research-article

Modeling patterns of activities using activity curves

Authors:

Prafulla N. Dawadi,

Maureen Schmitter-EdgecombeAuthors Info & Claims

Pervasive and Mobile Computing, Volume 28, Issue C

Pages 51 - 68

Published: 01 June 2016 Publication History

Abstract

Pervasive computing offers an unprecedented opportunity to unobtrusively monitor behavior and use the large amount of collected data to perform analysis of activity-based behavioral patterns. In this paper, we introduce the notion of an activity curve, which represents an abstraction of an individual's normal daily routine based on automatically-recognized activities. We propose methods 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 demonstrate our model and evaluate our change detection approach using a longitudinal smart home sensor dataset collected from 18 smart homes with older adult residents. Finally, we demonstrate how big data-based pervasive analytics such as activity curve-based change detection can be used to perform functional health assessment. Our evaluation indicates that correlations do exist between behavior and health changes and that these changes can be automatically detected using smart homes, machine learning, and big data-based pervasive analytics.

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Cited By

Zhang YLiang WYuan XZhang SYang GZeng Z(2023)Deep Learning-Based Abnormal Behavior Detection for Elderly Healthcare Using Consumer Network CamerasIEEE Transactions on Consumer Electronics10.1109/TCE.2023.330985270:1(2414-2422)Online publication date: 29-Aug-2023
https://dl.acm.org/doi/10.1109/TCE.2023.3309852
Cook DStrickland MSchmitter-Edgecombe M(2022)Detecting Smartwatch-Based Behavior Change in Response to a Multi-Domain Brain Health InterventionACM Transactions on Computing for Healthcare10.1145/35080203:3(1-18)Online publication date: 7-Apr-2022
https://dl.acm.org/doi/10.1145/3508020
Soto-Mendoza VGarca-Macas JChvez EGomez-Montalvo JQuintana E(2017)Detecting abnormal behaviours of institutionalized older adults through a hybrid-inference approachPervasive and Mobile Computing10.1016/j.pmcj.2017.06.01940:C(708-723)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1016/j.pmcj.2017.06.019
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Published In

cover image Pervasive and Mobile Computing

Pervasive and Mobile Computing Volume 28, Issue C

June 2016

149 pages

ISSN:1574-1192

Issue’s Table of Contents

Copyright © Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 June 2016

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Cited By

Zhang YLiang WYuan XZhang SYang GZeng Z(2023)Deep Learning-Based Abnormal Behavior Detection for Elderly Healthcare Using Consumer Network CamerasIEEE Transactions on Consumer Electronics10.1109/TCE.2023.330985270:1(2414-2422)Online publication date: 29-Aug-2023
https://dl.acm.org/doi/10.1109/TCE.2023.3309852
Cook DStrickland MSchmitter-Edgecombe M(2022)Detecting Smartwatch-Based Behavior Change in Response to a Multi-Domain Brain Health InterventionACM Transactions on Computing for Healthcare10.1145/35080203:3(1-18)Online publication date: 7-Apr-2022
https://dl.acm.org/doi/10.1145/3508020
Soto-Mendoza VGarca-Macas JChvez EGomez-Montalvo JQuintana E(2017)Detecting abnormal behaviours of institutionalized older adults through a hybrid-inference approachPervasive and Mobile Computing10.1016/j.pmcj.2017.06.01940:C(708-723)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1016/j.pmcj.2017.06.019
Ye JFang LDobson SLukowicz PKrüger ABulling ALim YPatel S(2016)Discovery and recognition of unknown activitiesProceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct10.1145/2968219.2968288(783-792)Online publication date: 12-Sep-2016
https://dl.acm.org/doi/10.1145/2968219.2968288
Sprint GCook DSchmitter-Edgecombe M(2016)Unsupervised detection and analysis of changes in everyday physical activity dataJournal of Biomedical Informatics10.1016/j.jbi.2016.07.02063:C(54-65)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1016/j.jbi.2016.07.020

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