Evaluation of clustering techniques for GPS phenotyping using mobile sensor data

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dc.contributor.advisorDawson, Clinton N.
dc.contributor.advisorSchulz, Karl Wayne, 1972-
dc.creatorTschirhart, Zachary Shane
dc.creator.orcid0000-0002-2968-6531
dc.date.accessioned2020-12-18T15:35:41Z
dc.date.available2020-12-18T15:35:41Z
dc.date.created2020-05
dc.date.issued2020-05
dc.date.submittedMay 2020
dc.date.updated2020-12-18T15:35:41Z
dc.description.abstractWith the ubiquitousness of mobile smart phones, health researchers are increasingly interested in leveraging these commonplace devices as data collection instruments for near real-time data to aid in remote monitoring, and to support analysis and detection of patterns associated with a variety of health-related outcomes. As such, this work focuses on the analysis of GPS data collected through an open-source mobile platform over two months in support of a larger study being undertaken to develop a digital phenotype for pregnancy using smart phone data. An exploration of a variety of off-the-shelf clustering methods was completed to assess accuracy and runtime performance for a modest time-series of 292K non-uniform samples on the Stampede2 system at TACC. Motivated by phenotyping needs to not-only assess the physical coordinates of GPS clusters, but also the accumulated time spent at high-interest locations, two additional approaches were implemented to facilitate cluster time accumulation using a pre-processing step that was also crucial in improving clustering accuracy and scalability.
dc.description.departmentComputational Science, Engineering, and Mathematics
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/2152/83975
dc.identifier.urihttp://dx.doi.org/10.26153/tsw/10968
dc.subjectDigital phenotype
dc.subjectGPS
dc.subjectClustering
dc.subjectTime locality
dc.subjectMachine learning
dc.subjectContainerization
dc.titleEvaluation of clustering techniques for GPS phenotyping using mobile sensor data
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentComputational Science, Engineering, and Mathematics
thesis.degree.disciplineComputational Science, Engineering, and Mathematics
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelMasters
thesis.degree.nameMaster of Science in Computational Science, Engineering, and Mathematics

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