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dc.contributor.advisorMartell, Craig
dc.contributor.advisorGondree, Mark
dc.contributor.authorParker, William A.
dc.dateMar-14
dc.date.accessioned2014-05-23T15:19:38Z
dc.date.available2014-05-23T15:19:38Z
dc.date.issued2014-03
dc.identifier.urihttp://hdl.handle.net/10945/41429
dc.descriptionApproved for public release; distribution is unlimited.en_US
dc.description.abstractThe growth in smartphone usage has led to increased storage of sensitive data on these easily lost or stolen devices. In order to mitigate the effects of users who ignore, disable, or circumvent authentication measures like passwords, we evaluate a method employing gait as a source of identifying information. This research is based on previously reported methods with a goal of evaluating gait signal processing and classification techniques. This thesis evaluates the performance of four signal normalization techniques (raw signal, zero-scaled, gravity-rotated, and gravity rotated with zero-scaling). Additionally, we evaluate the effect of carrying position on classification. Data was captured from 23 subjects carrying the device in the front pocket, back pocket, and on the hip. Unlike previous research, we analyzed classifier performance on data collected from multiple positions and tested on each individual location, which would be necessary in a robust, deployable system. Our results indicate that restricting device position can achieve the best overall performance using zero-scaling with 6.13% total error rate (TER) on the XY-axis but with a high variance across different axes. Using data from all positions with gravity rotation can achieve 12.6% TER with a low statistical variance.en_US
dc.description.urihttp://archive.org/details/evaluationofdatp1094541429
dc.publisherMonterey, California: Naval Postgraduate Schoolen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. As such, it is in the public domain, and under the provisions of Title 17, United States Code, Section 105, may not be copyrighted.en_US
dc.titleEvaluation of data processing techniques for unobtrusive gait authenticationen_US
dc.typeThesisen_US
dc.contributor.departmentComputer Science
dc.subject.authorSmartphone authenticationen_US
dc.subject.authoraccelerometer gait recognitionen_US
dc.subject.authorsignal processingen_US
dc.subject.authorquaternion rotationen_US
dc.subject.authorgravity calibrationen_US
dc.subject.authormachine learningen_US
dc.subject.authorsupport vector machineen_US
dc.subject.authork-nearest neighborsen_US
dc.description.recognitionOutstanding Thesisen_US
dc.description.serviceLieutenant, United States Navyen_US
etd.thesisdegree.nameMaster Of Science In Computer Scienceen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineComputer Scienceen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US


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