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

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Context-Aware Multi-Inhabitant Functional and Physiological Health Assessment in Smart Home Environment./
Author:	Alam, Mohammad Arif Ul.
Description:	1 online resource (295 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-04(E), Section: A.
Contained By:	Dissertation Abstracts International79-04A(E).
Subject:	Information science. -
Online resource:	click for full text (PQDT)
ISBN:	9780355544220

Context-Aware Multi-Inhabitant Functional and Physiological Health Assessment in Smart Home Environment.
Alam, Mohammad Arif Ul.

Context-Aware Multi-Inhabitant Functional and Physiological Health Assessment in Smart Home Environment. - 1 online resource (295 pages)

Source: Dissertation Abstracts International, Volume: 79-04(E), Section: A.

Thesis (Ph.D.)

Includes bibliographical references

Recognizing 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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355544220Subjects--Topical Terms:

561178
Information science.
Index Terms--Genre/Form:

554714
Electronic books.

Context-Aware Multi-Inhabitant Functional and Physiological Health Assessment in Smart Home Environment.
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245 1 0 $a Context-Aware Multi-Inhabitant Functional and Physiological Health Assessment in Smart Home Environment.
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300 $a 1 online resource (295 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 79-04(E), Section: A.
500 $a Adviser: Nirmalya Roy.
502 $a Thesis (Ph.D.) $c University of Maryland, Baltimore County $d 2017.
504 $a Includes bibliographical references
520 $a Recognizing 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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Information science. $3 561178
655 7 $a Electronic books. $2 local $3 554714
690 $a 0723
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710 2 $a University of Maryland, Baltimore County. $b Information Systems. $3 1179394
773 0 $t Dissertation Abstracts International $g 79-04A(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10683091 $z click for full text (PQDT)