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CovidDeep

COVID-19 detection from commercially available smartwatch sensors, published in IEEE Transactions on Consumer Electronics and built on a controlled prospective study of about 2,000 participants.

Published research (historical)

Indication
COVID-19 detection
Data source
Smartwatch sensors and questionnaire data
Collaborators
Princeton University (research origin)
Partnership availability
Historical program; methods available through platform licensing

The problem

Containing a fast-moving infectious disease depends on quickly identifying and isolating cases — including asymptomatic ones — which conventional triage tools such as temperature checks and questionnaires do poorly.

Our approach

CovidDeep, originating in Princeton University research, detected SARS-CoV-2 infection from commercially available wearable medical sensors combined with questionnaire data, with the neural network running inference on a smartphone. The models were developed from data captured in a controlled prospective study of approximately 2,000 participants in Europe.

Current status

Published research (historical). The CovidDeep paper was published in IEEE Transactions on Consumer Electronics, where it became the journal's most popular paper. The program is maintained as a historical demonstration of the platform's ability to move from data collection to on-device disease detection; its reported performance figures are those of the publication. This status does not imply regulatory clearance, approval or diagnostic status in any jurisdiction.

Collaboration need

The methods behind CovidDeep — rapid model development from multimodal wearable data — are available to partners through the StarDeep platform, including for infectious-disease preparedness use cases. Partner with us.

Evidence

Program status reflects the stage stated above; it does not imply regulatory clearance, diagnostic availability, or commercial deployment unless explicitly stated.

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