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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. Research Data Curation Program, UC San Diego, La Jolla, 92093-0175 (https://lib.ucsd.edu/rdcp) Krishan, Rajaraman ; Mahalingam, Sasikumar ; Varadharajan, Swetha Maithreyi ; Mamidala, Venu ; Bansal, Yogesh; Smarr, Benjamin (2021). IOT Wearable Data Fever Analysis - COVID Detection. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects. UC San Diego Library Digital Collections. https://doi.org/10.6075/J0KD1XSQ Due to data restrictions in "Timescale DB of sdsc.edu", no input or output files have been provided, however, if the user has Nautilus environment access then this source will work from there. Please contact Prof. Benjamin Smarr regarding input and output files.
Type
dataset
Identifier
ark:/20775/bb91891486
Language
English
Subject
Infection onset Dashboard Data Science & Engineering Master of Advanced Study (DSE MAS) Physiological signal COVID-19 detection Task: Clustering Task: Feature extraction Capstone projects Internet of things (IoT) DSE MAS - 2021 Cohort
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