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

SenSE：可穿戴混合生化和生物物理传感系统，与强大的人工智能集成，用于监测 COVID-19 患者

• 批准号: 2037405
• 负责人: Yi Zhang
• 金额: $ 73.75万
• 依托单位: University of Missouri-Columbia
• 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: 2020-09-15 至 2021-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2037405&HistoricalAwards=false
• 关键词: SenSE; Wearable; hybrid; biochemical; biophysical

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.

截至 2020 年 7 月，2019 年冠状病毒病 (COVID-19) 的爆发已在全球范围内感染了超过 1,700 万人，导致超过 669, 000 人死亡。根据现有数据和已发表的报告，大多数人被诊断患有新冠肺炎-19人没有症状或症状轻微，可以出院自我隔离，其中约20%会发展为重症，需要住院治疗和医疗管理。医疗保健提供者在家中远程监测患者的临床状况，评估其疾病进展并预测临床恶化情况，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设备来创建一条改善 COVID-19 康复结果的新途径。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，以培训我们的研究生并让本科生，特别是少数民族学生参与研究。为向 K-12 学生进行外展而实施和公众，以提高他们对解决 COVID-19 的关键医疗挑战的先进科学和工程解决方案的认识。最近的研究表明，细胞因子水平与 COVID-19 疾病的严重程度和死亡率相关。作为疾病严重程度和进展的有效预测因子，目前，细胞因子检测涉及有组织的设置，例如由抽血者采集血液并使用读板器等实验室设备分析样本，一个主要缺点是无法连续监测细胞因子水平。已完成，因为这需要数十次访问该提案的目标是开发一种集成了可解释且强大的人工智能的可穿戴多模式传感系统，用于持续监测家里的 COVID-19 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治疗。该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 COVID-19 患者进行非侵入性持续监测；(2) 将可穿戴式传感系统与可解释且稳健的人工系统集成。情报多模态传感器数据分析、个性化疾病进展建模和传感器性能优化，以及 (3) 表征和评估适用于 COVID-19 患者的多模态传感器系统。该研究将为长期开发新型传感器提供基本理解和基本原则。该项目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、风险水平确定和传感器性能优化，该技术、设备设计和机器学习模型适用于其他领域。 ，包括监测患者的传感器流感或其他疾病，需要持续监测和及时干预。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。