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.

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University of Connecticut
United States
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