Cardiovascular Physiological Monitoring Based on Video
dc.contributor.advisor | Wu, Min | en_US |
dc.contributor.author | Gebeyehu, Henok | en_US |
dc.contributor.department | Electrical Engineering | en_US |
dc.contributor.publisher | Digital Repository at the University of Maryland | en_US |
dc.contributor.publisher | University of Maryland (College Park, Md.) | en_US |
dc.date.accessioned | 2023-10-13T05:32:31Z | |
dc.date.available | 2023-10-13T05:32:31Z | |
dc.date.issued | 2023 | en_US |
dc.description.abstract | Regular, 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.identifier | https://doi.org/10.13016/dspace/taw5-1ahh | |
dc.identifier.uri | http://hdl.handle.net/1903/30994 | |
dc.language.iso | en | en_US |
dc.subject.pqcontrolled | Artificial intelligence | en_US |
dc.subject.pquncontrolled | Cardiovascular | en_US |
dc.subject.pquncontrolled | Electrocardiogram | en_US |
dc.subject.pquncontrolled | From Video | en_US |
dc.subject.pquncontrolled | Interpretable | en_US |
dc.subject.pquncontrolled | Machine Learning | en_US |
dc.subject.pquncontrolled | remote PPG | en_US |
dc.title | Cardiovascular Physiological Monitoring Based on Video | en_US |
dc.type | Thesis | en_US |
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