Cardiovascular Physiological Monitoring Based on Video

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dc.contributor.advisorWu, Minen_US
dc.contributor.authorGebeyehu, Henoken_US
dc.contributor.departmentElectrical Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2023-10-13T05:32:31Z
dc.date.available2023-10-13T05:32:31Z
dc.date.issued2023en_US
dc.description.abstractRegular, continuous monitoring of the heart is advantageous to maintaining one’s cardiovascular health as it enables the early detection of potentially life-threatening cardiovascular diseases. Typically, the required devices for continuous monitoring are found in a clinical setting, but recent research developments have advanced remote physiological monitoring capabilities and expanded the options for continuous monitoring from home. This thesis focuses on further extending the monitoring capabilities of consumer electronic devices to motivate the feasibility of reconstructing Electrocardiograms via a smartphone camera. First, the relationship between skin tone and remote physiological sensing is examined as variations in melanin concentrations for people of diverse skin tones can affect remote physiological sensing. In this work, a study is performed to observe the prospect of reducing the performance disparity caused by melanin differences by exploring the sites from which the physiological signal is collected. Second, the physiological signals obtained from the previous part are enhanced to improve the signal-to-noise ratio and utilized to infer ECG as parts of a novel technique that emphasizes interpretability as a guiding principle. The findings in this work have the potential to enable and promote the remote sensing of a physiological signal that is more informative than what is currently possible with remote sensing.en_US
dc.identifierhttps://doi.org/10.13016/dspace/taw5-1ahh
dc.identifier.urihttp://hdl.handle.net/1903/30994
dc.language.isoenen_US
dc.subject.pqcontrolledArtificial intelligenceen_US
dc.subject.pquncontrolledCardiovascularen_US
dc.subject.pquncontrolledElectrocardiogramen_US
dc.subject.pquncontrolledFrom Videoen_US
dc.subject.pquncontrolledInterpretableen_US
dc.subject.pquncontrolledMachine Learningen_US
dc.subject.pquncontrolledremote PPGen_US
dc.titleCardiovascular Physiological Monitoring Based on Videoen_US
dc.typeThesisen_US

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