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Activity Recognition using Singular Value Decomposition

A wearable device that accurately records a user's daily activities is of substantial value. It can be used to enhance medical monitoring by maintaining a diary that lists what a person was doing and for how long. The design of a wearable system to record context such as activity recognition is influenced by a combination of variables. A flexible yet systematic approach for building a software classification environment according to a set of variables is described. The integral part of the software design is the use of a unique robust classifier that uses principal component analysis (PCA) through singular value decomposition (SVD) to perform real-time activity recognition. The thesis describes the different facets of the SVD-based approach and how the classifier inputs can be modified to better differentiate between activities. This thesis presents the design and implementation of a classification environment used to perform activity detection for a wearable e-textile system. / Master of Science

Identiferoai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/35219
Date09 November 2006
CreatorsJolly, Vineet Kumar
ContributorsElectrical and Computer Engineering, Plassmann, Paul E., Jones, Mark T., Martin, Thomas L.
PublisherVirginia Tech
Source SetsVirginia Tech Theses and Dissertation
Detected LanguageEnglish
TypeThesis
Formatapplication/pdf, application/pdf
RightsIn Copyright, http://rightsstatements.org/vocab/InC/1.0/
RelationIRB_s05-690_Approval.pdf, JollyThesis.pdf

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