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dc.contributor.advisorCook, Diane J.
dc.creatorDawadi, Prafulla Nath
dc.date.accessioned2015-11-02T19:10:08Z
dc.date.available2015-11-02T19:10:08Z
dc.date.issued2015
dc.identifier.urihttp://hdl.handle.net/2376/5500
dc.descriptionThesis (Ph.D.), School of Electrical Engineering and Computer Science, Washington State Universityen_US
dc.description.abstractThis dissertation proposes smart home-based intelligent techniques that perform automated assessment of a resident's well-being by monitoring their behavior inside the home. We hypothesize that the everyday behavior of smart home residents can be estimated by tracking residents' activities using smart home sensors and that machine learning algorithms can predict their cognitive and physical health utilizing behavioral information. We first describe a cross-sectional study where we compare behavior differences across an entire population sample to assess activity quality and the individual's cognitive health. For this study, we introduce a machine learning-based framework for assessing the quality of eight different activities and one complex activity with interweaved sub-activities, called the Day Out Task. We compare our automated assessment of task quality with direct observation scores and assess the ability of machine learning techniques to classify an individual's cognitive health using the same machine learning-based framework. We then describe a longitudinal study where we use an individual as their own baseline to identify behavioral changes that can predict cognitive health and mobility. We first introduce a Clinical Assessment using Activity Behavior (CAAB) approach to model a smart home resident's daily behavior and predict the corresponding standard clinical assessment scores utilizing longitudinal smart home sensor data. CAAB extracts statistical features that describe characteristics of a resident's daily activity performance and trains the machine learning algorithms to predict the standard clinical assessment scores. We then introduce an activity curve to represent an abstraction of an individual's normal daily routine based on automatically recognized activities. We develop algorithms to detect changes in behavioral routines by comparing activity curves and use these changes to analyze the possibility of changes in cognitive or physical health. We evaluate all of our algorithms using real-world longitudinal smart home sensor data. We conclude that it is possible to assess the health and well-being of a smart home resident utilizing smart home sensor data and machine learning algorithms.en_US
dc.description.sponsorshipDepartment of Computer Science, Washington State Universityen_US
dc.language.isoEnglish
dc.rightsIn copyright
dc.rightsPublicly accessible
dc.rightsopenAccess
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.rights.urihttp://www.ndltd.org/standards/metadata
dc.rights.urihttp://purl.org/eprint/accessRights/OpenAccess
dc.subjectComputer scienceen_US
dc.subjectFunctional Assessmenten_US
dc.subjectMachine Learningen_US
dc.subjectSmart Enviromentsen_US
dc.titleAUTOMATED FUNCTIONAL ASSESSMENT OF SMART HOME RESIDENTS
dc.typeElectronic Thesis or Dissertation


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