Context-Aware Multi-Inhabitant Functional and Physiological Health Assessment in Smart Home Environment

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dc.contributor.advisorRoy, Nirmalya
dc.contributor.authorAlam, Mohammad Arif UlAlam, Mohammad Arif Ul
dc.contributor.departmentInformation Systems
dc.contributor.programInformation Systems
dc.date.accessioned2019-10-11T13:57:51Z
dc.date.available2019-10-11T13:57:51Z
dc.date.issued2017-01-01
dc.description.abstractRecognizing the human activity, behavior, and physiological symptoms in smart home environments is of utmost importance for the functional, physiological, and cognitive health assessment of the older adults. Unprecedented data from everyday devices such as smart wristbands, smart ornaments, smartphones, and ambient sensors provide opportunities for activity mining and inference, but pose fundamental research challenges in data processing, physiological feature extraction, activity labeling, learning and inference in the presence of multiple inhabitants. In this thesis, we develop micro-activity driven macro-activity recognition approaches while considering the underpinning spatiotemporal constraints and correlations across multiple inhabitants. We postulate an activity recognition framework that helps recognize the unseen activities by exploiting the underlying taxonomical structure. We also design novel signal processing and machine learning algorithms to detect fine-grained physiological symptoms such as stress, depression and agitation. We combine these activity recognition methodologies along with the physiological health assessment approaches to quantify the functional, behavioral, and cognitive health of the older adults. We collected data from a continuing care retirement community center using our smart home sensor setup. Finally, we evaluate, compare, and benchmark our proposed computational approaches with the clinical tools used extensively for functional and cognitive health assessment in practice.
dc.genredissertations
dc.identifierdoi:10.13016/m2qjr0-mocd
dc.identifier.other11771
dc.identifier.urihttp://hdl.handle.net/11603/15592
dc.languageen
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Theses and Dissertations Collection
dc.relation.ispartofUMBC Graduate School Collection
dc.relation.ispartofUMBC Student Collection
dc.rightsThis item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu
dc.sourceOriginal File Name: Alam_umbc_0434D_11771.pdf
dc.subjectActivity recognition
dc.subjectCognitive Computing
dc.subjectContext aware computing
dc.subjectMachine Learning
dc.subjectSignal Processing
dc.subjectSmart Home Environment
dc.titleContext-Aware Multi-Inhabitant Functional and Physiological Health Assessment in Smart Home Environment
dc.typeText
dcterms.accessRightsAccess limited to the UMBC community. Item may possibly be obtained via Interlibrary Loan through a local library, pending author/copyright holder's permission.

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