Title:
Unsupervised discovery of activity primitives from multivariate sensor data

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dc.contributor.advisor Starner, Thad
dc.contributor.author Minnen, David en_US
dc.contributor.committeeMember Bobick, Aaron
dc.contributor.committeeMember Schiele, Bernt
dc.contributor.committeeMember Isbell, Charles
dc.contributor.committeeMember Essa, Irfan
dc.contributor.department Computing en_US
dc.date.accessioned 2008-09-17T19:25:55Z
dc.date.available 2008-09-17T19:25:55Z
dc.date.issued 2008-07-08 en_US
dc.description.abstract This research addresses the problem of temporal pattern discovery in real-valued, multivariate sensor data. Several algorithms were developed, and subsequent evaluation demonstrates that they can efficiently and accurately discover unknown recurring patterns in time series data taken from many different domains. Different data representations and motif models were investigated in order to design an algorithm with an improved balance between run-time and detection accuracy. The different data representations are used to quickly filter large data sets in order to detect potential patterns that form the basis of a more detailed analysis. The representations include global discretization, which can be efficiently analyzed using a suffix tree, local discretization with a corresponding random projection algorithm for locating similar pairs of subsequences, and a density-based detection method that operates on the original, real-valued data. In addition, a new variation of the multivariate motif discovery problem is proposed in which each pattern may span only a subset of the input features. An algorithm that can efficiently discover such "subdimensional" patterns was developed and evaluated. The discovery algorithms are evaluated by measuring the detection accuracy of discovered patterns relative to a set of expected patterns for each data set. The data sets used for evaluation are drawn from a variety of domains including speech, on-body inertial sensors, music, American Sign Language video, and GPS tracks. en_US
dc.description.degree Ph.D. en_US
dc.identifier.uri http://hdl.handle.net/1853/24623
dc.publisher Georgia Institute of Technology en_US
dc.subject Activity discovery en_US
dc.subject Temporal pattern discovery en_US
dc.subject Data mining en_US
dc.subject Unsupervised learning en_US
dc.subject Sensor analysis en_US
dc.subject.lcsh Detectors
dc.subject.lcsh Algorithms
dc.subject.lcsh Pattern recognition systems
dc.subject.lcsh Time-series analysis
dc.subject.lcsh Probabilities
dc.title Unsupervised discovery of activity primitives from multivariate sensor data en_US
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Starner, Thad
local.contributor.corporatename College of Computing
local.contributor.corporatename School of Interactive Computing
relation.isAdvisorOfPublication cc00e6b1-68e4-4f70-a21e-da9f450fe552
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relation.isOrgUnitOfPublication aac3f010-e629-4d08-8276-81143eeaf5cc
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