DETECT: Wearable Sensor Data to Predict COVID-19 and Viral Illnesses
ABSTRACT:
The availability of large longitudinal data opens new opportunities exploiting statistical learning and deep convolutional neural networks in healthcare. We will overview our large study with data collected from wireless devices from 200,000 individuals for 2 years. We will observe how sleep changes significantly among this large population and is related to age and body mass index. We will also show how a person’s resting heart rate may be a uniquely individualized measure of their health, with potential value for early detection of important physiologic changes.
This large study provides the baseline for DETECT, our app-based, nationwide clinical study enrolling individuals who routinely use a smartwatch or other wireless devices to determine if individualized tracking of changes in heart rate, activity and sleep can provide early diagnosis and self-monitoring for COVID-19. In this talk, we discuss how this program has been implemented and which insights for the individual and for public health are obtained by analyzing data from more than 36,000 individuals. We show our recent results on the validation of this algorithm, proving that it can identify COVID-19 positive cases by analyzing both self-reported symptoms and wearable sensor data.
Date and Time
Location
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- Date: 15 Dec 2020
- Time: 06:00 PM to 07:30 PM
- All times are (GMT-08:00) America/Vancouver
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- Bellevue, Washington
- United States 98005
- Starts 15 November 2020 12:30 PM
- Ends 15 December 2020 05:59 PM
- All times are (GMT-08:00) America/Vancouver
- No Admission Charge
Speakers
Giorgio Quer, Ph.D. of Scripps Research Translational Institute
DETECT: Wearable Sensor Data to Predict COVID-19 and Viral Illnesses
ABSTRACT:
The availability of large longitudinal data opens new opportunities exploiting statistical learning and deep convolutional neural networks in healthcare. We will overview our large study with data collected from wireless devices from 200,000 individuals for 2 years. We will observe how sleep changes significantly among this large population and is related to age and body mass index. We will also show how a person’s resting heart rate may be a uniquely individualized measure of their health, with potential value for early detection of important physiologic changes.
This large study provides the baseline for DETECT, our app-based, nationwide clinical study enrolling individuals who routinely use a smartwatch or other wireless devices to determine if individualized tracking of changes in heart rate, activity and sleep can provide early diagnosis and self-monitoring for COVID-19. In this talk, we discuss how this program has been implemented and which insights for the individual and for public health are obtained by analyzing data from more than 36,000 individuals. We show our recent results on the validation of this algorithm, proving that it can identify COVID-19 positive cases by analyzing both self-reported symptoms and wearable sensor data.
Biography:
Dr. Giorgio Quer received a Ph.D. degree (2011) in Information Engineering from University of Padova, Italy. In 2007, he was a visiting researcher at the Centre for Wireless Communication at the University of Oulu, Finland. During his Ph.D., he proposed a solution for the distributed compression of wireless sensor networks signals, based on the joint exploitation of Compressive Sensing and Principal Component Analysis. From 2010 to 2016, he was at the Qualcomm Institute, University of California San Diego (UCSD), working on cognitive networks protocols and implementation. At Scripps Research, he is leading the Data Science and Analytics Scripps team involved in the All of Us Research Program (NIH), together with several efforts involving big data and AI in digital medicine, including DETECT, towards the use of wearables to detect COVID-19. He is a Senior Member of the IEEE and a Distinguished Lecturer for the IEEE Communications society. His research interests include wireless sensor networks, probabilistic models, deep convolutional networks, wearable sensors, physiological signal processing, and digital medicine.
Email:
Address:10550 N Torrey Pines Rd, , La Jolla, CA, California, United States, 92037
Agenda
ABSTRACT:
The availability of large longitudinal data opens new opportunities exploiting statistical learning and deep convolutional neural networks in healthcare. We will overview our large study with data collected from wireless devices from 200,000 individuals for 2 years. We will observe how sleep changes significantly among this large population and is related to age and body mass index. We will also show how a person’s resting heart rate may be a uniquely individualized measure of their health, with potential value for early detection of important physiologic changes.
This large study provides the baseline for DETECT, our app-based, nationwide clinical study enrolling individuals who routinely use a smartwatch or other wireless devices to determine if individualized tracking of changes in heart rate, activity and sleep can provide early diagnosis and self-monitoring for COVID-19. In this talk, we discuss how this program has been implemented and which insights for the individual and for public health are obtained by analyzing data from more than 36,000 individuals. We show our recent results on the validation of this algorithm, proving that it can identify COVID-19 positive cases by analyzing both self-reported symptoms and wearable sensor data.