IOT Wearable Data Fever Analysis - COVID Detection. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects

TemPredict launched in March 2020 as a collaboration between UCSF, UCSD, and Finnish wearable company Oura. The objective is to identify physiological signals from the wearable and provide early alerts for symptoms and diagnoses for COVID infection. The study's first phase ended on Nov 30th, 2020, with ~65,000 active participants. These participants shared data from their Oura ring from January 2020, answering onboarding, daily and monthly surveys about demographics, symptoms, and diagnoses, and other relevant information. Oura ring collects the person’s physiological data like heart rate, respiratory rate, skin temperature, metabolic equivalent of tasks. This data is stored, managed, and analyzed at SDSC. Our study focuses on developing an architecture that supports the different systematic exploration of approaches and performs comparison between them. We are analyzing the data, extracting new features, and building various algorithms that can be used for the early detection of COVID-19. In order to detect the onset of infection, we define a healthy window for each individual. This healthy window is derived by analyzing the daily rhythm of the physiological signals for every individual. In order to avoid false detection, the model calculates a dynamic baseline for each individual. Higher order features like ratios of temperature and activity, heart rate and its variability, deviations from the baseline, etc., are identified. Various ML models like Random Forest, XGBoost, CWT, Adaboost were trained, tested, and evaluated.