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Real-time monitoring of COVID-19 progress using magnetic sensing and machine learning

Year: 2024

Type: article

Abstract: A breathing monitor that includes a contactless sensor that uses magneto-LC resonance technology is provided. The breathing monitor collects breathing data from a patient that indicates a breathing pattern of the patient. A model trained using machine learning using breathing patterns collected from patients known to have different stages of COVID-19 is used to process the collected breathing data and to determine whether the patient has COVID-19. The model further determines the stage of COVID-19. The breathing monitor can identify likely COVID-19 infections faster than existing tests and requires no contact between the patient and a medical professional, which is an improvement over existing COVID-19 detection technologies.

Source: Digital Commons - University of South Florida (University of South Florida)

Authors: Phan, Manh-Huong, Hwang, Kee Young, Ortiz Jimenez, Valery, Muchharla, Baleeswaraiah

Language: English

Topic: Non-Invasive Vital Sign Monitoring

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W7033520147

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