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dc.contributor.authorBales, Dustin Bennetten_US
dc.date.accessioned2017-12-13T07:00:43Z
dc.date.available2017-12-13T07:00:43Z
dc.date.issued2016-06-20en_US
dc.identifier.othervt_gsexam:7921en_US
dc.identifier.urihttp://hdl.handle.net/10919/81183
dc.description.abstractThe ability to classify occupants in a building has far-reaching applications in security, monitoring human health, and managing energy resources effectively. In this work, gender and weight of walkers are classified via machine learning or pattern recognition techniques. Accelerometers mounted beneath the floor of Virginia Tech's Goodwin Hall measured walkers' gait. These acceleration measurements serve as the inputs to machine learning techniques allowing for classification. For this work, the gait of fifteen individual walkers was recorded via fourteen accelerometers as they, alone, walked down the instrumented hallway, in multiple trials. These machine learning algorithms produce an 88 % accurate model for gender classification. The machine learning algorithms included are Bagged Decision Trees, Boosted Decision Trees, Support Vector Machines (SVMs), and Neural Networks. Data reduction techniques achieve a higher gender classification accuracy of 93 % and classify weight with 64% accuracy. The data reduction techniques are Discrete Empirical Interpolation Method (DEIM), Q-DEIM, and Projection Coefficients. A two-part methodology is proposed to implement the approach completed in this thesis work. The first step validates the algorithm design choices, i.e. using bagged or boosted decision trees for classification. The second step reduces the walking data measured to truncate accelerometers which do not aid in increasing characteristic classification.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis Item is protected by copyright and/or related rights. Some uses of this Item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectGoodwin Hallen_US
dc.subjectMachine Learningen_US
dc.subjectGender Classificationen_US
dc.subjectSensor Reductionen_US
dc.titleCharacteristic Classification of Walkers via Underfloor Accelerometer Gait Measurements through Machine Learningen_US
dc.typeThesisen_US
dc.contributor.departmentMechanical Engineeringen_US
dc.description.degreeMaster of Scienceen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineMechanical Engineeringen_US
dc.contributor.committeechairTarazaga, Pablo Albertoen_US
dc.contributor.committeechairKasarda, Mary E.en_US
dc.contributor.committeememberGugercin, Serkanen_US


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