Machine learning COVID-19 detection from wearables
Bret Nestor, Jaryd Hunter, Raghu Kainkaryam, Erik Drysdale, Jeffrey B. Inglis, Allison Shapiro, Sujay Nagaraj, Marzyeh Ghassemi, Luca Foschini, Anna Goldenberg
Abstract
The increasing accessibility of wearable activity-tracking and health-tracking devices has prompted much research into passive diagnostics and screening that could contribute to infrastructure for population health testing and ultimately mitigate potential pandemics. Elevated resting heart rates have been noted to occur alongside fever.1 This finding has enabled researchers to accurately estimate the prevalence of influenza using data from wearable devices alone.1 In the past 3 years, studies show the potential to make individualised predictions of infection.
Topics & Concepts
Wearable computerScopusWearable technologyPopulationTracking (education)Computer scienceMedicinePandemicInternet privacyCoronavirus disease 2019 (COVID-19)MEDLINEDiseasePsychologyEnvironmental healthPolitical sciencePathologyEmbedded systemInfectious disease (medical specialty)PedagogyLawRespiratory viral infections researchSARS-CoV-2 detection and testingCOVID-19 epidemiological studies