Graduation Year

2023

Document Type

Thesis

Degree

M.S.E.E.

Degree Name

MS in Electrical Engineering (M.S.E.E.)

Degree Granting Department

Electrical Engineering

Major Professor

Stephen Saddow, Ph.D.

Committee Member

Mark Jaroszeski, Ph.D.

Committee Member

Sriram Chellappan, Ph.D.

Keywords

Contagious, Kiosk, Mobile, Scanner, Upper Respiratory Tract Infection

Abstract

Fast, accurate, and non-invasive diagnostic techniques are required by the medical industry to increase the success of medical treatments and enhance the quality of patient care. Medical IRT has been demonstrated reasonably effective at diagnosing and monitoring several physiological conditions. Diversities in the human body, physical and psychological condition, measurement equipment, and environment all influence the sensitive readings obtained by passive IR measurement devices. New standards for medical IRT and fever screening have been demonstrated effective, but there is limited adherence to the guidelines [36]. Absolute temperature readings require regular calibration checks and can easily be thrown off by noise. Finally, limited IRT images and data are available for training ML and DL models, and annotating the images manually is time consuming. New methods are required to annotate medical IRT images and evaluate them for diagnostic potential. Multiple vital signs are required for making more accurate assessments of Upper Respiratory Tract Infections. An AR app is created to provide remote measurement of vital signs and detect contagious diseases in real-time.

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