Wearable Sensor to Monitor COVID-19 Like Signs and Symptoms

Overview

1. Develop a wearable sensor package to gather data on COVID-19-like signs and symptoms such as elevated body temperature, respiratory parameters, heart rate ,cough and gait. 2. Create algorithms to monitor and track changes to COVID19-like signs and symptoms for developing a better care and isolation strategies for COVID-19 pandemic.

Aim 1: Each enrolled participant will be asked to wear the sensor on a daily basis. Duration of the participation varies based on the symptom severity. With the currently available information, recovery times are ranging from 7 days to 56 days. The duration of the study participation can begin at the early detection to all the way until complete recovery or discharge. Participants may be asked to use the sensors anywhere from 7 days to 60 days. Duration of study will be based on the participant's self-reported symptoms or as appropriate determined by the PI. This will allow the research team to collect a comprehensive data set that can characterize both COVID-like and non-COVID-like signs and symptoms. Aim 2: Data collected from Aim#1 will aid in generating machine learning algorithms to characterize the signs and symptoms. Further algorithm development will be carried out to develop signs and symptoms progression and regression models for early warning or warning to prevent return to work of health-care staff or civilians Wearable sensors are compact battery powered miniature electronic devices that are attached to a user's body to record physiological, biochemical and physical activity information. Different types of sensors can be used to monitor these digital biomarkers. Inertial measurement units (IMUs), including accelerometers, gyroscopes, magnetometers are typically used to measure physical activity, movement signatures. Miniature temperature, galvanic skin response (GSR), photoplethysmogram (PPG), oxygen saturation (SPO2) sensors are increasingly embedded in wearable devices for vital sign monitoring. Non-invasive monitoring is very ideal in the current pandemic situation. These sensors can be potentially deployed in large scale to monitor cases of suspected infection and patients recovering from COVID-19. This project is planning to develop a sensor system that is capable of gathering data on COVID-19 like symptoms such as cough, body temperature, respiratory parameters. Machine algorithms will be developed to handle data analysis and derive useful clinical and monitor signs and symptoms in cases of suspected infection and individuals actively recovering from COVID-19 like symptoms

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Outcomes