US 12,102,424 B2
Real-time monitoring of COVID-19 progress using magnetic sensing and machine learning
Manh-Huong Phan, Tampa, FL (US); Kee Young Hwang, Wesley Chapel, FL (US); Valery Ortiz Jimenez, Tampa, FL (US); and Baleeswaraiah Muchharla, Tampa, FL (US)
Assigned to University of South Florida, Tampa, FL (US)
Filed by University of South Florida, Tampa, FL (US)
Filed on May 27, 2021, as Appl. No. 17/331,761.
Claims priority of provisional application 62/704,779, filed on May 28, 2020.
Prior Publication US 2021/0369137 A1, Dec. 2, 2021
Int. Cl. A61B 5/08 (2006.01); A61B 5/00 (2006.01); A61B 5/05 (2021.01); G01N 27/74 (2006.01); G01N 33/497 (2006.01); G06N 20/00 (2019.01); G16H 10/60 (2018.01); G16H 40/67 (2018.01); G16H 50/20 (2018.01); G16H 50/50 (2018.01); G16H 50/70 (2018.01); G16H 70/60 (2018.01)
CPC A61B 5/0809 (2013.01) [A61B 5/05 (2013.01); A61B 5/4842 (2013.01); A61B 5/7267 (2013.01); A61B 5/7282 (2013.01); G01N 27/74 (2013.01); G01N 33/497 (2013.01); G06N 20/00 (2019.01); G16H 10/60 (2018.01); G16H 40/67 (2018.01); G16H 50/20 (2018.01); G16H 50/50 (2018.01); G16H 50/70 (2018.01); G16H 70/60 (2018.01); G01N 33/4975 (2024.05)] 10 Claims
OG exemplary drawing
 
1. A system comprising:
a network;
a breathing monitor at a first location associated with a patient, the breathing monitor comprising:
a magnet affixed to a patient using an adhesive by the patient; and
a non-contact sensor that measures impedance caused by a magnet field associated with the magnet, wherein the breathing monitor continuously outputs breathing data indicative of a breathing pattern of the patient based on impedance measurements from the non-contact sensor and provides the breathing data to a diagnosis engine through the network; and
the diagnosis engine, at a second location not associated with the patient, that:
receives the breathing data from the non-contact sensor through the network;
receives medical data associated with the patient;
retrieves a model of a plurality of models associated with a disease based on the medical data associated with the patient, wherein each model of the plurality of models is associated with the disease and a different subset of patients based on the medical data;
diagnoses the patient with the disease using the model and the breathing data; and
in response to diagnosing the patient with the disease, provides an instruction to the patient at the first location through the network, wherein the instruction is for the patient to proceed to a hospital.