capture heart rate (HR), respiratory rate (RR), temperature, physical activity (PA), swallow count, and talk time, along with additional novel respiratory biomarkers relevant to COVID-19. In this proposal, we propose to develop a new COVID-19 software package, machine learning enhancements to our cough algorithm, and validation in LTCs with both elderly patients and staff to evaluate usability, feasibility, and adherence. The high level of technology readiness with partner LTCs allows us to deploy efficiently to generate essential data for a future FDA Emergency Use Authorization. Our team of experts in engineering, dermatology, gerontology, and machine learning are highly qualified to develop this COVID-19 surveillance system that offers both commercial and clinical value with broad applicability to a wide range of other respiratory and chronic medical conditions after the pandemic subsides.