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
Device: ADAM Sensor
ADAM sensor The data collected from this sensor contains a wide range of core and novel respiratory digital biomarkers as a home-based early identification system. The core measurements include: heart rate, heart rate variability, temperature, physical activity (including sleep quality) and respiratory rate. The novel respiratory digital biomarkers include: respiratory cadence (expiration / inspiration time), coughing, swallowing, throat clearing, and talk time.
Inclusion Criteria:
- Ages between 18-95 years old
- Currently experiencing any COVID-like signs and symptoms such as fever, cough,
shortness of breath, trouble breathing, persistent pain or pressure in the chest,
confusion or inability to arouse, bluish lips or face.
- Individuals who are not experience any COVID like signs and symptoms (will be asked to
be healthy control)
- Able and willing to give written consent and comply with study procedures.
Exclusion Criteria:
- Inability to understand instructions and follow a three step command.
- The subject is pregnant, nursing or planning a pregnancy.
- Inability to provide written consent.
Shirley Ryan AbilityLab
Chicago, Illinois, United States
Arun Jayaraman, PhD, Principal Investigator
Shirley Ryan AbilityLab