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Robust health stream processing

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dc.contributor.authorEricson, Kathleen, author
dc.contributor.authorPallickara, Shrideep, advisor
dc.contributor.authorMassey, Daniel, committee member
dc.contributor.authorTurk, Daniel, committee member
dc.contributor.authorAnderson, Charles, committee member
dc.date.accessioned2007-01-03T05:57:17Z
dc.date.available2007-01-03T05:57:17Z
dc.date.issued2014
dc.description.abstractAs the cost of personal health sensors decrease along with improvements in battery life and connectivity, it becomes more feasible to allow patients to leave full-time care environments sooner. Such devices could lead to greater independence for the elderly, as well as for others who would normally require full-time care. It would also allow surgery patients to spend less time in the hospital, both pre- and post-operation, as all data could be gathered via remote sensors in the patients home. While sensor technology is rapidly approaching the point where this is a feasible option, we still lack in processing frameworks which would make such a leap not only feasible but safe. This work focuses on developing a framework which is robust to both failures of processing elements as well as interference from other computations processing health sensor data. We work with 3 disparate data streams and accompanying computations: electroencephalogram (EEG) data gathered for a brain-computer interface (BCI) application, electrocardiogram (ECG) data gathered for arrhythmia detection, and thorax data gathered from monitoring patient sleep status.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierEricson_colostate_0053A_12647.pdf
dc.identifier.urihttp://hdl.handle.net/10217/88427
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectinterference detection
dc.subjecthealth stream processing
dc.subjectstream processing
dc.subjectdistributed systems
dc.subjectfault-tolerance
dc.titleRobust health stream processing
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineComputer Science
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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