SenSE:Wearable hybrid biochemical and biophysical sensing systems integrated with robust artificial intelligence for monitoring COVID-19 patients

2019年冠状病毒病(新冠肺炎)的爆发已在全球范围内感染了1700多万人，截至2020年7月，已导致66.9万多人死亡。根据现有数据和已发表的报告，大多数被诊断为新冠肺炎的人没有症状或症状轻微，可能会出院进行自我隔离。其中约20%的人将发展为需要住院和医疗管理的严重疾病。目前，医疗服务提供者缺乏有效的方法和技术来远程监控患者在家中的临床情况，评估他们的疾病进展，并预测临床恶化，以便及时进行医疗干预。这个多学科项目旨在通过提供家庭智能监控系统，开辟一条改善新冠肺炎回收成果的新途径。该项目将提供令人兴奋的跨学科教育和研究机会，以及实践经验，以培养我们的研究生，并让本科生，特别是少数族裔学生参与研究。将实施一套整合的研究和教育活动，面向K-12学生和公众，以提高他们对先进科学和工程解决方案的认识，以应对新冠肺炎的关键医疗挑战。最近的研究证实，细胞因子水平与新冠肺炎疾病的严重程度和死亡率有关。细胞因子水平可作为疾病严重程度和进展的有效预测因子。目前，细胞因子检测涉及一种有组织的设置，如采血员采集血液，并使用平板阅读器等实验室设备分析样本。一个主要缺点是无法实现对细胞因子水平的持续监测，因为随着时间的推移，这将需要数十次去医院就诊。这项建议的目标是开发一个可穿戴的多模式传感系统，集成可解释和稳健的人工智能，用于在家中持续监测新冠肺炎患者的生物物理和生化状况，密切跟踪他们的病情进展，并及时进行风险水平预测和医疗干预。本项目包括三个研究目标：(1)开发可穿戴式生化/生物物理传感系统，用于对新冠肺炎患者进行无创、持续的监测；(2)将可穿戴式传感系统与可解释且稳健的人工智能相集成，用于多模式传感器数据分析、个性化疾病进展建模和传感器性能优化；(3)针对新冠肺炎患者，对多模式传感器系统进行表征和评估。这项研究将为开发一种用于长期、连续监测细胞因子的新型传感器提供基本的理解和基本原理。将开发先进的机器学习方法和工具，用于多模式传感器数据分析、风险级别确定和传感器性能优化。该项目开发的生物传感技术、设备设计和机器学习模型也适用于其他领域，包括监测流感或其他疾病患者的传感器，这些领域需要持续监测和及时干预。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The outbreak of Coronavirus Disease 2019 (COVID-19) has infected more than 17 million individuals worldwide, resulting in the death of more than 669, 000 people as of July 2020. Based on the available data and published reports, most people diagnosed with COVID-19 exhibit no or mild symptoms and could be discharged home for self-isolation. About 20% of them will progress to severe disease requiring hospitalization and medical management. Currently, there is a lack of effective methods and technologies for healthcare providers to remotely monitor patients’ clinical conditions at home, evaluate their disease progression, and predict clinical deterioration for timely medical interventions. This multidisciplinary project aims to create a new route to improve the COVID-19 recovery outcome by providing an at-home smart monitoring system. This project will provide exciting interdisciplinary education and research opportunities, as well as hands-on experience, to train our graduate students and to involve undergraduate students, especially minority students, into research. A set of integrated research and education activities will be implemented for out-reaching to K-12 students and the public, to increase their awareness of advanced scientific and engineering solutions for addressing the critical healthcare challenges of COVID-19. Recent studies have established that cytokine level is associated with COVID-19 disease severity and mortality. The level of cytokines can be used as an effective predictor for disease severity and progression. Currently, testing for cytokines involves an organized setup such as blood collection by a phlebotomist and analysis of samples using laboratory equipment such as plate readers. A major drawback is that continuous monitoring of cytokine levels cannot be accomplished since it will require dozens of visits to the hospital over time. The objective of this proposal is to develop a wearable multimodal sensing system integrated with explainable and robust artificial intelligence for continuous monitoring of biophysical and biochemical conditions of COVID-19 patients at home, close tracking of their illness progression, and timely risk level prediction and medical intervention. This project includes three research objectives: (1) develop a wearable biochemical/biophysical sensing system for non-invasive and continuous monitoring of COVID-19 patients, (2) integrate wearable sensing system with explainable and robust artificial intelligence for multimodal sensor data analysis, personalized illness progression modeling, and sensor performance optimization, and (3) characterize and evaluate the multimodal sensor systems with COVID-19 patients. The research will provide fundamental understanding and essential principles for developing a novel sensor for long-term and continuous monitoring of cytokines. Advanced machine learning methods and tools will be developed for multimodal sensor data analysis, risk level determination, and sensor performance optimization. The biosensing technology, device design, and machine learning models developed in this project are applicable to other fields, including sensors to monitor patients with influenza or other diseases, where the continuous monitoring and timely interventions are required.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.